Small Business Programs: Benefits, Barriers, Bridges and Critical Success Factors

DTIC Science & Technology

2009-05-01

Decreased product development time cycles • Product, process and technology innovation • Joint marketing and advertising • Access to new markets or...Increased Joint Marketing and Advertising Yuva (2005) Increased Penetration into New Markets Yuva (2005); Terrill (2007) Improved Forecasting and Response

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

A Preliminary Study of Grade Forecasting by Students

ERIC Educational Resources Information Center

Armstrong, Michael J.

2013-01-01

This experiment enabled undergraduate business students to better assess their progress in a course by quantitatively forecasting their own end-of-course grades. This innovation provided them with predictive feedback in addition to the outcome feedback they were already receiving. A total of 144 students forecast their grades using an…

[Innovation guidelines and strategies for pharmaceutical engineering of Chinese medicine and their industrial translation].

PubMed

Cheng, Yi-Yu; Qu, Hai-Bin; Zhang, Bo-Li

2013-01-01

This paper briefly analyzes the bottlenecks and major technical requirements for pharmaceutical industry of Chinese medicine, providing current status of pharmaceutical engineering of Chinese medicine. The innovation directions and strategies of the pharmaceutical engineering for manufacturing Chinese medicine are proposed along with the framework of their core technology. As a consequence, the development of the third-generation pharmaceutical technology for Chinese medicine, featured as "precision, digital and intelligent", is recommended. The prospects of the pharmaceutical technology are also forecasted.

Workshop for transportation forecasters

DOT National Transportation Integrated Search

2009-01-01

On September 22-23, 2009, the U.S. Department of Transportation, Research and Innovative Technology Administration's - Bureau of Transportation Statistics is hosted an international workshop on transportation forecasting in Washington, D.C. Topics fo...

[Prospects of systemic radioecology in solving innovative tasks of nuclear power engineering].

PubMed

Spiridonov, S I

2014-01-01

A need of systemic radioecological studies in the strategy developed by the atomic industry in Russia in the XXI century has been justified. The priorities in the radioecology of nuclear power engineering of natural safety associated with the development of the radiation-migration equivalence concept, comparative evaluation of innovative nuclear technologies and forecasting methods of various emergencies have been identified. Also described is an algorithm for the integrated solution of these tasks that includes elaboration of methodological approaches, methods and software allowing dose burdens to humans and biota to be estimated. The rationale of using radioecological risks for the analysis of uncertainties in the environmental contamination impacts,at different stages of the existing and innovative nuclear fuel cycles is shown.

A Sub-seasonal to Seasonal Western Forecasting Rodeo: Time to Giddy-up!

NASA Astrophysics Data System (ADS)

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

2016-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, exhibits an intrinsic need for 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 a recognized need 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 year-long, real-time forecasting prize competition where solvers will submit S2S forecasts of temperature and precipitation every two weeks. Prize competitions enable federal and other agencies to spur innovation on mission relevant topics by reaching a broad and diverse community of thinkers. Solvers will compete in real-time against each other and with current operational and experimental forecast products as well as climatology and persistence. The competition domain will focus on the 17 western states where Reclamation operates. Forecasts will be evaluated once observational data become available and performance/skill will be posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). Prize categories include performance overall, regionally, and for any extreme events that may occur during the course of the competition. Potential Reclamation prizes total over $500,000. Although some researchers or entities may not be eligible for monetary prizes, the competition is open to anyone who wishes to participate and jockey for a spot atop the leader board. This is a unique opportunity to solicit innovation and for novel forecast approaches, experimental products, and established models to all compete for both prestige and monetary incentives. The competition is expected to raise awareness on the S2S forecast need and the potential benefits- which extend beyond water management - to drought preparedness, public health, and others, while also yielding actionable advances for the state of the science in S2S prediction.

Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning

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

Mills, Andrew D.; Barbose, Galen L.; Seel, Joachim

The rapid growth of distributed solar photovoltaics (DPV) has critical implications for U.S. utility planning processes. This report informs utility planning through a comparative analysis of roughly 30 recent utility integrated resource plans or other generation planning studies, transmission planning studies, and distribution system plans. It reveals a spectrum of approaches to incorporating DPV across nine key planning areas, and it identifies areas where even the best current practices might be enhanced. (1) Forecasting DPV deployment: Because it explicitly captures several predictive factors, customer-adoption modeling is the most comprehensive forecasting approach. It could be combined with other forecasting methods tomore » generate a range of potential futures. (2) Ensuring robustness of decisions to uncertain DPV quantities: using a capacity-expansion model to develop least-cost plans for various scenarios accounts for changes in net load and the generation portfolio; an innovative variation of this approach combines multiple per-scenario plans with trigger events, which indicate when conditions have changed sufficiently from the expected to trigger modifications in resource-acquisition strategy. (3) Characterizing DPV as a resource option: Today's most comprehensive plans account for all of DPV's monetary costs and benefits. An enhanced approach would address non-monetary and societal impacts as well. (4) Incorporating the non-dispatchability of DPV into planning: Rather than having a distinct innovative practice, innovation in this area is represented by evolving methods for capturing this important aspect of DPV. (5) Accounting for DPV's location-specific factors: The innovative propensity-to-adopt method employs several factors to predict future DPV locations. Another emerging utility innovation is locating DPV strategically to enhance its benefits. (6) Estimating DPV's impact on transmission and distribution investments: Innovative practices are being implemented to evaluate system needs, hosting capacities, and system investments needed to accommodate DPV deployment. (7) Estimating avoided losses associated with DPV: A time-differentiated marginal loss rate provides the most comprehensive estimate of avoided losses due to DPV, but no studies appear to use it. (8) Considering changes in DPV's value with higher solar penetration: Innovative methods for addressing the value changes at high solar penetrations are lacking among the studies we evaluate. (9) Integrating DPV in planning across generation, transmission, and distribution: A few states and regions have started to develop more comprehensive processes that link planning forums, but there are still many issues to address.« less

Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning

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

Mill, Andrew; Barbose, Galen; Seel, Joachim

The rapid growth of distributed solar photovoltaics (DPV) has critical implications for U.S. utility planning processes. This report informs utility planning through a comparative analysis of roughly 30 recent utility integrated resource plans or other generation planning studies, transmission planning studies, and distribution system plans. It reveals a spectrum of approaches to incorporating DPV across nine key planning areas, and it identifies areas where even the best current practices might be enhanced. 1) Forecasting DPV deployment: Because it explicitly captures several predictive factors, customer-adoption modeling is the most comprehensive forecasting approach. It could be combined with other forecasting methods tomore » generate a range of potential futures. 2) Ensuring robustness of decisions to uncertain DPV quantities: using a capacity-expansion model to develop least-cost plans for various scenarios accounts for changes in net load and the generation portfolio; an innovative variation of this approach combines multiple per-scenario plans with trigger events, which indicate when conditions have changed sufficiently from the expected to trigger modifications in resource-acquisition strategy. 3) Characterizing DPV as a resource option: Today’s most comprehensive plans account for all of DPV’s monetary costs and benefits. An enhanced approach would address non-monetary and societal impacts as well. 4) Incorporating the non-dispatchability of DPV into planning: Rather than having a distinct innovative practice, innovation in this area is represented by evolving methods for capturing this important aspect of DPV. 5) Accounting for DPV’s location-specific factors: The innovative propensity-to-adopt method employs several factors to predict future DPV locations. Another emerging utility innovation is locating DPV strategically to enhance its benefits. 6) Estimating DPV’s impact on transmission and distribution investments: Innovative practices are being implemented to evaluate system needs, hosting capacities, and system investments needed to accommodate DPV deployment. 7) Estimating avoided losses associated with DPV: A time-differentiated marginal loss rate provides the most comprehensive estimate of avoided losses due to DPV, but no studies appear to use it. 8) Considering changes in DPV’s value with higher solar penetration: Innovative methods for addressing the value changes at high solar penetrations are lacking among the studies we evaluate. 9) Integrating DPV in planning across generation, transmission, and distribution: A few states and regions have started to develop more comprehensive processes that link planning forums, but there are still many issues to address.« less

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.

e-Business Innovation: The Next Decade

NASA Astrophysics Data System (ADS)

Marca, David A.

Innovation is invention or application of technologies or theories that radically alters business and the economy. For many years, innovation and the economy have been locked in 80-year cycles, which might imply that innovation is an economic driver, and vice versa. Based on this, some forecast that innovation and the economy might decrease sharply due to several forces: a) decreasing economic growth, b) increasing demand for custom services, c) more entrepreneurial work environments, and d) urban and environmental degradation. Should such forecasts hold true, business may need to alter its offerings, operations and organization to survive. Such a scenario may also require applied e-Business innovation by combining existing internet, wireless, broadband, and video technologies. One possible result: flexible front offices integrated with efficient back offices. Such an e-Business could comprise: a) a customer-based and transaction-based organization, b) functions for adaptive offerings that anticipate need, c) highly responsive, real-time, operations having no inventory, and d) value-based front-end, and automated back-end, decision making.

Community Coordinated Modeling Center: Paving the Way for Progress in Space Science Research to Operational Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Maddox, M. M.; Mays, M. L.; Mullinix, R.; MacNeice, P. J.; Pulkkinen, A. A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.; Wiegand, C.

2013-12-01

Community Coordinated Modeling Center (CCMC) was established at the dawn of the millennium as an essential element on the National Space Weather Program. One of the CCMC goals was to pave the way for progress in space science research to operational space weather forecasting. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment, in developing and maintaining powerful web-based tools and systems ready to be used by space weather service providers and decision makers as well as in space weather prediction capabilities assessments. The presentation will showcase latest innovative solutions for space weather research, analysis, forecasting and validation and review on-going community-wide initiatives enabled by CCMC applications.

The Westernization of Arab Pedagogies: Abu Dhabi Attempts to Move towards a Knowledge Economy

ERIC Educational Resources Information Center

Chrystall, Steve

2014-01-01

As the oil reserves in the United Arab Emirates (UAE) are forecast to become depleted over the next 50 to 150 years, the emirate of Abu Dhabi has set a vision to develop a knowledge economy in order to develop alternative sources of revenue in areas such as tourism, alternative energy and innovative business enterprises. Reformation of its…

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.

PubMed

Biggerstaff, Matthew; Alper, David; Dredze, Mark; Fox, Spencer; Fung, Isaac Chun-Hai; Hickmann, Kyle S; Lewis, Bryan; Rosenfeld, Roni; Shaman, Jeffrey; Tsou, Ming-Hsiang; Velardi, Paola; Vespignani, Alessandro; Finelli, Lyn

2016-07-22

Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.

Bridging the Gap Between NASA Earth Observations and Decision Makers Through the NASA Develop National Program

NASA Astrophysics Data System (ADS)

Remillard, C. M.; Madden, M.; Favors, J.; Childs-Gleason, L.; Ross, K. W.; Rogers, L.; Ruiz, M. L.

2016-06-01

The NASA DEVELOP National Program bridges the gap between NASA Earth Science and society by building capacity in both participants and partner organizations that collaborate to conduct projects. These rapid feasibility projects highlight the capabilities of satellite and aerial Earth observations. Immersion of decision and policy makers in these feasibility projects increases awareness of the capabilities of Earth observations and contributes to the tools and resources available to support enhanced decision making. This paper will present the DEVELOP model, best practices, and two case studies, the Colombia Ecological Forecasting project and the Miami-Dade County Ecological Forecasting project, that showcase the successful adoption of tools and methods for decision making. Through over 90 projects each year, DEVELOP is always striving for the innovative, practical, and beneficial use of NASA Earth science data.

The Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

2016-05-01

The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

WIPCast: Probabilistic Forecasting for Aviation Decision Aid Applications

DTIC Science & Technology

2011-06-01

traders, or families planning an outing – manage weather-related risk. By quantifying risk , probabilistic forecasting enables optimization of actions via...confidence interval to the user’s risk tolerance helps drive highly effective and innovative decision support mechanisms for visually quantifying risk for

Forecasting volatility with neural regression: a contribution to model adequacy.

PubMed

Refenes, A N; Holt, W T

2001-01-01

Neural nets' usefulness for forecasting is limited by problems of overfitting and the lack of rigorous procedures for model identification, selection and adequacy testing. This paper describes a methodology for neural model misspecification testing. We introduce a generalization of the Durbin-Watson statistic for neural regression and discuss the general issues of misspecification testing using residual analysis. We derive a generalized influence matrix for neural estimators which enables us to evaluate the distribution of the statistic. We deploy Monte Carlo simulation to compare the power of the test for neural and linear regressors. While residual testing is not a sufficient condition for model adequacy, it is nevertheless a necessary condition to demonstrate that the model is a good approximation to the data generating process, particularly as neural-network estimation procedures are susceptible to partial convergence. The work is also an important step toward developing rigorous procedures for neural model identification, selection and adequacy testing which have started to appear in the literature. We demonstrate its applicability in the nontrivial problem of forecasting implied volatility innovations using high-frequency stock index options. Each step of the model building process is validated using statistical tests to verify variable significance and model adequacy with the results confirming the presence of nonlinear relationships in implied volatility innovations.

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Kuznetsova, M.; Maddox, M.; Pulkkinen, A.; Hesse, M.; Rastaetter, L.; Macneice, P.; Taktakishvili, A.; Berrios, D.; Chulaki, A.; Zheng, Y.;

Testing an innovative framework for flood forecasting, monitoring and mapping in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Between May and June 2016, France was hit by severe floods, particularly in the Loire and Seine river basins. In this work, we use this case study to test an innovative framework for flood forecasting, mapping and monitoring. More in detail, the system integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. We explore in detail the performance of each component of the system, demonstrating the improvements in respect to stand-alone flood forecasting and monitoring systems. We show how the performances of the forecasting component can be refined using the real-time feedback from social media monitoring to identify which areas were flooded, to evaluate the flood intensity, and therefore to correct impact estimations. Moreover, we show how the integration with impact forecast and social media monitoring can improve the timeliness and efficiency of satellite based emergency mapping, and reduce the chances of missing areas where flooding is already happening. These results illustrate how the new integrated approach leads to a better and earlier decision making and a timely evaluation of impacts.

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.

School Physics Education in Southeast Asia.

ERIC Educational Resources Information Center

Seng, Chin Pin; Tee, Tan Boon

1978-01-01

Traces physics curriculum innovation in Southeast Asia since the 1950s. The unique features of such innovation in Indonesia, Malaysia, Philippines, Singapore, and Thailand are highlighted. Forecasts for the future of physics education in part of the world are also discussed. (Author/HM)

Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

NASA Astrophysics Data System (ADS)

Ramírez-Cuesta, J. M.; Cruz-Blanco, M.; Santos, C.; Lorite, I. J.

2017-03-01

Reference evapotranspiration (ETo) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ETo assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed. Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ETo assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ETo assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas. The proposed approaches not only enabled accurate calculations of seasonal and daily ETo values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ETo, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

Development of Ensemble Model Based Water Demand Forecasting Model

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

2014-05-01

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

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.

WSN system design by using an innovative neural network model to perform thermals forecasting in a urban canyon scenario

NASA Astrophysics Data System (ADS)

Giuseppina, Nicolosi; Salvatore, Tirrito

2015-12-01

Wireless Sensor Networks (WSNs) were studied by researchers in order to manage Heating, Ventilating and Air-Conditioning (HVAC) indoor systems. WSN can be useful specially to regulate indoor confort in a urban canyon scenario, where the thermal parameters vary rapidly, influenced by outdoor climate changing. This paper shows an innovative neural network approach, by using WSN data collected, in order to forecast the indoor temperature to varying the outdoor conditions based on climate parameters and boundary conditions typically of urban canyon. In this work more attention will be done to influence of traffic jam and number of vehicles in queue.

Operational Earthquake Forecasting and Earthquake Early Warning: The Challenges of Introducing Scientific Innovations for Public Safety

NASA Astrophysics Data System (ADS)

Goltz, J. D.

2016-12-01

Although variants of both earthquake early warning and short-term operational earthquake forecasting systems have been implemented or are now being implemented in some regions and nations, they have been slow to gain acceptance within the disciplines that produced them as well as among those for whom they were intended to assist. To accelerate the development and implementation of these technologies will require the cooperation and collaboration of multiple disciplines, some inside and others outside of academia. Seismologists, social scientists, emergency managers, elected officials and key opinion leaders from the media and public must be the participants in this process. Representatives of these groups come from both inside and outside of academia and represent very different organizational cultures, backgrounds and expectations for these systems, sometimes leading to serious disagreements and impediments to further development and implementation. This presentation will focus on examples of the emergence of earthquake early warning and operational earthquake forecasting systems in California, Japan and other regions and document the challenges confronted in the ongoing effort to improve seismic safety.

Acceleration, Transport, Forecasting and Impact of solar energetic particles in the framework of the 'HESPERIA' HORIZON 2020 project

NASA Astrophysics Data System (ADS)

Malandraki, Olga; Klein, Karl-Ludwig; Vainio, Rami; Agueda, Neus; Nunez, Marlon; Heber, Bernd; Buetikofer, Rolf; Sarlanis, Christos; Crosby, Norma

2017-04-01

High-energy solar energetic particles (SEPs) emitted from the Sun are a major space weather hazard motivating the development of predictive capabilities. In this work, the current state of knowledge on the origin and forecasting of SEP events will be reviewed. Subsequently, we will present the EU HORIZON2020 HESPERIA (High Energy Solar Particle Events foRecastIng and Analysis) project, its structure, its main scientific objectives and forecasting operational tools, as well as the added value to SEP research both from the observational as well as the SEP modelling perspective. The project addresses through multi-frequency observations and simulations the chain of processes from particle acceleration in the corona, particle transport in the magnetically complex corona and interplanetary space to the detection near 1 AU. Furthermore, publicly available software to invert neutron monitor observations of relativistic SEPs to physical parameters that can be compared with space-borne measurements at lower energies is provided for the first time by HESPERIA. In order to achieve these goals, HESPERIA is exploiting already available large datasets stored in databases such as the neutron monitor database (NMDB) and SEPServer that were developed under EU FP7 projects from 2008 to 2013. Forecasting results of the two novel SEP operational forecasting tools published via the consortium server of 'HESPERIA' will be presented, as well as some scientific key results on the acceleration, transport and impact on Earth of high-energy particles. Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637324.

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.

Development of the physics driver in NOAA Environmental Modeling System (NEMS)

NASA Astrophysics Data System (ADS)

Lei, H.; Iredell, M.; Tripp, P.

2016-12-01

As a key component of the Next Generation Global Prediction System (NGGPS), a physics driver is developed in the NOAA Environmental Modeling System (NEMS) in order to facilitate the research, development, and transition to operations of innovations in atmospheric physical parameterizations. The physics driver connects the atmospheric dynamic core, the Common Community Physics Package and the other NEMS-based forecast components (land, ocean, sea ice, wave, and space weather). In current global forecasting system, the physics driver has incorporated major existing physics packages including radiation, surface physics, cloud and microphysics, ozone, and stochastic physics. The physics driver is also applicable to external physics packages. The structure adjustment in NEMS by separating the PHYS trunk is to create an open physics package pool. This open platform is beneficial to the enhancement of U.S. weather forecast ability. In addition, with the universal physics driver, the NEMS can also be used for specific functions by connecting external target physics packages through physics driver. The test of its function is to connect a physics dust-radiation model in the system. Then the modified system can be used for dust storm prediction and forecast. The physics driver is also developed into a standalone form. This is to facilitate the development works on physics packages. The developers can save instant fields of meteorology data and snapshots from the running system , and then used them as offline driving data fields to test the new individual physics modules or small modifications to current modules. This prevents the run of whole system for every test.

Verification of short lead time forecast models: applied to Kp and Dst forecasting

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus

2016-04-01

In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.

[Recent Development of Atomic Spectrometry in China].

PubMed

Xiao, Yuan-fang; Wang, Xiao-hua; Hang, Wei

2015-09-01

As an important part of modern analytical techniques, atomic spectrometry occupies a decisive status in the whole analytical field. The development of atomic spectrometry also reflects the continuous reform and innovation of analytical techniques. In the past fifteen years, atomic spectrometry has experienced rapid development and been applied widely in many fields in China. This review has witnessed its development and remarkable achievements. It contains several directions of atomic spectrometry, including atomic emission spectrometry (AES), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), X-ray fluorescence spectrometry (XRF), and atomic mass spectrometry (AMS). Emphasis is put on the innovation of the detection methods and their applications in related fields, including environmental samples, biological samples, food and beverage, and geological materials, etc. There is also a brief introduction to the hyphenated techniques utilized in atomic spectrometry. Finally, the prospects of atomic spectrometry in China have been forecasted.

Reinforced two-step-ahead weight adjustment technique for online training of recurrent neural networks.

PubMed

Chang, Li-Chiu; Chen, Pin-An; Chang, Fi-John

2012-08-01

A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.

Development of personal pollen information—the next generation of pollen information and a step forward for hay fever sufferers

NASA Astrophysics Data System (ADS)

Kmenta, Maximilian; Bastl, Katharina; Jäger, Siegfried; Berger, Uwe

2014-10-01

Pollen allergies affect a large part of the European population and are considered likely to increase. User feedback indicates that there are difficulties in providing proper information and valid forecasts using traditional methods of aerobiology due to a variety of factors. Allergen content, pollen loads, and pollen allergy symptoms vary per region and year. The first steps in challenging such issues have already been undertaken. A personalized pollen-related symptom forecast is thought to be a possible answer. However, attempts made thus far have not led to an improvement in daily forecasting procedures. This study describes a model that was launched in 2013 in Austria to provide the first available personal pollen information. This system includes innovative forecast models using bi-hourly pollen data, traditional pollen forecasts based on historical data, meteorological data, and recent symptom data from the patient's hayfever diary. Furthermore, it calculates the personal symptom load in real time, in particular, the entries of the previous 5 days, to classify users. The personal pollen information was made available in Austria on the Austrian pollen information website and via a mobile pollen application, described herein for the first time. It is supposed that the inclusion of personal symptoms will lead to major improvements in pollen information concerning hay fever sufferers.

UCERF3: A new earthquake forecast for California's complex fault system

USGS Publications Warehouse

Field, Edward H.; ,

2015-01-01

With innovations, fresh data, and lessons learned from recent earthquakes, scientists have developed a new earthquake forecast model for California, a region under constant threat from potentially damaging events. The new model, referred to as the third Uniform California Earthquake Rupture Forecast, or "UCERF" (http://www.WGCEP.org/UCERF3), provides authoritative estimates of the magnitude, location, and likelihood of earthquake fault rupture throughout the state. Overall the results confirm previous findings, but with some significant changes because of model improvements. For example, compared to the previous forecast (Uniform California Earthquake Rupture Forecast 2), the likelihood of moderate-sized earthquakes (magnitude 6.5 to 7.5) is lower, whereas that of larger events is higher. This is because of the inclusion of multifault ruptures, where earthquakes are no longer confined to separate, individual faults, but can occasionally rupture multiple faults simultaneously. The public-safety implications of this and other model improvements depend on several factors, including site location and type of structure (for example, family dwelling compared to a long-span bridge). Building codes, earthquake insurance products, emergency plans, and other risk-mitigation efforts will be updated accordingly. This model also serves as a reminder that damaging earthquakes are inevitable for California. Fortunately, there are many simple steps residents can take to protect lives and property.

Test operation of a real-time tsunami inundation forecast system using actual data observed by S-net

NASA Astrophysics Data System (ADS)

Suzuki, W.; Yamamoto, N.; Miyoshi, T.; Aoi, S.

2017-12-01

If the tsunami inundation information can be rapidly and stably forecast before the large tsunami attacks, the information would have effectively people realize the impeding danger and necessity of evacuation. Toward that goal, we have developed a prototype system to perform the real-time tsunami inundation forecast for Chiba prefecture, eastern Japan, using off-shore ocean bottom pressure data observed by the seafloor observation network for earthquakes and tsunamis along the Japan Trench (S-net) (Aoi et al., 2015, AGU). Because tsunami inundation simulation requires a large computation cost, we employ a database approach searching the pre-calculated tsunami scenarios that reasonably explain the observed S-net pressure data based on the multi-index method (Yamamoto et al., 2016, EPS). The scenario search is regularly repeated, not triggered by the occurrence of the tsunami event, and the forecast information is generated from the selected scenarios that meet the criterion. Test operation of the prototype system using the actual observation data started in April, 2017 and the performance and behavior of the system during non-tsunami event periods have been examined. It is found that the treatment of the noises affecting the observed data is the main issue to be solved toward the improvement of the system. Even if the observed pressure data are filtered to extract the tsunami signals, the noises in ordinary times or unusually large noises like high ocean waves due to storm affect the comparison between the observed and scenario data. Due to the noises, the tsunami scenarios are selected and the tsunami is forecast although any tsunami event does not actually occur. In most cases, the selected scenarios due to the noises have the fault models in the region along the Kurile or Izu-Bonin Trenches, far from the S-net region, or the fault models below the land. Based on the parallel operation of the forecast system with a different scenario search condition and examination of the fault models, we improve the stability and performance of the forecast system.This work was supported by Council for Science, Technology and Innovation(CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), "Enhancement of societal resiliency against natural disasters"(Funding agency: JST).

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Improving global flood risk awareness through collaborative research: Id-Lab

NASA Astrophysics Data System (ADS)

Weerts, A.; Zijderveld, A.; Cumiskey, L.; Buckman, L.; Verlaan, M.; Baart, F.

2015-12-01

Scientific and end-user collaboration on operational flood risk modelling and forecasting requires an environment where scientists and end-users can physically work together and demonstrate, enhance and learn about new tools, methods and models for forecasting and warning purposes. Therefore, Deltares has built a real-time demonstration, training and research infrastructure ('operational' room and ICT backend). This research infrastructure supports various functions like (1) Real time response and disaster management, (2) Training, (3) Collaborative Research, (4) Demonstration. The research infrastructure will be used for a mixture of these functions on a regular basis by Deltares and a multitude of both scientists as well as end users such as universities, research institutes, consultants, governments and aid agencies. This infrastructure facilitates emergency advice and support during international and national disasters caused by rainfall, tropical cyclones or tsunamis. It hosts research flood and storm surge forecasting systems for global/continental/regional scale. It facilitates training for emergency & disaster management (along with hosting forecasting system user trainings in for instance the forecasting platform Delft-FEWS) both internally and externally. The facility is expected to inspire and initiate creative innovations by bringing together different experts from various organizations. The room hosts interactive modelling developments, participatory workshops and stakeholder meetings. State of the art tools, models and software, being applied across the globe are available and on display within the facility. We will present the Id-Lab in detail and we will put particular focus on the global operational forecasting systems GLOFFIS (Global Flood Forecasting Information System) and GLOSSIS (Global Storm Surge Information System).

An innovative forecasting and dashboard system for Malaysian dengue trends

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2016-08-01

Dengue fever has been recognized in over 100 countries and 2.5 billion people live in areas where dengue is endemic. It is currently a serious arthropod-borne disease, affecting around 50-100 million people worldwide every year. Dengue fever is also prevalent in Malaysia with numerous cases including mortality recorded over the past year. In 2012, a total of 21,900 cases of dengue fever were reported with 35 deaths. Dengue, a mosquito-transmitted virus, causes a high fever accompanied by significant pain in afflicted patient and the Aedes Aegypti mosquito is the primary disease carrier. Knowing the dangerous effect of dengue fever, thus one of the solutions is to implement an innovative forecasting and dashboard system of dengue spread in Malaysia, with emphasize on an early prediction of dengue outbreak. Specifically, the model developed will provide with a valuable insight into strategically managing and controlling the future dengue epidemic. Importantly, this research will deliver the message to health policy makers such as The Ministry of Health Malaysia (MOH), practitioners, and researchers of the importance to integrate their collaboration in exploring the potential strategies in order to reduce the future burden of the increase in dengue transmission cases in Malaysia.

A patent survey case: how could technological forecasting help cosmetic chemists with product innovation?

PubMed

Domicio Da Silva Souza, Ivan; Juliana Pinheiro, Bárbara; Passarini Takahashi, Vania

2012-01-01

Patents represent a free and open source of data for studying innovation and forecasting technological trends. Thus, we suggest that new discussions about the role of patent information are needed. To illustrate the relevance of this issue, we performed a survey of patents involving skin care products, which were granted by the United States Patent and Trademark Office (USPTO) between 2006 and 2010, to identify opportunities for innovation and technological trends. We quantified the use of technologies in 333 patents. We plotted a life cycle of technologies related to natural ingredients. We also determined the cross impact of the technologies identified. We observed technologies related to processes applied to cosmetics (2.2%), functional packaging and applicators (2.9%), excipients and active compounds (21.5%), and cosmetic preparations (73.5%). Further, 21.6% of the patents were related to the use of natural ingredients. Several opportunities for innovation were discussed throughout this paper, for example, the use of peptides as active compounds or intracellular carriers (only 3.9% of the technologies in cosmetic preparations). We also observed technological cross impacts that suggested a trend toward multifunctional cosmetics, among others. Patent surveys may help researchers with product innovation because they allow us to identify available and unexplored technologies and turn them into whole new concepts.

Innovative Ideas in Introductory Economics. Volume 2. A Report Developed from the 1979-80 College and University Level Entries in the International Paper Company Foundation Awards Program for the Teaching of Economics.

ERIC Educational Resources Information Center

Nappi, Andrew T., Ed.; Suglia, Anthony F., Ed.

Award winning projects in K-12 and college level economics are described in this publication. There are two major sections. Section I describes winning projects for 1979-80. A senior research seminar in economics offered undergraduate students a chance to build inexpensive, simplified forecasting models of the U.S. economy. Each student develops…

[Evaluation of possibility of using new financial instruments for supporting biomedical projects].

PubMed

Starodubov, V I; Kurakova, N G; Eremchenko, O A; Tsvetkova, L A; Zinov, V G

2014-01-01

Analysis of selection criteria on projects of Russian medical research centers for funding in Russian scientific fund and Federal program "Research and innovations" was done. It was noted that a high degree of uncertainty of such concepts as "priority direction", "applied" and "search" research and "industrial partner" in regards to research of biomedical theme. Analysis of classified "Medicine and health care" "Forecast of scientific-technological development of Russian Federation till 2030 year" were completed.

European Gravity Service for Improved Emergency Management - Status and project highlights

NASA Astrophysics Data System (ADS)

Mayer-Guerr, Torsten; Adrian, Jäggi; Meyer, Ulrich; Jean, Yoomin; Susnik, Andreja; Weigelt, Matthias; van Dam, Tonie; Flechtner, Frank; Gruber, Christian; Güntner, Andreas; Gouweleeuw, Ben; Kvas, Andreas; Klinger, Beate; Flury, Jakob; Bruinsma, Sean; Lemoine, Jean-Michel; Zwenzner, Hendrik; Bourgogne, Stephane; Bandikova, Tamara

2016-04-01

The European Gravity Service for Improved Emergency Management (EGSIEM) is a project of the Horizon 2020 Framework Programme for Research and Innovation of the European Commission. EGSIEM shall demonstrate that observations of the redistribution of water and ice mass derived from the current GRACE mission, the future GRACE-FO mission, and additional data provide critical and complementary information to more traditional Earth Observation products and open the door for innovative approaches to flood and drought monitoring and forecasting. In the frame of EGSIEM three key services should established: 1) a scientific combination service to deliver the best gravity products for applications in Earth and environmental science research based on the unified knowledge of the European GRACE community, 2) a near real-time and regional service to reduce the latency and increase the temporal resolution of the mass redistribution products, and 3) a hydrological and early warning service to develop gravity-based indicators for extreme hydrological events and to demonstrate their value for flood and drought forecasting and monitoring services. All of these services shall be tailored to the various needs of the respective communities. Significant efforts shall also be devoted to transform the service products into user-friendly and easy-to-interpret data sets and the development of visualization tools. In this talk the status of the ongoing project is presented and selected results are discussed.

Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

2013-04-01

Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a world map, with detailed reports for individual gauging sites. A comparison of discharge estimates from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS) with observations for representative climatic zones is presented. Both systems have demonstrated strong potential in forecasting and detecting recent catastrophic floods. The usefulness of their combined information on global scale for decision makers at different levels is discussed. Combining space-based monitoring and global forecasting models is an innovative approach and has significant benefits for international river commissions as well as international aid organisations. This is in line with the objectives of the Hyogo and the Post-2015 Framework that aim at the development of systems which involve trans-boundary collaboration, space-based earth observation, flood forecasting and early warning.

Development of the AOML Hurricane Research System

NASA Astrophysics Data System (ADS)

Yeh, K.; Gopalakrishnan, S.; Zhang, X.; Bao, J.; Quirino, T.; Sainani, V.; Rogers, R.; Aberson, S.; Marks, F.; Atlas, R.

2008-12-01

NOAA's Atlantic Oceanographic and Meteorological Laboratory (AOML) has committed to the development of a modeling and data-assimilation system recently. This Hurricane Research System (HRS) aims to improve hurricane forecast by developing innovative modeling techniques, and by assimilating the hurricane inner-core data that is timely collected with aircrafts by the scientists at the AOML Hurricane Research Division (HRD), in addition to the data collected by other channels. We have started the development of the HRS by implementing a moving nest within a regional domain on the Weather Research and Forecasting (WRF) Nonhydrostatic Mesoscale Model (NMM). The dynamically moving nest is used to track the hurricane with an enhanced resolution to better simulate the hurricane structure with more accurate dynamical and physical processes. Combining with the diagnostic expertise at the HRD, and benefiting from the community efforts, we have quickly composed the HRS with excellent ingredients from various organizations. This baseline system has been in experimental operation for this hurricane season, and early result with these experiments seems quite promising. We have also developed a new visualization tool and an efficient post-processor emphasizing diagnostic functionality to facilitate hurricane research. Further development of the HRS includes the implementation of a third, moving nest to advance the model resolution to 1 km or higher with the limited computing resource. Innovative model initialization techniques and versatile hurricane-diagnostic tools are undergoing development. An Ensemble Kalman Filter is being constructed for the HRS to assimilate observation data. Physical parameterizations are being refined to improve the forcing and heating mechanisms, and ocean model coupling is to be implemented for realistic air-sea interactions. We will report the status up to date.

Case studies on forecasting for innovative technologies: frequent revisions improve accuracy.

PubMed

Lerner, Jeffrey C; Robertson, Diane C; Goldstein, Sara M

2015-02-01

Health technology forecasting is designed to provide reliable predictions about costs, utilization, diffusion, and other market realities before the technologies enter routine clinical use. In this article we address three questions central to forecasting's usefulness: Are early forecasts sufficiently accurate to help providers acquire the most promising technology and payers to set effective coverage policies? What variables contribute to inaccurate forecasts? How can forecasters manage the variables to improve accuracy? We analyzed forecasts published between 2007 and 2010 by the ECRI Institute on four technologies: single-room proton beam radiation therapy for various cancers; digital breast tomosynthesis imaging technology for breast cancer screening; transcatheter aortic valve replacement for serious heart valve disease; and minimally invasive robot-assisted surgery for various cancers. We then examined revised ECRI forecasts published in 2013 (digital breast tomosynthesis) and 2014 (the other three topics) to identify inaccuracies in the earlier forecasts and explore why they occurred. We found that five of twenty early predictions were inaccurate when compared with the updated forecasts. The inaccuracies pertained to two technologies that had more time-sensitive variables to consider. The case studies suggest that frequent revision of forecasts could improve accuracy, especially for complex technologies whose eventual use is governed by multiple interactive factors. Project HOPE—The People-to-People Health Foundation, Inc.

NASA's aircraft icing technology program

NASA Technical Reports Server (NTRS)

Reinmann, John J.

1991-01-01

NASA' Aircraft Icing Technology program is aimed at developing innovative technologies for safe and efficient flight into forecasted icing. The program addresses the needs of all aircraft classes and supports both commercial and military applications. The program is guided by three key strategic objectives: (1) numerically simulate an aircraft's response to an in-flight icing encounter, (2) provide improved experimental icing simulation facilities and testing techniques, and (3) offer innovative approaches to ice protection. Our research focuses on topics that directly support stated industry needs, and we work closely with industry to assure a rapid and smooth transfer of technology. This paper presents selected results that illustrate progress towards the three strategic objectives, and it provides a comprehensive list of references on the NASA icing program.

Design and Prototype Implementation of non-Triggered Database-driven Real-time Tsunami Forecast System using Multi-index Method

NASA Astrophysics Data System (ADS)

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

2016-12-01

We have launched a new project to develop real-time tsunami inundation forecast system for the Pacific coast of Chiba prefecture (Kujukuri-Sotobo region), Japan (Aoi et al., 2015, AGU). In this study, we design a database-driven real-time tsunami forecast system using the multi-index method (Yamamoto et al., 2016, EPS) and implement a prototype system. In the previous study (Yamamoto et al., 2015, AGU), we assumed that the origin-time of tsunami was known before a forecast based on comparing observed and calculated ocean-bottom pressure waveforms stored in the Tsunami Scenario Bank (TSB). As shown in the figure, we assume the scenario origin-times by defining the scenario elapsed timeτp to compare observed and calculated waveforms. In this design, when several appropriate tsunami scenarios were selected by multiple indices (two variance reductions and correlation coefficient), the system could make tsunami forecast using the selected tsunami scenarios for the target coastal region without any triggered information derived from observed seismic and/or tsunami data. In addition, we define the time range Tq shown in the figure for masking perturbations contaminated by ocean-acoustic and seismic waves on the observed pressure records (Saito, 2015, JpGU). Following the proposed design, we implement a prototype system of real-time tsunami inundation forecast system for the exclusive use of the target coastal region using ocean-bottom pressure data from the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) (Kanazawa et al., 2012, JpGU; Uehira et al., 2015, IUGG), which is constructed by National Research institute for Earth Science and Disaster Resilience (NIED). For the prototype system, we construct a prototype TSB using interplate earthquake fault models located along the Japan Trench (Mw 7.6-9.8), the Sagami Trough (Mw 7.6-8.6), and the Nankai Trough (Mw 7.6-8.6) as well as intraplate earthquake fault models (Mw 7.6-8.6) within the subducting Pacific plate, which could affect the target coastal region. This work was partially supported by the Council for Science, Technology and Innovation (CSTI) through the Cross-ministerial Strategic Innovation Promotion Program (SIP), titled "Enhancement of societal resiliency against natural disasters" (Funding agency: JST).

The option value of innovative treatments in the context of chronic myeloid leukemia.

PubMed

Sanchez, Yuri; Penrod, John R; Qiu, Xiaoli Lily; Romley, John; Thornton Snider, Julia; Philipson, Tomas

2012-11-01

To quantify in the context of chronic myeloid leukemia (CML) the additional value patients receive when innovative treatments enable them to survive until the advent of even more effective future treatments (ie, the "option value"). Observational study using data from the Surveillance, Epidemiology and End Results (SEER) cancer registry comprising all US patients with CML diagnosed between 2000 and 2008 (N = 9,760). We quantified the option value of recent breakthroughs in CML treatment by first conducting retrospective survival analyses on SEER data to assess the effectiveness of TKI treatments, and then forecasting survival from CML and other causes to measure expected future medical progress. We then developed an analytical framework to calculate option value of innovative CML therapies, and used an economic model to value these gains. We calculated the option value created both by future innovations in CML treatment and by medical progress in reducing background mortality. For a recently diagnosed CML patient, the option value of innovative therapies from future medical innovation amounts to 0.76 life-years. This option value is worth $63,000, equivalent to 9% of the average survival gains from existing treatments. Future innovations in CML treatment jointly account for 96% of this benefit. The option value of innovative treatments has significance in the context of CML and, more broadly, in disease areas with rapid innovation. Incorporating option value into traditional valuations of medical innovations is both a feasible and a necessary practice in health technology assessment.

The multi-level perspective analysis: Indonesia geothermal energy transition study

NASA Astrophysics Data System (ADS)

Wisaksono, A.; Murphy, J.; Sharp, J. H.; Younger, P. L.

2018-01-01

The study adopts a multi-level perspective in technology transition to analyse how the transition process in the development of geothermal energy in Indonesia is able to compete against the incumbent fossil-fuelled energy sources. Three levels of multi-level perspective are socio-technical landscape (ST-landscape), socio-technical regime (ST-regime) and niche innovations in Indonesia geothermal development. The identification, mapping and analysis of the dynamic relationship between each level are the important pillars of the multi-level perspective framework. The analysis considers the set of rules, actors and controversies that may arise in the technological transition process. The identified geothermal resource risks are the basis of the emerging geothermal technological innovations in Indonesian geothermal. The analysis of this study reveals the transition pathway, which yields a forecast for the Indonesian geothermal technology transition in the form of scenarios and probable impacts.

Characterization of biosurfactant produced by petrofilic bacteria isolated from hydrocarbon impacted soil and its potential application in bioremediation

NASA Astrophysics Data System (ADS)

Arsyah, D. M.; Kardena, E.; Helmy, Q.

2018-01-01

The study adopts a multi-level perspective in technology transition to analyse how the transition process in the development of geothermal energy in Indonesia is able to compete against the incumbent fossil-fuelled energy sources. Three levels of multi-level perspective are socio-technical landscape (ST-landscape), socio-technical regime (ST-regime) and niche innovations in Indonesia geothermal development. The identification, mapping and analysis of the dynamic relationship between each level are the important pillars of the multi-level perspective framework. The analysis considers the set of rules, actors and controversies that may arise in the technological transition process. The identified geothermal resource risks are the basis of the emerging geothermal technological innovations in Indonesian geothermal. The analysis of this study reveals the transition pathway, which yields a forecast for the Indonesian geothermal technology transition in the form of scenarios and probable impacts.

Predictability and extended-range prognosis in natural hazard risk mitigation process: A case study over west Greece

NASA Astrophysics Data System (ADS)

Matsangouras, Ioannis T.; Nastos, Panagiotis T.

2014-05-01

Natural hazards pose an increasing threat to society and new innovative techniques or methodologies are necessary to be developed, in order to enhance the risk mitigation process in nowadays. It is commonly accepted that disaster risk reduction is a vital key for future successful economic and social development. The systematic improvement accuracy of extended-range prognosis products, relating with monthly and seasonal predictability, introduced them as a new essential link in risk mitigation procedure. Aiming at decreasing the risk, this paper presents the use of seasonal and monthly forecasting process that was tested over west Greece from September to December, 2013. During that season significant severe weather events occurred, causing significant impact to the local society (severe storms/rainfalls, hail, flash floods, etc). Seasonal and monthly forecasting products from European Centre for Medium-Range Weather Forecasts (ECMWF) depicted, with probabilities stratified by terciles, areas of Greece where significant weather may occur. As atmospheric natural hazard early warning systems are able to deliver warnings up to 72 hours in advance, this study illustrates that extended-range prognosis could be introduced as a new technique in risk mitigation. Seasonal and monthly forecast products could highlight extended areas where severe weather events may occur in one month lead time. In addition, a risk mitigation procedure, that extended prognosis products are adopted, is also presented providing useful time to preparedness process at regional administration level.

Constraining Stochastic Parametrisation Schemes Using High-Resolution Model Simulations

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Dawson, A.; Palmer, T.

2017-12-01

Stochastic parametrisations are used in weather and climate models as a physically motivated way to represent model error due to unresolved processes. Designing new stochastic schemes has been the target of much innovative research over the last decade. While a focus has been on developing physically motivated approaches, many successful stochastic parametrisation schemes are very simple, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) multiplicative scheme `Stochastically Perturbed Parametrisation Tendencies' (SPPT). The SPPT scheme improves the skill of probabilistic weather and seasonal forecasts, and so is widely used. However, little work has focused on assessing the physical basis of the SPPT scheme. We address this matter by using high-resolution model simulations to explicitly measure the `error' in the parametrised tendency that SPPT seeks to represent. The high resolution simulations are first coarse-grained to the desired forecast model resolution before they are used to produce initial conditions and forcing data needed to drive the ECMWF Single Column Model (SCM). By comparing SCM forecast tendencies with the evolution of the high resolution model, we can measure the `error' in the forecast tendencies. In this way, we provide justification for the multiplicative nature of SPPT, and for the temporal and spatial scales of the stochastic perturbations. However, we also identify issues with the SPPT scheme. It is therefore hoped these measurements will improve both holistic and process based approaches to stochastic parametrisation. Figure caption: Instantaneous snapshot of the optimal SPPT stochastic perturbation, derived by comparing high-resolution simulations with a low resolution forecast model.

Examining the articulation of innovativeness in co-creative firms: a neural network approach

NASA Astrophysics Data System (ADS)

di Tollo, Giacomo; Tanev, Stoyan

2010-10-01

Value co-creation is an emerging marketing and innovation paradigm describing a broader opening of the firm to its customers by providing them with the opportunity to become active participants in the design and development of personalized products, services and experiences. The aim of the present contribution is to provide preliminary results from a research project focusing on the relationship between value co-creation and the perception of innovation in technology-driven firms. The data was collected in a previous study using web search techniques and factor analysis to identify the key co-creation components and the frequency of firms' online comments about their new products, processes and services. The present work focuses on using an Artificial Neural Network (ANN) approach to understand if the extent of value co-creation activities can be thought of as an indicator of the perception of innovation. The preliminary simulation results indicate the existence of such relationship. The ANN approach does not suggest a specific model but the relationship that was found out between the forecasted values of the perception of innovation and its actual values clearly points in this direction.

Examining the articulation of innovativeness in co-creative firms: a neural network approach

NASA Astrophysics Data System (ADS)

di Tollo, Giacomo; Tanev, Stoyan

2011-03-01

Value co-creation is an emerging marketing and innovation paradigm describing a broader opening of the firm to its customers by providing them with the opportunity to become active participants in the design and development of personalized products, services and experiences. The aim of the present contribution is to provide preliminary results from a research project focusing on the relationship between value co-creation and the perception of innovation in technology-driven firms. The data was collected in a previous study using web search techniques and factor analysis to identify the key co-creation components and the frequency of firms' online comments about their new products, processes and services. The present work focuses on using an Artificial Neural Network (ANN) approach to understand if the extent of value co-creation activities can be thought of as an indicator of the perception of innovation. The preliminary simulation results indicate the existence of such relationship. The ANN approach does not suggest a specific model but the relationship that was found out between the forecasted values of the perception of innovation and its actual values clearly points in this direction.

Microseismic techniques for avoiding induced seismicity during fluid injection

DOE PAGES

Matzel, Eric; White, Joshua; Templeton, Dennise; ...

2014-01-01

The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

Medium-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

2018-06-01

Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

Informing policy makers about future health spending: a comparative analysis of forecasting methods in OECD countries.

PubMed

Astolfi, Roberto; Lorenzoni, Luca; Oderkirk, Jillian

2012-09-01

Concerns about health care expenditure growth and its long-term sustainability have risen to the top of the policy agenda in many OECD countries. As continued growth in spending places pressure on government budgets, health services provision and patients' personal finances, policy makers have launched forecasting projects to support policy planning. This comparative analysis reviewed 25 models that were developed for policy analysis in OECD countries by governments, research agencies, academics and international organisations. We observed that the policy questions that need to be addressed drive the choice of forecasting model and the model's specification. By considering both the level of aggregation of the units analysed and the level of detail of health expenditure to be projected, we identified three classes of models: micro, component-based, and macro. Virtually all models account for demographic shifts in the population, while two important influences on health expenditure growth that are the least understood include technological innovation and health-seeking behaviour. The landscape for health forecasting models is dynamic and evolving. Advances in computing technology and increases in data granularity are opening up new possibilities for the generation of system of models which become an on-going decision support tool capable of adapting to new questions as they arise. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Models and applications for space weather forecasting and analysis at the Community Coordinated Modeling Center.

NASA Astrophysics Data System (ADS)

Kuznetsova, Maria

The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.

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.

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.

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.

Assessing public forecasts to encourage accountability: The case of MIT's Technology Review.

PubMed

Funk, Jeffrey

2017-01-01

Although high degrees of reliability have been found for many types of forecasts purportedly due to the existence of accountability, public forecasts of technology are rarely assessed and continue to have a poor reputation. This paper's analysis of forecasts made by MIT's Technology Review provides a rare assessment and thus a means to encourage accountability. It first shows that few of the predicted "breakthrough technologies" currently have large markets. Only four have sales greater than $10 billion while eight technologies not predicted by Technology Review have sales greater than $10 billion including three with greater than $100 billion and one other with greater than $50 billion. Second, possible reasons for these poor forecasts are then discussed including an over emphasis on the science-based process of technology change, sometimes called the linear model of innovation. Third, this paper describes a different model of technology change, one that is widely used by private companies and that explains the emergence of those technologies that have greater than $10 billion in sales. Fourth, technology change and forecasts are discussed in terms of cognitive biases and mental models.

Multi-step-ahead crude oil price forecasting using a hybrid grey wave model

NASA Astrophysics Data System (ADS)

Chen, Yanhui; Zhang, Chuan; He, Kaijian; Zheng, Aibing

2018-07-01

Crude oil is crucial to the operation and economic well-being of the modern society. Huge changes of crude oil price always cause panics to the global economy. There are many factors influencing crude oil price. Crude oil price prediction is still a difficult research problem widely discussed among researchers. Based on the researches on Heterogeneous Market Hypothesis and the relationship between crude oil price and macroeconomic factors, exchange market, stock market, this paper proposes a hybrid grey wave forecasting model, which combines Random Walk (RW)/ARMA to forecast multi-step-ahead crude oil price. More specifically, we use grey wave forecasting model to model the periodical characteristics of crude oil price and ARMA/RW to simulate the daily random movements. The innovation also comes from using the information of the time series graph to forecast crude oil price, since grey wave forecasting is a graphical prediction method. The empirical results demonstrate that based on the daily data of crude oil price, the hybrid grey wave forecasting model performs well in 15- to 20-step-ahead prediction and it always dominates ARMA and Random Walk in correct direction prediction.

Value of biologic therapy: a forecasting model in three disease areas.

PubMed

Paramore, L Clark; Hunter, Craig A; Luce, Bryan R; Nordyke, Robert J; Halbert, R J

2010-01-01

Forecast the return on investment (ROI) for advances in biologic therapies in years 2015 and 2030, based upon impact on disease prevalence, morbidity, and mortality for asthma, diabetes, and colorectal cancer. A deterministic, spreadsheet-based, forecasting model was developed based on trends in demographics and disease epidemiology. 'Return' was defined as reductions in disease burden (prevalence, morbidity, mortality) translated into monetary terms; 'investment' was defined as the incremental costs of biologic therapy advances. Data on disease prevalence, morbidity, mortality, and associated costs were obtained from government survey statistics or published literature. Expected impact of advances in biologic therapies was based on expert opinion. Gains in quality-adjusted life years (QALYs) were valued at $100,000 per QALY. The base case analysis, in which reductions in disease prevalence and mortality predicted by the expert panel are not considered, shows the resulting ROIs remain positive for asthma and diabetes but fall below $1 for colorectal cancer. Analysis involving expert panel predictions indicated positive ROI results for all three diseases at both time points, ranging from $207 for each incremental dollar spent on biologic therapies to treat asthma in 2030, to $4 for each incremental dollar spent on biologic therapies to treat colorectal cancer in 2015. If QALYs are not considered, the resulting ROIs remain positive for all three diseases at both time points. Society may expect substantial returns from investments in innovative biologic therapies. These benefits are most likely to be realized in an environment of appropriate use of new molecules. The potential variance between forecasted (from expert opinion) and actual future health outcomes could be significant. Similarly, the forecasted growth in use of biologic therapies relied upon unvalidated market forecasts.

Unusual spacecraft materials

NASA Technical Reports Server (NTRS)

Post, Jonathan V.

1990-01-01

For particularly innovative space exploration missions, unusual requirements are levied on the structural components of the spacecraft. In many cases, the preferred solution is the utilization of unusual materials. This trend is forecast to continue. Several hypothetic examples are discussed.

An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

Trends in Microfabrication Capabilities & Device Architectures.

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

Bauer, Todd; Jones, Adam; Lentine, Anthony L.

The last two decades have seen an explosion in worldwide R&D, enabling fundamentally new capabilities while at the same time changing the international technology landscape. The advent of technologies for continued miniaturization and electronics feature size reduction, and for architectural innovations, will have many technical, economic, and national security implications. It is important to anticipate possible microelectronics development directions and their implications on US national interests. This report forecasts and assesses trends and directions for several potentially disruptive microfabrication capabilities and device architectures that may emerge in the next 5-10 years.

Project Selection in Monolithic Organizations.

DTIC Science & Technology

1974-08-01

indicators reflecting government’s objectives, forecast the total impact of R & D activities and evaluate them in terms of these indicators. This will be... Industrial Innovation Centre. P.O. Box 6079 Station A Montreal, Quebec H3C 3A7 43. James T. Goode Department of Comerce Japan’s Postwar Experience With and...Faculty of Administration, Study of the Snowmobile Industry University of Sherbrooke. in Canada and the Role that Technological Innovation has Played in

About the Mid-Continent Ecology Division (MED) of EPA's National Health and Environmental Effects Research Laboratory

EPA Pesticide Factsheets

The Mid-Continent Ecology Division (MED) conducts innovative research and predictive modeling to document and forecast the effects of pollutants on the integrity of watersheds and freshwater ecosystems.

Forecasting drought risks for a water supply storage system using bootstrap position analysis

USGS Publications Warehouse

Tasker, Gary; Dunne, Paul

1997-01-01

Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

Quantifying innovation in surgery.

PubMed

Hughes-Hallett, Archie; Mayer, Erik K; Marcus, Hani J; Cundy, Thomas P; Pratt, Philip J; Parston, Greg; Vale, Justin A; Darzi, Ara W

2014-08-01

The objectives of this study were to assess the applicability of patents and publications as metrics of surgical technology and innovation; evaluate the historical relationship between patents and publications; develop a methodology that can be used to determine the rate of innovation growth in any given health care technology. The study of health care innovation represents an emerging academic field, yet it is limited by a lack of valid scientific methods for quantitative analysis. This article explores and cross-validates 2 innovation metrics using surgical technology as an exemplar. Electronic patenting databases and the MEDLINE database were searched between 1980 and 2010 for "surgeon" OR "surgical" OR "surgery." Resulting patent codes were grouped into technology clusters. Growth curves were plotted for these technology clusters to establish the rate and characteristics of growth. The initial search retrieved 52,046 patents and 1,801,075 publications. The top performing technology cluster of the last 30 years was minimally invasive surgery. Robotic surgery, surgical staplers, and image guidance were the most emergent technology clusters. When examining the growth curves for these clusters they were found to follow an S-shaped pattern of growth, with the emergent technologies lying on the exponential phases of their respective growth curves. In addition, publication and patent counts were closely correlated in areas of technology expansion. This article demonstrates the utility of publically available patent and publication data to quantify innovations within surgical technology and proposes a novel methodology for assessing and forecasting areas of technological innovation.

How seasonal forecast could help a decision maker: an example of climate service for water resource management

NASA Astrophysics Data System (ADS)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

New drug adoption models: a review and assessment of future needs.

PubMed

Agrawal, M; Calantone, R J

1995-01-01

New drug products today are the key to survival in the pharmaceutical industry. However, the new product development process in the pharmaceutical industry also happens to be one of the riskiest and most expensive undertakings because of the huge research and development costs involved. Consequently market forecasting of new pharmaceutical products takes on added importance if the formidable investments are to be recovered. New drug adoption models provide the marketer with a means to assess new product potential. Although several adoption models are available in the marketing literature for assessing potential of common consumer goods, the unique characteristics of the prescription drug market makes it necessary to examine the current state of pharmaceutical innovations. The purpose of this study, therefore, is to: (1) review new drug adoption models in the pharmaceutical literature, (2) evaluate the existing models of new drug adoption using the ten criteria for a good model as prescribed by Zaltman and Wallendorf (1983), and (3) provide an overall assessment and a ¿prescription¿ for better forecasting of new drug products.

A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

USGS Publications Warehouse

Holtschlag, David J.; Sweat, M.J.

1999-01-01

Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26- well network could be described by the 6 most effective wells.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Clinical leadership development and education for nurses: prospects and opportunities

PubMed Central

Joseph, M Lindell; Huber, Diane L

2015-01-01

With the implementation of the Affordable Care Act, elevated roles for nurses of care coordinator, clinical nurse leader, and advanced practice registered nurse have come to the forefront. Because change occurs so fast, matching development and education to job requirements is a challenging forecasting endeavor. The purpose of this article is to envision clinical leadership development and education opportunities for three emerging roles. The adoption of a common framework for intentional leadership development is proposed for clinical leadership development across the continuum of care. Solutions of innovation and interdependency are framed as core concepts that serve as an opportunity to better inform clinical leadership development and education. Additionally, strategies are proposed to advance knowledge, skills, and abilities for crucial implementation of improvements and new solutions at the point of care. PMID:29355179

A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.

The Next Level in Automated Solar Flare Forecasting: the EU FLARECAST Project

NASA Astrophysics Data System (ADS)

Georgoulis, M. K.; Bloomfield, D.; Piana, M.; Massone, A. M.; Gallagher, P.; Vilmer, N.; Pariat, E.; Buchlin, E.; Baudin, F.; Csillaghy, A.; Soldati, M.; Sathiapal, H.; Jackson, D.; Alingery, P.; Argoudelis, V.; Benvenuto, F.; Campi, C.; Florios, K.; Gontikakis, C.; Guennou, C.; Guerra, J. A.; Kontogiannis, I.; Latorre, V.; Murray, S.; Park, S. H.; Perasso, A.; Sciacchitano, F.; von Stachelski, S.; Torbica, A.; Vischi, D.

2017-12-01

We attempt an informative description of the Flare Likelihood And Region Eruption Forecasting (FLARECAST) project, European Commission's first large-scale investment to explore the limits of reliability and accuracy achieved for the forecasting of major solar flares. We outline the consortium, top-level objectives and first results of the project, highlighting the diversity and fusion of expertise needed to deliver what was promised. The project's final product, featuring an openly accessible, fully modular and free to download flare forecasting facility will be delivered in early 2018. The project's three objectives, namely, science, research-to-operations and dissemination / communication, are also discussed: in terms of science, we encapsulate our close-to-final assessment on how close (or far) are we from a practically exploitable solar flare forecasting. In terms of R2O, we briefly describe the architecture of the FLARECAST infrastructure that includes rigorous validation for each forecasting step. From the three different communication levers of the project we finally focus on lessons learned from the two-way interaction with the community of stakeholders and governmental organizations. The FLARECAST project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 640216.

Assessing public forecasts to encourage accountability: The case of MIT’s Technology Review

PubMed Central

2017-01-01

Although high degrees of reliability have been found for many types of forecasts purportedly due to the existence of accountability, public forecasts of technology are rarely assessed and continue to have a poor reputation. This paper’s analysis of forecasts made by MIT’s Technology Review provides a rare assessment and thus a means to encourage accountability. It first shows that few of the predicted “breakthrough technologies” currently have large markets. Only four have sales greater than $10 billion while eight technologies not predicted by Technology Review have sales greater than $10 billion including three with greater than $100 billion and one other with greater than $50 billion. Second, possible reasons for these poor forecasts are then discussed including an over emphasis on the science-based process of technology change, sometimes called the linear model of innovation. Third, this paper describes a different model of technology change, one that is widely used by private companies and that explains the emergence of those technologies that have greater than $10 billion in sales. Fourth, technology change and forecasts are discussed in terms of cognitive biases and mental models. PMID:28797114

Integration of climate change considerations in statewide and regional transportation planning processes

DOT National Transportation Integrated Search

2009-07-01

This report is part on on-going work for the US Department of Transportations Center for Climate Change and Environmental Forecasting and the Federal Highway Administration to highlight innovative actions and initiatives undertaken by states and m...

From Forecasters to the General Public: A Communication Tool to Understand Decision-making Challenges in Weather-related Early Warning Systems

NASA Astrophysics Data System (ADS)

Terti, G.; Ruin, I.; Kalas, M.; Lorini, V.; Sabbatini, T.; i Alonso, A. C.

2017-12-01

New technologies are currently adopted worldwide to improve weather forecasts and communication of the corresponding warnings to the end-users. "EnhANcing emergency management and response to extreme WeatHER and climate Events" (ANYWHERE) project is an innovating action that aims at developing and implementing a European decision-support platform for weather-related risks integrating cutting-edge forecasting technology. The initiative is built in a collaborative manner where researchers, developers, potential users and other stakeholders meet frequently to define needs, capabilities and challenges. In this study, we propose a role-playing game to test the added value of the ANYWHERE platform on i) the decision-making process and the choice of warning levels under uncertainty, ii) the management of the official emergency response and iii) the crisis communication and triggering of protective actions at different levels of the warning system (from hazard detection to citizen response). The designed game serves as an interactive communication tool. Here, flood and flash flood focused simulations seek to enhance participant's understanding of the complexities and challenges embedded in various levels of the decision-making process under the threat of weather disasters (e.g., forecasting/warnings, official emergency actions, self-protection). Also, we facilitate collaboration and coordination between the participants who belong to different national or local agencies/authorities across Europe. The game is first applied and tested in ANYWHERE's workshop in Helsinki (September, 2017) where about 30-50 people, including researchers, forecasters, civil protection and representatives of related companies, are anticipated to play the simulation. The main idea is to provide to the players a virtual case study that well represents realistic uncertainties and dilemmas embedded in the real-time forecasting-warning processes. At the final debriefing step the participants are encouraged to exchange knowledge, thoughts and insights on their capability or difficulty to decide and communicate their action based on the available information and given constrains. Such feedback will be analyzed and presented and future potentialities for the application of the game will be discussed.

A time series model: First-order integer-valued autoregressive (INAR(1))

NASA Astrophysics Data System (ADS)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

USA Nutrient managment forecasting via the "Fertilizer Forecaster": linking surface runnof, nutrient application and ecohydrology.

NASA Astrophysics Data System (ADS)

Drohan, Patrick; Buda, Anthony; Kleinman, Peter; Miller, Douglas; Lin, Henry; Beegle, Douglas; Knight, Paul

2017-04-01

USA and state nutrient management planning offers strategic guidance that strives to educate farmers and those involved in nutrient management to make wise management decisions. A goal of such programs is to manage hotspots of water quality degradation that threaten human and ecosystem health, water and food security. The guidance provided by nutrient management plans does not provide the day-to-day support necessary to make operational decisions, particularly when and where to apply nutrients over the short term. These short-term decisions on when and where to apply nutrients often make the difference between whether the nutrients impact water quality or are efficiently utilized by crops. Infiltrating rainfall events occurring shortly after broadcast nutrient applications are beneficial, given they will wash soluble nutrients into the soil where they are used by crops. Rainfall events that generate runoff shortly after nutrients are broadcast may wash off applied nutrients, and produce substantial nutrient losses from that site. We are developing a model and data based support tool for nutrient management, the Fertilizer Forecaster, which identifies the relative probability of runoff or infiltrating events in Pennsylvania (PA) landscapes in order to improve water quality. This tool will support field specific decisions by farmers and land managers on when and where to apply fertilizers and manures over 24, 48 and 72 hour periods. Our objectives are to: (1) monitor agricultural hillslopes in watersheds representing four of the five Physiographic Provinces of the Chesapeake Bay basin; (2) validate a high resolution mapping model that identifies soils prone to runoff; (3) develop an empirically based approach to relate state-of-the-art weather forecast variables to site-specific rainfall infiltration or runoff occurrence; (4) test the empirical forecasting model against alternative approaches to forecasting runoff occurrence; and (5) recruit farmers from the four watersheds to use web-based forecast maps in daily manure and fertilizer application decisions. Data from on-farm trials is being used to assess farmer fertilizer, manure, and tillage management decisions before and after use of the Fertilizer Forecaster. This data will help us understand not only the effectiveness of the tool, but also characteristics of farmers with the greatest potential to benefit from such a tool. Feedback from on-farm trials will be used to refine a final tool for field deployment. We hope that the Fertilizer Forecaster will serve as the basis for state (USA-PA), regional (Chesapeake Bay), and national changes in nutrient management planning. This Fertilizer Forecaster is an innovative management practice that is designed to enhance the services of aquatic ecosystems by improving water quality and enhance the services of terrestrial ecosystems by increasing the efficiency of nutrient use by targeted crops.

Nanogenerators for Human Body Energy Harvesting.

PubMed

Proto, Antonino; Penhaker, Marek; Conforto, Silvia; Schmid, Maurizio

2017-07-01

Humans generate remarkable quantities of energy while performing daily activities, but this energy usually dissipates into the environment. Here, we address recent progress in the development of nanogenerators (NGs): devices that are able to harvest such body-produced biomechanical and thermal energies by exploiting piezoelectric, triboelectric, and thermoelectric physical effects. In designing NGs, the end-user's comfort is a primary concern. Therefore, we focus on recently developed materials giving flexibility and stretchability to NGs. In addition, we summarize common fabrics for NG design. Finally, the mid-2020s market forecasts for these promising technologies highlight the potential for the commercialization of NGs because they may help contribute to the route of innovation for developing self-powered systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2013-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC. Distribution Statement A: Approved for Public Release; distribution is unlimited

Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

NASA Astrophysics Data System (ADS)

Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

2012-12-01

The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi-model ensemble forecasting, to U.S. Department of Defense use, and creating a superior U.S. global meteorological and oceanographic prediction capability. Forecast verification is an important component of NAEFS and NUOPC.

Road icing forecasting and detecting system

NASA Astrophysics Data System (ADS)

Xu, Hongke; Zheng, Jinnan; Li, Peiqi; Wang, Qiucai

2017-05-01

Regard for the facts that the low accuracy and low real-time of the artificial observation to determine the road icing condition, and it is difficult to forecast icing situation, according to the main factors influencing the road-icing, and the electrical characteristics reflected by the pavement ice layer, this paper presents an innovative system, that is, ice-forecasting of the highway's dangerous section. The system bases on road surface water salinity measurements and pavement temperature measurement to calculate the freezing point of water and temperature change trend, and then predicts the occurrence time of road icing; using capacitance measurements to verdict the road surface is frozen or not; This paper expounds the method of using single chip microcomputer as the core of the control system and described the business process of the system.

An operational mesoscale ensemble data assimilation and prediction system: E-RTFDDA

NASA Astrophysics Data System (ADS)

Liu, Y.; Hopson, T.; Roux, G.; Hacker, J.; Xu, M.; Warner, T.; Swerdlin, S.

2009-04-01

Mesoscale (2-2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, and physics processes that are parameterized in NWP models play a great role. Complex interactions of synoptic circulations, regional and local terrain, land-surface heterogeneity, and associated physical properties, and the physical processes of radiative transfer, cloud and precipitation and boundary layer mixing, are crucial in shaping regional weather and climate. Mesoscale ensemble analysis and prediction should sample the uncertainties of mesoscale modeling systems in representing these factors. An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system has been developed at NCAR. E-RTFDDA contains diverse ensemble perturbation approaches that consider uncertainties in all major system components to produce multi-scale continuously-cycling probabilistic data assimilation and forecasting. A 30-member E-RTFDDA system with three nested domains with grid sizes of 30, 10 and 3.33 km has been running on a Department of Defense high-performance computing platform since September 2007. It has been applied at two very different US geographical locations; one in the western inter-mountain area and the other in the northeastern states, producing 6 hour analyses and 48 hour forecasts, with 4 forecast cycles a day. The operational model outputs are analyzed to a) assess overall ensemble performance and properties, b) study terrain effect on mesoscale predictability, c) quantify the contribution of different ensemble perturbation approaches to the overall forecast skill, and d) assess the additional contributed skill from an ensemble calibration process based on a quantile-regression algorithm. The system and the results will be reported at the meeting.

The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

NASA Astrophysics Data System (ADS)

Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

2017-04-01

Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the apparent benefit from using ensembles for cloudy radiance assimilation in an EnVar context.

Innovative Near Real-Time Data Dissemination Tools Developed by the Space Weather Research Center

NASA Astrophysics Data System (ADS)

Mullinix, R.; Maddox, M. M.; Berrios, D.; Kuznetsova, M.; Pulkkinen, A.; Rastaetter, L.; Zheng, Y.

2012-12-01

Space weather affects virtually all of NASA's endeavors, from robotic missions to human exploration. Knowledge and prediction of space weather conditions are therefore essential to NASA operations. The diverse nature of currently available space environment measurements and modeling products compels the need for a single access point to such information. The Integrated Space Weather Analysis (iSWA) System provides this single point access along with the capability to collect and catalog a vast range of sources including both observational and model data. NASA Goddard Space Weather Research Center heavily utilizes the iSWA System daily for research, space weather model validation, and forecasting for NASA missions. iSWA provides the capabilities to view and analyze near real-time space weather data from any where in the world. This presentation will describe the technology behind the iSWA system and describe how to use the system for space weather research, forecasting, training, education, and sharing.

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.

Probabilistic seasonal Forecasts to deterministic Farm Leve Decisions: Innovative Approach

NASA Astrophysics Data System (ADS)

Mwangi, M. W.

2015-12-01

Climate change and vulnerability are major challenges in ensuring household food security. Climate information services have the potential to cushion rural households from extreme climate risks. However, most the probabilistic nature of climate information products is not easily understood by majority of smallholder farmers. Despite the probabilistic nature, climate information have proved to be a valuable climate risk adaptation strategy at the farm level. This calls for innovative ways to help farmers understand and apply climate information services to inform their farm level decisions. The study endeavored to co-design and test appropriate innovation systems for climate information services uptake and scale up necessary for achieving climate risk development. In addition it also determined the conditions necessary to support the effective performance of the proposed innovation system. Data and information sources included systematic literature review, secondary sources, government statistics, focused group discussions, household surveys and semi-structured interviews. Data wasanalyzed using both quantitative and qualitative data analysis techniques. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) software. Qualitative data was analyzed using qualitative techniques, which involved establishing the categories and themes, relationships/patterns and conclusions in line with the study objectives. Sustainable livelihood, reduced household poverty and climate change resilience were the impact that resulted from the study.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

NASA Astrophysics Data System (ADS)

Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

2016-06-01

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Personalized symptoms forecasting for pollen-induced allergic rhinitis sufferers

NASA Astrophysics Data System (ADS)

Voukantsis, D.; Berger, U.; Tzima, F.; Karatzas, K.; Jaeger, S.; Bergmann, K. C.

2015-07-01

Hay fever is a pollen-induced allergic reaction that strongly affects the overall quality of life of many individuals. The disorder may vary in severity and symptoms depending on patient-specific factors such as genetic disposition, individual threshold of pollen concentration levels, medication, former immunotherapy, and others. Thus, information services that improve the quality of life of hay fever sufferers must address the needs of each individual separately. In this paper, we demonstrate the development of information services that offer personalized pollen-induced symptoms forecasts. The backbone of these services consists of data of allergic symptoms reported by the users of the Personal Hay Fever Diary system and pollen concentration levels (European Aeroallergen Network) in several sampling sites. Data were analyzed using computational intelligence methods, resulting in highly customizable forecasting models that offer personalized warnings to users of the Patient Hay Fever Diary system. The overall system performance for the pilot area (Vienna and Lower Austria) reached a correlation coefficient of r = 0.71 ± 0.17 (average ± standard deviation) in a sample of 219 users with major contribution to the Pollen Hay Fever Diary system and an overall performance of r = 0.66 ± 0.18 in a second sample of 393 users, with minor contribution to the system. These findings provide an example of combining data from different sources using advanced data engineering in order to develop innovative e-health services with the capacity to provide more direct and personalized information to allergic rhinitis sufferers.

The impact bias in self and others: Affective and empathic forecasting in individuals with social anxiety.

PubMed

Arditte Hall, Kimberly A; Joormann, Jutta; Siemer, Matthias; Timpano, Kiara R

2018-07-01

People tend to overestimate the intensity and duration of affect (i.e., impact bias) when making predictions about their own and others' responding, termed affective and empathic forecasting, respectively. Research links impact biases to clinical symptoms of affective disorders, but little work has been done to examine how social anxiety is related to affective and empathic forecasting biases. The current investigation included two studies examining these associations in independent samples of young adults with dimensionally distributed social anxiety symptoms. Study 1 (N = 100) examined the associations between social anxiety and affective and empathic forecasts in response to a series of novel hypothetical vignettes in which a second-person narrator (i.e., the self) elicited anger, disgust, or happiness from another person (i.e., the other). Study 2 utilized an innovative experimental paradigm involving N = 68 participant dyads. Overall, results supported the existence of affective and empathic forecasting biases. Further, symptoms of social anxiety were associated with the tendency to overestimate one's own and others' negative affect and underestimate others' positive affect. Such forecasting biases may help to explain the avoidance that is characteristic of individuals with social anxiety and could represent a fruitful target of cognitive behavioral intervention. Copyright © 2018 Elsevier Ltd. All rights reserved.

Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

2016-02-01

Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an area spanning from San Diego, CA to Honolulu, HI, seven months in-advance. Key findings include: weighted combinations of models are strictly better than individual models; machine-learned combinations are especially better; and forecasts produced using our approach have the highest rank probability skill score most often.

Improving Ecological Forecasting: Data Assimilation Enhances the Ecological Forecast Horizon of a Complex Food Web

NASA Astrophysics Data System (ADS)

Massoud, E. C.; Huisman, J.; Benincà, E.; Bouten, W.; Vrugt, J. A.

2017-12-01

Species abundances in ecological communities can display chaotic non-equilibrium dynamics. A characteristic feature of chaotic systems is that long-term prediction of the system's trajectory is fundamentally impossible. How then should we make predictions for complex multi-species communities? We explore data assimilation (DA) with the Ensemble Kalman Filter (EnKF) to fuse a two-predator-two-prey model with abundance data from a long term experiment of a plankton community which displays chaotic dynamics. The results show that DA improves substantially the predictability and ecological forecast horizon of complex community dynamics. In addition, we show that DA helps provide guidance on measurement design, for instance on defining the frequency of observations. The study presented here is highly innovative, because DA methods at the current stage are almost unknown in ecology.

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, R. M.; Tai, K.-S.

2013-01-01

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

Evaluation of the Impact of an Innovative Immunization Practice Model Designed to Improve Population Health: Results of the Project IMPACT Immunizations Pilot.

PubMed

Bluml, Benjamin M; Brock, Kelly A; Hamstra, Scott; Tonrey, Lisa

2018-02-01

The goal of the initiative was to evaluate the impact of an innovative practice model on identification of unmet vaccination needs and vaccination rates. This was accomplished through a prospective, multisite, observational study in 8 community pharmacy practices with adults receiving an influenza vaccine with a documented vaccination forecast review from October 22, 2015 through March 22, 2016. When patients presented for influenza vaccinations, pharmacists utilized immunization information systems (IIS) data at the point of care to identify unmet vaccination needs, educate patients, and improve vaccination rates. The main outcome measures were the number of vaccination forecast reviews, patients educated, unmet vaccination needs identified and resolved, and vaccines administered. Pharmacists reviewed vaccination forecasts generated by clinical decision-support technology based on patient information documented in the IIS for 1080 patients receiving influenza vaccinations. The vaccination forecasts predicted there were 1566 additional vaccinations due at the time patients were receiving the influenza vaccine. Pharmacist assessments identified 36 contraindications and 196 potential duplications, leaving a net of 1334 unmet vaccination needs eligible for vaccination. In all, 447 of the 1334 unmet vaccinations needs were resolved during the 6-month study period, and the remainder of patients received information about their vaccination needs and recommendations to follow up for their vaccinations. Integration of streamlined principle-centered processes of care in immunization practices that allow pharmacists to utilize actionable point-of-care data resulted in identification of unmet vaccination needs, education of patients about their vaccination needs, a 41.4% increase in the number of vaccines administered, and significant improvements in routinely recommended adult vaccination rates.

Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

PubMed

Deo, Ravinesh C; Downs, Nathan; Parisi, Alfio V; Adamowski, Jan F; Quilty, John M

2017-05-01

Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model's absolute errors were small in magnitude (±0.25), whereas the MARS and M5 Model Tree models generated 53% and 48% of such errors, respectively, indicating the latter models' errors to be distributed in larger magnitude error range. In terms of peak global UVI forecasting, with half the level of error, the ELM model outperformed MARS and M5 Model Tree. A comparison of the magnitude of hourly-cumulated errors of 10-min lead time forecasts for diffuse and global UVI highlighted ELM model's greater accuracy compared to MARS, M5 Model Tree or Pro6UV models. This confirmed the versatility of an ELM model drawing on θ s data for VSTR forecasting of UVI at near real-time horizon. When applied to the goal of enhancing expert systems, ELM-based accurate forecasts capable of reacting quickly to measured conditions can enhance real-time exposure advice for the public, mitigating the potential for solar UV-exposure-related disease. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Innovations for Requirements Engineering

DTIC Science & Technology

2008-01-01

bidding decision system. Trade Managemen t Production Management Cost Management Settlement Forecasting & Decision LDAS Electricity Market MDAS Figure...i NAVAL POSTGRADUATE SCHOOL Monterey, California 93943-5000 Daniel T . Oliver Leonard A. Ferrari...Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of

Dutch national rainfallradar project: a unique corporation

NASA Astrophysics Data System (ADS)

Schuurmans, Hanneke; Maarten Verbree, Jan; Leijnse, Hidde; van Heeringen, Klaas-Jan; Uijlenhoet, Remko; Bierkens, Mark; van de Giesen, Nick; Gooijer, Jan; van den Houten, Gert

2013-04-01

Since January 2013 Dutch watermanagers have access to innovative high-quality rainfall data. This product is innovative because of the following reasons. (i) The product is developed in a 'golden triangle' construction - corporation between government, business and research institutes. (ii) Second the rainfall products are developed according to the open-source GPL license. The initiative comes from a group of water boards in the Netherlands that joined their forces to fund the development of a new rainfall product. Not only data from Dutch radar stations (as is currently done by the Dutch meteorological organization KNMI) is used but also data from radars in Germany and Belgium. After a radarcomposite is made, it is adjusted according to data from raingauges (ground truth). This results in 9 different rainfall products that give for each moment the best rainfall data. This data will be used, depending on the end-user for several applications: (i) forecasts: input for flood early warning systems, (ii) water system analysis: hydrological model input, (iii) optimization: real time control and (iv) investigation of incidents: in case of flooding, who's responsible. The latter is mainly insight in the return period of heavy rainfall events. More info (in Dutch): www.nationaleregenradar.nl

NASA Tech Briefs, December 1988. Volume 12, No. 11

NASA Technical Reports Server (NTRS)

1988-01-01

This month's technical section includes forecasts for 1989 and beyond by NASA experts in the following fields: Integrated Circuits; Communications; Computational Fluid Dynamics; Ceramics; Image Processing; Sensors; Dynamic Power; Superconductivity; Artificial Intelligence; and Flow Cytometry. The quotes provide a brief overview of emerging trends, and describe inventions and innovations being developed by NASA, other government agencies, and private industry that could make a significant impact in coming years. A second bonus feature in this month's issue is the expanded subject index that begins on page 98. The index contains cross-referenced listings for all technical briefs appearing in NASA Tech Briefs during 1988.

Past speculations of future health technologies: a description of technologies predicted in 15 forecasting studies published between 1986 and 2010

PubMed Central

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2017-01-01

Objective To describe and classify health technologies predicted in forecasting studies. Design and methods A portrait describing health technologies predicted in 15 forecasting studies published between 1986 and 2010 that were identified in a previous systematic review. Health technologies are classified according to their type, purpose and clinical use; relating these to the original purpose and timing of the forecasting studies. Data sources All health-related technologies predicted in 15 forecasting studies identified in a previously published systematic review. Main outcome measure Outcomes related to (1) each forecasting study including country, year, intention and forecasting methods used and (2) the predicted technologies including technology type, purpose, targeted clinical area and forecast timeframe. Results Of the 896 identified health-related technologies, 685 (76.5%) were health technologies with an explicit or implied health application and included in our study. Of these, 19.1% were diagnostic or imaging tests, 14.3% devices or biomaterials, 12.6% information technology systems, eHealth or mHealth and 12% drugs. The majority of the technologies were intended to treat or manage disease (38.1%) or diagnose or monitor disease (26.1%). The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The most frequent technology types were for: infectious diseases—prophylactic vaccines (45.8%), cancer—drugs (40%), circulatory disease—devices and biomaterials (26.3%), and diseases of the nervous system—equally devices and biomaterials (25%) and regenerative medicine (25%). The mean timeframe for forecasting was 11.6 years (range 0–33 years, median=10, SD=6.6). The forecasting timeframe significantly differed by technology type (p=0.002), the intent of the forecasting group (p<0.001) and the methods used (p<001). Conclusion While description and classification of predicted health-related technologies is crucial in preparing healthcare systems for adopting new innovations, further work is needed to test the accuracy of predictions made. PMID:28760796

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent

Space weather’s effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun’s photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET’s Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. In addition, an ENLIL/Rice Dst prediction out to several days has also been developed and will be described. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the “weather” of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

Solar Particle Radiation Storms Forecasting and Analysis within the Framework of the `HESPERIA' HORIZON 2020 Project

NASA Astrophysics Data System (ADS)

Posner, A.; Malandraki, O.; Nunez, M.; Heber, B.; Labrenz, J.; Kühl, P.; Milas, N.; Tsiropoula, G.; Pavlos, E.

2017-12-01

Two prediction tools that have been developed in the framework of HESPERIA based upon the proven concepts UMASEP and REleASE. Near-relativistic (NR) electrons traveling faster than ions (30 MeV protons have 0.25c) are used to forecast the arrival of protons of Solar Energetic Particle (SEP) events with real-time measurements of NR electrons. The faster electrons arrive at L1 30 to 90 minutes before the slower protons. REleASE (Relativistic Electron Alert System for Exploration, Posner, 2007) uses this effect to predict the proton flux by utilizing actual electron fluxes and their most recent increases. Through HESPERIA, a clone of REleASE was built in open source programming language. The same forecasting principle was adapted to real-time data from ACE/EPAM. It is shown that HESPERIA REleASE forecasting works with any NR electron flux measurements. >500 MeV solar protons are so energetic that they usually have effects on the ground, producing Ground Level Enhancement (GLE) events. Within HESPERIA, a predictor of >500 SEP proton events near earth (geostationary orbit) has been developed. In order to predict these events, UMASEP (Núñez, 2011, 2015) has been used. UMASEP makes a lag-correlation of solar electromagnetic (EM) flux with the particle flux near earth. If the correlation is high, the model infers that there is a magnetic connection through which particles are arriving. If, additionally, the intensity of the flux of the associated solar event is also high, then UMASEP issues a SEP prediction. In the case of the prediction of >500 MeV SEP events, the implemented system, called HESPERIA UMASEP-500, correlates X-ray flux with differential proton fluxes by GOES, and with fluxes collected by neutron monitor stations around the world. When the correlation estimation and flare surpasses thresholds, a >500 MeV SEP forecast is issued. These findings suggest that a synthesis of the various approaches may improve over the status quo. Both forecasting tools are operational on the HESPERIA server maintained at the National Observatory of Athens (https://www.hesperia.astro.noa.gr/). This project received funding from the EU's Horizon 2020 research and innovation programme under grant No 637324.

Assimilation of NUCAPS Retrieved Profiles in GSI for Unique Forecasting Applications

NASA Technical Reports Server (NTRS)

Berndt, Emily Beth; Zavodsky, Bradley; Srikishen, Jayanthi; Blankenship, Clay

2015-01-01

Hyperspectral IR profiles can be assimilated in GSI as a separate observation other than radiosondes with only changes to tables in the fix directory. Assimilation of profiles does produce changes to analysis fields and evidenced by: Innovations larger than +/-2.0 K are present and represent where individual profiles impact the final temperature analysis.The updated temperature analysis is colder behind the cold front and warmer in the warm sector. The updated moisture analysis is modified more in the low levels and tends to be drier than the original model background Analysis of model output shows: Differences relative to 13-km RAP analyses are smaller when profiles are assimilated with NUCAPS errors. CAPE is under-forecasted when assimilating NUCAPS profiles, which could be problematic for severe weather forecasting Refining the assimilation technique to incorporate an error covariance matrix and creating a separate GSI module to assimilate satellite profiles may improve results.

Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price

NASA Astrophysics Data System (ADS)

Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John

2015-02-01

Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series.

PubMed

Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Jia, Chun; Wang, Qiufan

2015-07-17

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS.

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series

PubMed Central

Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Jia, Chun; Wang, Qiufan

2015-01-01

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS. PMID:26193283

Satellites, tweets, forecasts: the future of flood disaster management?

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Floods have devastating effects on lives and livelihoods around the world. Structural flood defence measures such as dikes and dams can help protect people. However, it is the emerging science and technologies for flood disaster management and preparedness, such as increasingly accurate flood forecasting systems, high-resolution satellite monitoring, rapid risk mapping, and the unique strength of social media information and crowdsourcing, that are most promising for reducing the impacts of flooding. Here, we describe an innovative framework which integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. The integrated framework enables improved flood impact forecast, thanks to the real-time integration of forecasting and monitoring components, and increases the timeliness and efficiency of satellite mapping, with the aim of capturing flood peaks and following the evolution of flooding processes. Thanks to the proposed framework, emergency responders will have access to a broad range of timely and accurate information for more effective and robust planning, decision-making, and resource allocation.

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

A Social Justice Agenda: Ready, or Not?

ERIC Educational Resources Information Center

Speight, Suzette L.; Vera, Elizabeth M.

2004-01-01

This commentary highlights the innovative inclusion of social action groups in the 2001 Houston Conference and expands on their significance to the conference and the field. If the 2001 Houston Conference has correctly forecast a (re)establishment of social action as a mainstay of counseling psychology, then an in-depth exploration of how we train…

Long-run evolution of the global economy - Part 2: Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-10-01

Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolved societal dynamics. If this model is initialized with economic data from the 1950s, it yields hindcasts for how fast global economic production and energy consumption grew between 2000 and 2010 with skill scores > 90 % relative to a model of persistence in trends. The model appears to attain high skill partly because there was a strong impulse of discovery of fossil fuel energy reserves in the mid-twentieth century that helped civilization to grow rapidly as a deterministic physical response. Forecasting the coming century may prove more of a challenge because the effect of the energy impulse appears to have nearly run its course. Nonetheless, an understanding of the external forces that drive civilization may help development of constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting.

PubMed

Capizzi, Giacomo; Napoli, Christian; Bonanno, Francesco

2012-11-01

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

Stimulating innovation for global monitoring of agriculture and its impact on the environment in support of GEOGLAM

NASA Astrophysics Data System (ADS)

Bydekerke, Lieven; Gilliams, Sven; Gobin, Anne

2015-04-01

There is an urgent need to ensure food supply for a growing global population. To enable a sustainable growth of agricultural production, effective and timely information is required to support decision making and to improve management of agricultural resources. This requires innovative ways and monitoring methods that will not only improve short-term crop production forecasts, but also allow to assess changes in cultivation practices, agricultural areas, agriculture in general and, its impact on the environment. The G20 launched in June 2011 the "GEO Global Agricultural Monitoring initiative (GEOGLAM), requesting the GEO (Group on Earth Observations) Agricultural Community of Practice to implement GEOGLAM with the main objective to improve crop yield forecasts as an input to the Agricultural Market Information System (AMIS), in order to foster stabilisation of markets and increase transparency on agricultural production. In response to this need, the European Commission decided in 2013 to fund an international partnership to contribute to GEOGLAM and its research agenda. The resulting SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture), a partnership of 23 globally distributed expert organisations, focusses on developing datasets and innovative techniques in support of agricultural monitoring and its impact on the environment in support of GEOGLAM. SIGMA has 3 generic objectives which are: (i) develop and test methods to characterise cropland and assess its changes at various scales; (ii) develop and test methods to assess changes in agricultural production levels; and; (iii) study environmental impacts of agriculture. Firstly, multi-scale remote sensing data sets, in combination with field and other ancillary data, will be used to generate an improved (global) agro-ecological zoning map and crop mask. Secondly, a combination of agro-meteorological models, satellite-based information and long-term time series will be explored to assess crop yield gaps and shifts in cultivation. The third research topic entails the development of best practices for assessing the impact of crop land and cropping system change on the environment. In support of the GEO JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative, SIGMA has selected case studies in Ukraine, Russia, Europe, Africa, Latin America and China, coinciding with the JECAM sites in these area, to explore possible methodological synergies and particularities according to different cropping systems. In combination with research conducted at regional and global scale, it is one of the goals to improve the understanding of dynamics, interactions and validity of the developed methods at the various scales. In addition, specific activities will be dedicated to raising awareness and strengthening capacity for what concerns agro-environmental monitoring, data accessibility and interoperability in line with the GEOSS Data-core principles. The SIGMA project will also anticipate on the availability of the SENTINEL satellites for agricultural applications as open-data in the near future. References http://proba-v.vgt.vito.be/ http://www.geoglam-sigma.info/

Improved Anvil Forecasting

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2000-01-01

This report describes the outcome of Phase 1 of the AMU's Improved Anvil Forecasting task. Forecasters in the 45th Weather Squadron and the Spaceflight Meteorology Group have found that anvil forecasting is a difficult task when predicting LCC and FR violations. The purpose of this task is to determine the technical feasibility of creating an anvil-forecasting tool. Work on this study was separated into three steps: literature search, forecaster discussions, and determination of technical feasibility. The literature search revealed no existing anvil-forecasting techniques. However, there appears to be growing interest in anvils in recent years. If this interest continues to grow, more information will be available to aid in developing a reliable anvil-forecasting tool. The forecaster discussion step revealed an array of methods on how better forecasting techniques could be developed. The forecasters have ideas based on sound meteorological principles and personal experience in forecasting and analyzing anvils. Based on the information gathered in the discussions with the forecasters, the conclusion of this report is that it is technically feasible at this time to develop an anvil forecasting technique that will significantly contribute to the confidence in anvil forecasts.

Assessing and Adapting Scientific Results for Space Weather Research to Operations (R2O)

NASA Astrophysics Data System (ADS)

Thompson, B. J.; Friedl, L.; Halford, A. J.; Mays, M. L.; Pulkkinen, A. A.; Singer, H. J.; Stehr, J. W.

2017-12-01

Why doesn't a solid scientific paper necessarily result in a tangible improvement in space weather capability? A well-known challenge in space weather forecasting is investing effort to turn the results of basic scientific research into operational knowledge. This process is commonly known as "Research to Operations," abbreviated R2O. There are several aspects of this process: 1) How relevant is the scientific result to a particular space weather process? 2) If fully utilized, how much will that result improve the reliability of the forecast for the associated process? 3) How much effort will this transition require? Is it already in a relatively usable form, or will it require a great deal of adaptation? 4) How much burden will be placed on forecasters? Is it "plug-and-play" or will it require effort to operate? 5) How can robust space weather forecasting identify challenges for new research? This presentation will cover several approaches that have potential utility in assessing scientific results for use in space weather research. The demonstration of utility is the first step, relating to the establishment of metrics to ensure that there will be a clear benefit to the end user. The presentation will then move to means of determining cost vs. benefit, (where cost involves the full effort required to transition the science to forecasting, and benefit concerns the improvement of forecast reliability), and conclude with a discussion of the role of end users and forecasters in driving further innovation via "O2R."

Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

NASA Astrophysics Data System (ADS)

Tobiska, W.; Knipp, D. J.; Burke, W. J.; Bouwer, D.; Bailey, J. J.; Hagan, M. P.; Didkovsky, L. V.; Garrett, H. B.; Bowman, B. R.; Gannon, J. L.; Atwell, W.; Blake, J. B.; Crain, W.; Rice, D.; Schunk, R. W.; Fulgham, J.; Bell, D.; Gersey, B.; Wilkins, R.; Fuschino, R.; Flynn, C.; Cecil, K.; Mertens, C. J.; Xu, X.; Crowley, G.; Reynolds, A.; Azeem, S. I.; Wiley, S.; Holland, M.; Malone, K.

2013-12-01

Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET's Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the 'weather' of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

Status of the NASA GMAO Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2014-01-01

An Observing System Simulation Experiment (OSSE) is a pure modeling study used when actual observations are too expensive or difficult to obtain. OSSEs are valuable tools for determining the potential impact of new observing systems on numerical weather forecasts and for evaluation of data assimilation systems (DAS). An OSSE has been developed at the NASA Global Modeling and Assimilation Office (GMAO, Errico et al 2013). The GMAO OSSE uses a 13-month integration of the European Centre for Medium- Range Weather Forecasts 2005 operational model at T511/L91 resolution for the Nature Run (NR). Synthetic observations have been updated so that they are based on real observations during the summer of 2013. The emulated observation types include AMSU-A, MHS, IASI, AIRS, and HIRS4 radiance data, GPS-RO, and conventional types including aircraft, rawinsonde, profiler, surface, and satellite winds. The synthetic satellite wind observations are colocated with the NR cloud fields, and the rawinsondes are advected during ascent using the NR wind fields. Data counts for the synthetic observations are matched as closely as possible to real data counts, as shown in Figure 2. Errors are added to the synthetic observations to emulate representativeness and instrument errors. The synthetic errors are calibrated so that the statistics of observation innovation and analysis increments in the OSSE are similar to the same statistics for assimilation of real observations, in an iterative method described by Errico et al (2013). The standard deviations of observation minus forecast (xo-H(xb)) are compared for the OSSE and real data in Figure 3. The synthetic errors include both random, uncorrelated errors, and an additional correlated error component for some observational types. Vertically correlated errors are included for conventional sounding data and GPS-RO, and channel correlated errors are introduced to AIRS and IASI (Figure 4). HIRS, AMSU-A, and MHS have a component of horizontally correlated error. The forecast model used by the GMAO OSSE is the Goddard Earth Observing System Model, Version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) DAS. The model version has been updated to v. 5.13.3, corresponding to the current operational model. Forecasts are run on a cube-sphere grid with 180 points along each edge of the cube (approximately 0.5 degree horizontal resolution) with 72 vertical levels. The DAS is cycled at 6-hour intervals, with 240 hour forecasts launched daily at 0000 UTC. Evaluation of the forecasting skill for July and August is currently underway. Prior versions of the GMAO OSSE have been found to have greater forecasting skill than real world forecasts. It is anticipated that similar forecast skill will be found in the updated OSSE.

WetDATA Hub: Democratizing Access to Water Data to Accelerate Innovation through Data Visualization, Predictive Analytics and Artificial Intelligence Applications

NASA Astrophysics Data System (ADS)

Sarni, W.

2017-12-01

Water scarcity and poor quality impacts economic development, business growth, and social well-being. Water has become, in our generation, the foremost critical local, regional, and global issue of our time. Despite these needs, there is no water hub or water technology accelerator solely dedicated to water data and tools. There is a need by the public and private sectors for vastly improved data management and visualization tools. This is the WetDATA opportunity - to develop a water data tech hub dedicated to water data acquisition, analytics, and visualization tools for informed policy and business decisions. WetDATA's tools will help incubate disruptive water data technologies and accelerate adoption of current water data solutions. WetDATA is a Colorado-based (501c3), global hub for water data analytics and technology innovation. WetDATA's vision is to be a global leader in water information, data technology innovation and collaborate with other US and global water technology hubs. ROADMAP * Portal (www.wetdata.org) to provide stakeholders with tools/resources to understand related water risks. * The initial activities will provide education, awareness and tools to stakeholders to support the implementation of the Colorado State Water Plan. * Leverage the Western States Water Council Water Data Exchange database. * Development of visualization, predictive analytics and AI tools to engage with stakeholders and provide actionable data and information. TOOLS Education: Provide information on water issues and risks at the local, state, national and global scale. Visualizations: Development of data analytics and visualization tools based upon the 2030 Water Resources Group methodology to support the implementation of the Colorado State Water Plan. Predictive Analytics: Accessing publically available water databases and using machine learning to develop water availability forecasting tools, and time lapse images to support city / urban planning.

The Use of Rainfall Forecasts as a Decision Guide for Small-Scale Farming in Limpopo Province, South Africa

ERIC Educational Resources Information Center

Moeletsi, M. E.; Mellaart, E. A. R.; Mpandeli, N. S.; Hamandawana, H.

2013-01-01

Purpose: New innovative ways of communicating agrometeorological information are needed to help farmers, especially subsistence/small-scale farmers, to cope with the high climate variability experienced in most parts of southern Africa. Design/methodology/approach: The article introduces an early warning system for farmers. It utilizes short…

Tax-Credit Scholarships in Maryland: Forecasting the Fiscal Impact

ERIC Educational Resources Information Center

Gottlob, Brian

2010-01-01

This study seeks to inform the debate over a proposal in Maryland to give tax credits to businesses for contributions to organizations that provide scholarships to K-12 private schools or which contribute to innovative educational programs in the public schools. The study constructs a model to determine the fiscal impact of a tax-credit…

Target Your Brand: Build an Identity that Works in the Age of the Superstore

ERIC Educational Resources Information Center

Dempsey, Beth

2004-01-01

As bookstores and the Internet march forward, the library community continues to question and forecast its role in society. Innovative libraries nationwide have seized the opportunity to reinvent themselves, bringing a new level of excitement to the industry. Yet puzzlement remains on what strategies, what roles will work in communities where…

Mid-term fire danger index based on satellite imagery and ancillary geographic data

NASA Astrophysics Data System (ADS)

Stefanidou, A.; Dragozi, E.; Tompoulidou, M.; Stepanidou, L.; Grigoriadis, D.; Katagis, T.; Stavrakoudis, D.; Gitas, I.

2017-09-01

Fire danger forecast constitutes one of the most important components of integrated fire management since it provides crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The methodology that is currently under development is based on an innovative approach that employs dry fuel spatial connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically, the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data. Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires (NOFFi) project.

Space Weather Outreach: Connection to STEM Standards

NASA Astrophysics Data System (ADS)

Dusenbery, P. B.

2008-12-01

Many scientists are studying the Sun-Earth system and attempting to provide timely, accurate, and reliable space environment observations and forecasts. Research programs and missions serve as an ideal focal point for creating educational content, making this an ideal time to inform the public about the importance and value of space weather research. In order to take advantage of this opportunity, the Space Science Institute (SSI) is developing a comprehensive Space Weather Outreach program to reach students, educators, and other members of the public, and share with them the exciting discoveries from this important scientific discipline. The Space Weather Outreach program has the following five components: (1) the Space Weather Center Website that includes online educational games; (2) Small Exhibits for Libraries, Shopping Malls, and Science Centers; (3) After-School Programs; (4) Professional Development Workshops for Educators, and (5) an innovative Evaluation and Education Research project. Its overarching goal is to inspire, engage, and educate a broad spectrum of the public and make strategic and innovative connections between informal and K-12 education communities. An important factor in the success of this program will be its alignment with STEM standards especially those related to science and mathematics. This presentation will describe the Space Weather Outreach program and how standards are being used in the development of each of its components.

Past speculations of future health technologies: a description of technologies predicted in 15 forecasting studies published between 1986 and 2010.

PubMed

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2017-07-31

To describe and classify health technologies predicted in forecasting studies. A portrait describing health technologies predicted in 15 forecasting studies published between 1986 and 2010 that were identified in a previous systematic review. Health technologies are classified according to their type, purpose and clinical use; relating these to the original purpose and timing of the forecasting studies. All health-related technologies predicted in 15 forecasting studies identified in a previously published systematic review. Outcomes related to (1) each forecasting study including country, year, intention and forecasting methods used and (2) the predicted technologies including technology type, purpose, targeted clinical area and forecast timeframe. Of the 896 identified health-related technologies, 685 (76.5%) were health technologies with an explicit or implied health application and included in our study. Of these, 19.1% were diagnostic or imaging tests, 14.3% devices or biomaterials, 12.6% information technology systems, eHealth or mHealth and 12% drugs. The majority of the technologies were intended to treat or manage disease (38.1%) or diagnose or monitor disease (26.1%). The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The most frequent technology types were for: infectious diseases-prophylactic vaccines (45.8%), cancer-drugs (40%), circulatory disease-devices and biomaterials (26.3%), and diseases of the nervous system-equally devices and biomaterials (25%) and regenerative medicine (25%). The mean timeframe for forecasting was 11.6 years (range 0-33 years, median=10, SD=6.6). The forecasting timeframe significantly differed by technology type (p=0.002), the intent of the forecasting group (p<0.001) and the methods used (p<001). While description and classification of predicted health-related technologies is crucial in preparing healthcare systems for adopting new innovations, further work is needed to test the accuracy of predictions made. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Artificial Neural Network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning

NASA Astrophysics Data System (ADS)

French, Jon; Mawdsley, Robert; Fujiyama, Taku; Achuthan, Kamal

2017-04-01

Effective prediction of tidal storm surge is of considerable importance for operators of major ports, since much of their infrastructure is necessarily located close to sea level. Storm surge inundation can damage critical elements of this infrastructure and significantly disrupt port operations and downstream supply chains. The risk of surge inundation is typically approached using extreme value analysis, while short-term forecasting generally relies on coastal shelf-scale tide and surge models. However, extreme value analysis does not provide information on the duration of a surge event and can be sensitive to the assumptions made and the historic data available. Also, whilst regional tide and surge models perform well along open coasts, their fairly coarse spatial resolution means that they do not always provide accurate predictions for estuarine ports. As part of a NERC Environmental Risks to Infrastructure Innovation Programme project, we have developed a tool that is specifically designed to forecast the North Sea storm surges on major ports along the east coast of the UK. Of particular interest is the Port of Immingham, Humber estuary, which handles the largest volume of bulk cargo in the UK including major flows of coal and biomass for power generation. A tidal surge in December 2013, with an estimated return period of 760 years, partly flooded the port, damaged infrastructure and disrupted operations for several weeks. This and other recent surge events highlight the need for additional tools to supplement the national UK Storm Tide Warning Service. Port operators are also keen to have access to less computationally expensive forecasting tools for scenario planning and to improve their resilience to actual events. In this paper, we demonstrate the potential of machine learning methods based on Artificial Neural Networks (ANNs) to generate accurate short-term forecasts of extreme water levels at estuarine North Sea ports such as Immingham. An ANN is configured to take advantage of far-field information on developing tidal surges provided by tide gauges in NW Scotland (the 'external surge'), supported by observations of wind and atmospheric pressure and the predicted astronomical tide at Immingham. Missing data can cause problems with ANN models and a novel aspect of our implementation is the use of multiple redundant inputs (nearby tide gauges that experience a high degree of surge coherence) to synthesise a single external surge input. A similar approach is taken with meteorological forcings, creating an ANN that is resilient against data drop-outs within its input vector. The ANN generates 6 to 24 hour surge forecasts at Immingham with accuracy better than the present UK Storm Tide Warning Service. These can be used to cross-check national forecasts, generate more accurate estimates of likely flood depths, timings and durations and trigger planned responses to severe forecasts. Crucially, this capability can be 'owned' by the port operator, which encourages the development of a shared understanding of storm surge hazards and the challenges of port resilience planning between scientist and stakeholder.

Latest Community Coordinated Modeling Center (CCMC) services and innovative tools supporting the space weather research and operational communities.

NASA Astrophysics Data System (ADS)

Mendoza, A. M. M.; Rastaetter, L.; Kuznetsova, M. M.; Mays, M. L.; Chulaki, A.; Shim, J. S.; MacNeice, P. J.; Taktakishvili, A.; Collado-Vega, Y. M.; Weigand, C.; Zheng, Y.; Mullinix, R.; Patel, K.; Pembroke, A. D.; Pulkkinen, A. A.; Boblitt, J. M.; Bakshi, S. S.; Tsui, T.

2017-12-01

The Community Coordinated Modeling Center (CCMC), with the fundamental goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research, has been serving as an integral hub for over 15 years, providing invaluable resources to both space weather scientific and operational communities. CCMC has developed and provided innovative web-based point of access tools varying from: Runs-On-Request System - providing unprecedented global access to the largest collection of state-of-the-art solar and space physics models, Integrated Space Weather Analysis (iSWA) - a powerful dissemination system for space weather information, Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and Mobile apps to view space weather data anywhere to the scientific community. In addition to supporting research and performing model evaluations, CCMC also supports space science education by hosting summer students through local universities. In this poster, we will showcase CCMC's latest innovative tools and services, and CCMC's tools that revolutionized the way we do research and improve our operational space weather capabilities. CCMC's free tools and resources are all publicly available online (http://ccmc.gsfc.nasa.gov).

Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes

NASA Astrophysics Data System (ADS)

Lopes, Sílvia R. C.; Prass, Taiane S.

2014-05-01

Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if { is a FIEGARCH(p,d,q) process then, under mild conditions, { is an ARFIMA(q,d,0) with correlated innovations, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes { and { are discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH(p,d,q) process are also derived. The h-step ahead forecast for the processes {, { and { are given with their respective mean square error of forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.

A Case Study on Using Prediction Markets as a Rich Environment for Active Learning

ERIC Educational Resources Information Center

Buckley, Patrick; Garvey, John; McGrath, Fergal

2011-01-01

In this paper, prediction markets are presented as an innovative pedagogical tool which can be used to create a Rich Environment for Active Learning (REAL). Prediction markets are designed to make forecasts about specific future events by using a market mechanism to aggregate the information held by a large group of traders about that event into a…

Forecasting in foodservice: model development, testing, and evaluation.

PubMed

Miller, J L; Thompson, P A; Orabella, M M

1991-05-01

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

NASA Astrophysics Data System (ADS)

Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish

2009-11-01

Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

Soil moisture assimilation using a modified ensemble transform Kalman filter with water balance constraint

NASA Astrophysics Data System (ADS)

Wu, Guocan; Zheng, Xiaogu; Dan, Bo

2016-04-01

The shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. The forecast error is inflated to improve the analysis state accuracy and the water balance constraint is adopted to reduce the water budget residual in the assimilation procedure. The experiment results illustrate that the adaptive forecast error inflation can reduce the analysis error, while the proper inflation layer can be selected based on the -2log-likelihood function of the innovation statistic. The water balance constraint can result in reducing water budget residual substantially, at a low cost of assimilation accuracy loss. The assimilation scheme can be potentially applied to assimilate the remote sensing data.

Decadal climate prediction with a refined anomaly initialisation approach

NASA Astrophysics Data System (ADS)

Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.

2017-03-01

In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

Development of decision support system for oil spill management in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Liubartseva, Svitlana; Coppini, Giovanni; Pinardi, Nadia; De Dominicis, Michela; Marra, Palmalisa; Lecci, Rita; Turrisi, Giuseppe; Creti, Sergio; Martinelli, Sara; Agostini, Paola; Palermo, Francesco

2016-04-01

Possible oil spill accidents and operational pollution could have severe impacts on the Mediterranean basin. It is therefore crucial to provide decision makers, stakeholders, and public with trustworthy DSS (Decision Support System) based on the environmental monitoring, state-of-the-art modeling and innovative technology platforms. Innovative web-based decision support system, called WITOL (Where Is The Oil http://www.witoil.com), has been developed to maintain emergency management in case of oil spill accidents. WITOIL embraces (1) Lagrangian oil spill model MEDSLIK-II (De Dominicis et al., 2013 http://medslikii.bo.ingv.it) coupled with the basin-scale and regional operational oceanographic services; (2) two-modular block of oil spill forecast and uncertainty evaluation; (3) user visualization tool including web and mobile interface with visualization of geospatial information by means of Google Maps. Service-oriented approach plays a key role in the WITOIL DSS development. The system meets the real-time requirements in terms of performance and in dynamic service delivery. Client part of WITOIL is presented by a 8-language GUI (Graphical User Interface) supplied with a great variety of user services including a video tutorial (https://www.youtube.com/watch?v=qj_GokYy8MU). GUI allows users to configure and activate the system, visualize the results using Google Maps, and save them afterwards. Not only does a new generation of DSS require the oil spill forecast, but it also needs the evaluation of uncertainty, which is critical for efficient response, recovery, and mitigation. Uncertainty in prediction of the oil transport and transformation stems from the uncertain environment and data-sparse. A new methodology of uncertainty calculation with respect to initial conditions is incorporated in WITOIL DSS. The results are presented in probability terms. Special application to Android has been implemented to support users involved in the field operations. The system is developed as a part of TESSA Project portfolio providing the unified access to others services. Thus, SEACONDITIONS (http://www.sea-conditions.com) performs visualization and on-line delivery of forecast of surface currents, sea surface temperature, significant wave height and direction, wave period and direction; air temperature, surface pressure, precipitation, cloud coverage, wind speed, etc. Apart from the basin scale visualization SEACONDITIONS supports the zooming capability. User feedback reports from fishermen, port authorities including Coast Guard, offshore companies, aquatic and coastal tourism managers, and academia have been collected and used for the system improvements. User-friendliness of GUI, tooltips, an opportunity to vary the advanced parameters, efficiency of the visualization tool, and a help section were appreciated in these reports. In accordance with the users' requirements, a to-do list is composed for the further development of WITOIL. This work was performed in the framework of the TESSA Project (Sviluppo di TEcnologie per la Situational Sea Awareness) supported by PON (Ricerca & Competitività 2007-2013) cofunded by UE (Fondo Europeo di sviluppo regionale), MIUR (Ministero Italiano dell'Università e della Ricerca), and MSE (Ministero dello Sviluppo Economico). References De Dominicis, M., Pinardi, N., Zodiatis, G., and Lardner, R., 2013. MEDSLIK-II, a Lagrangian marine surface oil spill model for short term forecasting - Part 1: Theory. Geosci. Model Dev. 6, 1851-1869.

City scale pollen concentration variability

NASA Astrophysics Data System (ADS)

van der Molen, Michiel; van Vliet, Arnold; Krol, Maarten

2016-04-01

Pollen are emitted in the atmosphere both in the country-side and in cities. Yet the majority of the population is exposed to pollen in cities. Allergic reactions may be induced by short-term exposure to pollen. This raises the question how variable pollen concentration in cities are in temporally and spatially, and how much of the pollen in cities are actually produced in the urban region itself. We built a high resolution (1 × 1 km) pollen dispersion model based on WRF-Chem to study a city's pollen budget and the spatial and temporal variability in concentration. It shows that the concentrations are highly variable, as a result of source distribution, wind direction and boundary layer mixing, as well as the release rate as a function of temperature, turbulence intensity and humidity. Hay Fever Forecasts based on such high resolution emission and physical dispersion modelling surpass traditional hay fever warning methods based on temperature sum methods. The model gives new insights in concentration variability, personal and community level exposure and prevention. The model will be developped into a new forecast tool to serve allergic people to minimize their exposure and reduce nuisance, coast of medication and sick leave. This is an innovative approach in hay fever warning systems.

Effects of recent energy system changes on CO2 projections for the United States.

PubMed

Lenox, Carol S; Loughlin, Daniel H

2017-09-21

Recent projections of future United States carbon dioxide (CO 2 ) emissions are considerably lower than projections made just a decade ago. A myriad of factors have contributed to lower forecasts, including reductions in end-use energy service demands, improvements in energy efficiency, and technological innovations. Policies that have encouraged these changes include renewable portfolio standards, corporate vehicle efficiency standards, smart growth initiatives, revisions to building codes, and air and climate regulations. Understanding the effects of these and other factors can be advantageous as society evaluates opportunities for achieving additional CO 2 reductions. Energy system models provide a means to develop such insights. In this analysis, the MARKet ALlocation (MARKAL) model was applied to estimate the relative effects of various energy system changes that have happened since the year 2005 on CO 2 projections for the year 2025. The results indicate that transformations in the transportation and buildings sectors have played major roles in lowering projections. Particularly influential changes include improved vehicle efficiencies, reductions in projected travel demand, reductions in miscellaneous commercial electricity loads, and higher efficiency lighting. Electric sector changes have also contributed significantly to the lowered forecasts, driven by demand reductions, renewable portfolio standards, and air quality regulations.

Stochastic Analysis and Forecasts of the Patterns of Speed, Acceleration, and Levels of Material Stock Accumulation in Society.

PubMed

Fishman, Tomer; Schandl, Heinz; Tanikawa, Hiroki

2016-04-05

The recent acceleration of urbanization and industrialization of many parts of the developing world, most notably in Asia, has resulted in a fast-increasing demand for and accumulation of construction materials in society. Despite the importance of physical stocks in society, the empirical assessment of total material stock of buildings and infrastructure and reasons for its growth have been underexplored in the sustainability literature. We propose an innovative approach for explaining material stock dynamics in society and create a country typology for stock accumulation trajectories using the ARIMA (Autoregressive Integrated Moving Average) methodology, a stochastic approach commonly used in business studies and economics to inspect and forecast time series. This enables us to create scenarios for future demand and accumulation of building materials in society, including uncertainty estimates. We find that the so-far overlooked aspect of acceleration trends of material stock accumulation holds the key to explaining material stock growth, and that despite tremendous variability in country characteristics, stock accumulation is limited to only four archetypal growth patterns. The ability of nations to change their pattern will be a determining factor for global sustainability.

Innovation versus complexity: what is too much of a good thing?

PubMed

Gottfredson, Mark; Aspinall, Keith

2005-11-01

What's the number of product or service offerings that would optimize both your revenues and your profits? For most firms, it's considerably lower than the number they offer today. The fact is, companies have strong incentives to be overly innovative in new product development. But continual launches of new products and line extensions add complexity throughout a company's operations, and as the costs of managing that complexity multiply, margins shrink. To maximize profit potential, a company needs to identify its innovation fulcrum, the point at which an additional offering destroys more value than it creates. The usual antidotes to complexity miss their mark because they treat the problem on the factory floor rather than at its source: in the product line. Mark Gottfredson and Keith Aspinall of Bain & Company present an approach that goes beyond the typical Six Sigma or lean-operations program to root out complexity hidden in the value chain. The first step is to ask, What would our company look like if it made and sold only a single product or service? In other words, you identify your company's equivalent of Henry Ford's one-size-fits-all Model T-for Starbucks, it might be a medium-size cup of coffee; for a bank, a simple checking account-and then determine the cost of producing that baseline offering. Next, you add variety back into the business system, product by product, and carefully forecast the resulting impact on sales as well as the cost implications across the value chain. When the analysis shows the costs beginning to overwhelm the added revenues, you've found your innovation fulcrum. By deconstructing their companies to a zero-complexity baseline, managers can break through organizational resistance and deeply entrenched ways of thinking to find the right balance between innovation and complexity.

Multi-Dimensional Impact of the Public-Private Center for Translational Molecular Medicine (CTMM) in the Netherlands: Understanding New 21(st) Century Institutional Designs to Support Innovation-in-Society.

PubMed

Steuten, Lotte M

2016-05-01

Knowledge translation is at the epicenter of 21st century life sciences and integrative biology. Several innovative institutional designs have been formulated to cultivate knowledge translation. One of these organizational innovations has been the Center for Translational Molecular Medicine (CTMM), a multi-million public-private partnership in the Netherlands. The CTMM aims to accelerate molecular diagnostics and imaging technologies to forecast disease susceptibilities in healthy populations and early diagnosis and personalized treatment of patients. This research evaluated CTMM's impact on scientific, translational, clinical, and economic dimensions. A pragmatic, operationally-defined process indicators approach was used. Data were gathered from CTMM administrations, through a CTMM-wide survey (n = 167) and group interviews. We found that the CTMM focused on disease areas with high human, clinical, and economic burden to society (i.e., oncology, cardiovascular, neurologic, infection, and immunity diseases). CTMM displayed a robust scientific impact that rests 15%-80% above international reference values regarding publication volume and impact. Technology translation to the clinic was accelerated, with >50% of projects progressing from pre-clinical development to clinical testing within 5 years. Furthermore, CTMM has generated nearly 1500 Full Time Equivalent (FTE) of translational R&D capacity. Its positive impact on translational, (future) clinical, and economic aspects is recognized across all surveyed stakeholders. As organizational innovation is increasingly considered critical to forge linkages between life sciences discoveries and innovation-in-society, lessons learned from this study may inform other institutions with similar objectives such as the Clinical and Translational Science Awards (CTSA) Program of the National Institutes of Health (NIH) in the United States.

Traffic Flow Management Wrap-Up

NASA Technical Reports Server (NTRS)

Grabbe, Shon

2011-01-01

Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.

Design of Epidemia - an Ecohealth Informatics System for Integrated Forecasting of Malaria Epidemics

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Bayabil, E.; Beyane, B.; Bishaw, M.; Henebry, G. M.; Lemma, A.; Liu, Y.; Merkord, C. L.; Mihretie, A.; Senay, G. B.; Yalew, W.

2014-12-01

Early warning of the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. In response to this need, we are developing the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. The system incorporates software for capturing, processing, and integrating environmental and epidemiological data from multiple sources; data assimilation techniques that continually update models and forecasts; and a web-based interface that makes the resulting information available to public health decision makers. This technology will enable forecasts based on lagged responses to environmental risk factors as well as information about recent trends in malaria cases. Environmental driving variables will include a variety of remote-sensed hydrological indicators. EPIDEMIA will be implemented and tested in the Amhara Region of Ethiopia in collaboration with local stakeholders. We conducted an initial co-design workshop in July 2014 that included environmental scientists, software engineers, and participants from the NGO, academic, and public health sectors in Ethiopia. A prototype of the EPIDEMIA web interface was presented and a requirements analysis was conducted to characterize the main use cases for the public health community, identify the critical data requirements for malaria risk modeling, and develop of a list of baseline features for the public health interface. Several critical system features were identified, including a secure web-based interface for uploading and validating surveillance data; a flexible query system to allow retrieval of environmental and epidemiological data summaries as tables, charts, and maps; and an alert system to provide automatic warnings in response to environmental and epidemiological risk factors for malaria. Future system development will involve a cycle of implementation, training, usability testing, and upgrading. This innovative translational bioinformatics approach will allow us to assess the practical effectiveness of these tools as we continually improve the technologies.

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

Community Coordinated Modeling Center (CCMC): Using innovative tools and services to support worldwide space weather scientific communities and networks

NASA Astrophysics Data System (ADS)

Mendoza, A. M.; Bakshi, S.; Berrios, D.; Chulaki, A.; Evans, R. M.; Kuznetsova, M. M.; Lee, H.; MacNeice, P. J.; Maddox, M. M.; Mays, M. L.; Mullinix, R. E.; Ngwira, C. M.; Patel, K.; Pulkkinen, A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.

2012-12-01

Community Coordinated Modeling Center (CCMC) was established to enhance basic solar terrestrial research and to aid in the development of models for specifying and forecasting conditions in the space environment. In achieving this goal, CCMC has developed and provides a set of innovative tools varying from: Integrated Space Weather Analysis (iSWA) web -based dissemination system for space weather information, Runs-On-Request System providing access to unique collection of state-of-the-art solar and space physics models (unmatched anywhere in the world), Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and recently Mobile apps (iPhone/Android) to view space weather data anywhere to the scientific community. The number of runs requested and the number of resulting scientific publications and presentations from the research community has not only been an indication of the broad scientific usage of the CCMC and effective participation by space scientists and researchers, but also guarantees active collaboration and coordination amongst the space weather research community. Arising from the course of CCMC activities, CCMC also supports community-wide model validation challenges and research focus group projects for a broad range of programs such as the multi-agency National Space Weather Program, NSF's CEDAR (Coupling, Energetics and Dynamics of Atmospheric Regions), GEM (Geospace Environment Modeling) and Shine (Solar Heliospheric and INterplanetary Environment) programs. In addition to performing research and model development, CCMC also supports space science education by hosting summer students through local universities; through the provision of simulations in support of classroom programs such as Heliophysics Summer School (with student research contest) and CCMC Workshops; training next generation of junior scientists in space weather forecasting; and educating the general public about the importance and impacts of space weather effects. Although CCMC is organizationally comprised of United States federal agencies, CCMC services are open to members of the international science community and encourages interagency and international collaboration. In this poster, we provide an overview of using Community Coordinated Modeling Center (CCMC) tools and services to support worldwide space weather scientific communities and networks.;

Design/build vs traditional construction user delay modeling : an evaluation of the cost effectiveness of innovative construction methods for new construction. Part 2 : VISUM Online for Salt Lake, Davis, and Utah Counties

DOT National Transportation Integrated Search

2007-05-01

VISUM Online is a traffic management system for processing online traffic data. The system implements both a road network model and a traffic demand model. VISUM Online uses all available real-time and historic data to calculate current and forecaste...

Futuristics and the Role of the Responsible Leader in Providing Better Schools for Less Money: A Look into the Future.

ERIC Educational Resources Information Center

Alvir, Howard P.

Forecast of a 1986 program of educational innovation involving education stamps and student bank accounts is presented. The objectives of the program are to provide more efficient, less expensive, and more equal education. The author maintains that these objectives will be met by allowing average citizens to spend money on an individual basis in…

The Texas Children's Hospital immunization forecaster: conceptualization to implementation.

PubMed

Cunningham, Rachel M; Sahni, Leila C; Kerr, G Brady; King, Laura L; Bunker, Nathan A; Boom, Julie A

2014-12-01

Immunization forecasting systems evaluate patient vaccination histories and recommend the dates and vaccines that should be administered. We described the conceptualization, development, implementation, and distribution of a novel immunization forecaster, the Texas Children's Hospital (TCH) Forecaster. In 2007, TCH convened an internal expert team that included a pediatrician, immunization nurse, software engineer, and immunization subject matter experts to develop the TCH Forecaster. Our team developed the design of the model, wrote the software, populated the Excel tables, integrated the software, and tested the Forecaster. We created a table of rules that contained each vaccine's recommendations, minimum ages and intervals, and contraindications, which served as the basis for the TCH Forecaster. We created 15 vaccine tables that incorporated 79 unique dose states and 84 vaccine types to operationalize the entire United States recommended immunization schedule. The TCH Forecaster was implemented throughout the TCH system, the Indian Health Service, and the Virginia Department of Health. The TCH Forecast Tester is currently being used nationally. Immunization forecasting systems might positively affect adherence to vaccine recommendations. Efforts to support health care provider utilization of immunization forecasting systems and to evaluate their impact on patient care are needed.

THE NEW ENGLAND AIR QUALITY FORECASTING PILOT PROGRAM: DEVELOPMENT OF AN EVALUATION PROTOCOL AND PERFORMANCE BENCHMARK

EPA Science Inventory

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implemen...

Global operational hydrological forecasts through eWaterCycle

NASA Astrophysics Data System (ADS)

van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

2015-04-01

Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and parameterizations. This allows for very detailed simulations at hectare to meter scales, where and when this is needed. At EGU 2015, the operational global eWaterCycle model will be presented for the first time, including forecasts at high resolution, the innovative data assimilation approach, and on-demand coupling with hydraulic models.

Integrating interannual climate variability forecasts into weather-indexed crop insurance. The case of Malawi, Kenya and Tanzania

NASA Astrophysics Data System (ADS)

Vicarelli, M.; Giannini, A.; Osgood, D.

2009-12-01

In this study we explore the potential for re-insurance schemes built on regional climatic forecasts. We focus on micro-insurance contracts indexed on precipitation in 9 villages in Kenya, Tanzania (Eastern Africa) and Malawi (Southern Africa), and analyze the precipitation patterns and payouts resulting from El Niño Southern Oscillation (ENSO). The inability to manage future climate risk represents a “poverty trap” for several African regions. Weather shocks can potentially destabilize not only household, but also entire countries. Governments in drought-prone countries, donors and relief agencies are becoming aware of the importance to develop an ex-ante risk management framework for weather risk. Joint efforts to develop innovative mechanisms to spread and pool risk such as microinsurance and microcredit are currently being designed in several developing countries. While ENSO is an important component in modulating the rainfall regime in tropical Africa, the micro-insurance experiments currently under development to address drought risk among smallholder farmers in this region do not take into account ENSO monitoring or forecasting yet. ENSO forecasts could be integrated in the contracts and reinsurance schemes could be designed at the continental scale taking advantage of the different impact of ENSO on different regions. ENSO is associated to a bipolar precipitation pattern in Southern and Eastern Africa. La Niña years (i.e. Cold ENSO Episodes) are characterized by dry climate in Eastern Africa and wet climate in Southern Africa. During El Niño (or Warm Episode) the precipitation dipole is inverted, and Eastern Africa experiences increased probability for above normal rainfall (Halpert and Ropelewski, 1992, Journal of Climate). Our study represents the first exercise in trying to include ENSO forecasts in micro weather index insurance contract design. We analyzed the contracts payouts with respect to climate variability. In particular (i) we simulated possible payouts using historical precipitation data and analyzed the differences between years with different ENSO states from 1961 to 2005; (ii) we applied Monte Carlo methods to simulate precipitation distributions in each location and calculated the mean and variance of payouts associated to different ENSO states. The results obtained from historical precipitation data indicate that more abundant rainfall reduces payouts and the risk of loan default during La Niña in southern Kenya and Malawi, during El Niño in Tanzania. The results of the Monte Carlo simulations confirm our findings. Our results suggest that re-insurance schemes could be successfully designed to exploit the anti-correlation patterns related to interannual climate variability for different regions in Africa. Moreover, the exploratory framework presented can potentially be refined applied to other regions (e.g. Central and Latin America).

Dual-Polarization Observations of Precipitation: State of the Art in Operational and Research Applications

NASA Astrophysics Data System (ADS)

Chandra, C. V.; Moisseev, D. N.; Baldini, L.; Bechini, R.; Cremonini, R.; Wolff, D. B.; Petersen, W. A.; Junyent, F.; Chen, H.; Beauchamp, R.

2016-12-01

Dual-polarization weather radars have been widely used for rainfall measurement applications and studies of the microphysical characteristics of precipitation. Ground-based, dual-polarization radar systems form the cornerstones of national severe weather warning and forecasting infrastructure in many developed countries. As a result of the improved performance of dual-polarization radars for these applications, large scale dual-polarization upgrades are being planned for India and China. In addition to national forecast and warning operations, dual-polarization radars have also been used for satellite ground validation activities. The operational Dual-Polarization radars in the US are mostly S band systems whereas in Europe are mostly C band systems. In addition a third class of systems is emerging in urban regions where networks of X band systems are being deployed operationally. There are successful networks planned or already deployed in big cities such as Dallas Fort Worth, Tokyo or Beijing. These X band networks are developing their own operational domain. In summary a large infrastructure in terms of user specified products and dual use of operational research applications are also emerging in these systems. This paper will discuss some of the innovative uses of the operational dual-polarization radar networks for research purposes, with references to calibration, hydrometeor classification and quantitative precipitation estimation. Additional application to the study of precipitation processes will also be discussed.

Pharmaceutical expenditure forecast model to support health policy decision making.

PubMed

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

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of generics had a major impact on savings. However, very aggressive pricing of generic and biosimilar products might make this market unattractive and can be counterproductive. Worth noting, delaying time to access innovative products was also identified as an effective leverage to increase savings but might not be a desirable policy for breakthrough products. Increasing patient financial contributions, either directly or indirectly via their private insurances, is a more likely scenario rather than expanding the national pharmaceutical expenditure coverage.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Visualization and Nowcasting for Aviation using online verified ensemble weather radar extrapolation.

NASA Astrophysics Data System (ADS)

Kaltenboeck, Rudolf; Kerschbaum, Markus; Hennermann, Karin; Mayer, Stefan

2013-04-01

Nowcasting of precipitation events, especially thunderstorm events or winter storms, has high impact on flight safety and efficiency for air traffic management. Future strategic planning by air traffic control will result in circumnavigation of potential hazardous areas, reduction of load around efficiency hot spots by offering alternatives, increase of handling capacity, anticipation of avoidance manoeuvres and increase of awareness before dangerous areas are entered by aircraft. To facilitate this rapid update forecasts of location, intensity, size, movement and development of local storms are necessary. Weather radar data deliver precipitation analysis of high temporal and spatial resolution close to real time by using clever scanning strategies. These data are the basis to generate rapid update forecasts in a time frame up to 2 hours and more for applications in aviation meteorological service provision, such as optimizing safety and economic impact in the context of sub-scale phenomena. On the basis of tracking radar echoes by correlation the movement vectors of successive weather radar images are calculated. For every new successive radar image a set of ensemble precipitation fields is collected by using different parameter sets like pattern match size, different time steps, filter methods and an implementation of history of tracking vectors and plausibility checks. This method considers the uncertainty in rain field displacement and different scales in time and space. By validating manually a set of case studies, the best verification method and skill score is defined and implemented into an online-verification scheme which calculates the optimized forecasts for different time steps and different areas by using different extrapolation ensemble members. To get information about the quality and reliability of the extrapolation process additional information of data quality (e.g. shielding in Alpine areas) is extrapolated and combined with an extrapolation-quality-index. Subsequently the probability and quality information of the forecast ensemble is available and flexible blending to numerical prediction model for each subarea is possible. Simultaneously with automatic processing the ensemble nowcasting product is visualized in a new innovative way which combines the intensity, probability and quality information for different subareas in one forecast image.

A Japanese New Altimetry Mission, COMPIRA - Towards High Temporal and Spatial Sampling of Sea Surface Height Measurement

NASA Astrophysics Data System (ADS)

Ito, N.; Uematsu, A.; Yajima, Y.; Isoguchi, O.

2014-12-01

Japan Aerospace Exploration Agency (JAXA) is working on a conceptual study of altimeter mission named Coastal and Ocean measurement Mission with Precise and Innovative Radar Altimeter (COMPIRA), which will carry a wide-swath altimeter named Synthetic aperture radar (SAR) Height Imaging Oceanic Sensor with Advanced Interferometry (SHIOSAI). Capturing meso/submeso-scale phenomena is one of important objectives of the COMPIRA mission, as well as operational oceanography and fishery. For operational oceanography including coastal forecast, swath of SHIOSAI is selected to be 80 km in left and right sides to maximize temporal and spatial sampling of the sea surface height. Orbit specifications are also designed to be better sampling especially for mid-latitude region. That is, a spatial grid sampling is 5 km and an observation times per revisit period (about 10 days) is 2 to 3 times. In order to meet both sampling frequency and spatial coverage requirements as much as possible, orbit inclination was set relatively low, 51 degrees. Although this sampling frequency is, of course, not enough high to capture time evolution of coastal phenomena, an assimilation process would compensate its time evolution if 2D SSH fields was observed at least once within decal time scale of phenomena. JAXA has launched a framework called "Coastal forecast core team" to aim at developing coastal forecast system through pre-launch activities toward COMPIRA. Assimilation segment as well as satellite and in situ data provision will play an important role on these activities. As a first step, we evaluated effects of ocean current forecast improvement with COMPIRA-simulated wide-swath and high sampling sea surface heights (SSH) data. Simulated SSH data are generated from regional ocean numerical models and the COMPIRA orbit and error specifications. Then, identical twin experiments are conducted to investigate the effect of wide-swath SSH measurements on coastal forecast in the Tohoku Pacific coast region. The experiment shows that simulated sea surface current using COMPIRA data as an input data for assimilation well represents vortical feature, which cannot be reproduced by conventional nadir altimeters.

Real-time Social Internet Data to Guide Forecasting Models

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

Del Valle, Sara Y.

Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematicalmore » approaches and heterogeneous data streams.« less

NWS Operational Requirements for Ensemble-Based Hydrologic Forecasts

NASA Astrophysics Data System (ADS)

Hartman, R. K.

2008-12-01

Ensemble-based hydrologic forecasts have been developed and issued by National Weather Service (NWS) staff at River Forecast Centers (RFCs) for many years. Used principally for long-range water supply forecasts, only the uncertainty associated with weather and climate have been traditionally considered. As technology and societal expectations of resource managers increase, the use and desire for risk-based decision support tools has also increased. These tools require forecast information that includes reliable uncertainty estimates across all time and space domains. The development of reliable uncertainty estimates associated with hydrologic forecasts is being actively pursued within the United States and internationally. This presentation will describe the challenges, components, and requirements for operational hydrologic ensemble-based forecasts from the perspective of a NOAA/NWS River Forecast Center.

Forecasts of land uses

Treesearch

David N. Wear

2013-01-01

Key FindingsBetween 30 million and 43 million acres of land in the South are forecasted to be developed for urban uses by 2060 from a base of 30 million acres in 1997. These forecasts are based on a continuation of historical development intensities.From 1997 to 2060, the South is forecasted to lose between 11 million acres (7...

Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations

NASA Astrophysics Data System (ADS)

Trudel, Mélanie; Leconte, Robert; Paniconi, Claudio

2014-06-01

Data assimilation techniques not only enhance model simulations and forecast, they also provide the opportunity to obtain a diagnostic of both the model and observations used in the assimilation process. In this research, an ensemble Kalman filter was used to assimilate streamflow observations at a basin outlet and at interior locations, as well as soil moisture at two different depths (15 and 45 cm). The simulation model is the distributed physically-based hydrological model CATHY (CATchment HYdrology) and the study site is the Des Anglais watershed, a 690 km2 river basin located in southern Quebec, Canada. Use of Latin hypercube sampling instead of a conventional Monte Carlo method to generate the ensemble reduced the size of the ensemble, and therefore the calculation time. Different post-assimilation diagnostics, based on innovations (observation minus background), analysis residuals (observation minus analysis), and analysis increments (analysis minus background), were used to evaluate assimilation optimality. An important issue in data assimilation is the estimation of error covariance matrices. These diagnostics were also used in a calibration exercise to determine the standard deviation of model parameters, forcing data, and observations that led to optimal assimilations. The analysis of innovations showed a lag between the model forecast and the observation during rainfall events. Assimilation of streamflow observations corrected this discrepancy. Assimilation of outlet streamflow observations improved the Nash-Sutcliffe efficiencies (NSE) between the model forecast (one day) and the observation at both outlet and interior point locations, owing to the structure of the state vector used. However, assimilation of streamflow observations systematically increased the simulated soil moisture values.

The option value of innovative treatments for non-small cell lung cancer and renal cell carcinoma.

PubMed

Thornton Snider, Julia; Batt, Katharine; Wu, Yanyu; Tebeka, Mahlet Gizaw; Seabury, Seth

2017-10-01

To develop a model of the option value a therapy provides by enabling patients to live to see subsequent innovations and to apply the model to the case of nivolumab in renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC). A model of the option value of nivolumab in RCC and NSCLC was developed and estimated. Data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry and published clinical trial results were used to estimate survival curves for metastatic cancer patients with RCC, squamous NSCLC, or nonsquamous NSCLC. To estimate the conventional value of nivolumab, survival with the pre-nivolumab standard of care was compared with survival with nivolumab assuming no future innovation. To estimate the option value of nivolumab, long-term survival trends in RCC and squamous and nonsquamous NSCLC were measured in SEER to forecast mortality improvements that nivolumab patients may live to see. Compared with the previous standard of care, nivolumab extended life expectancy by 6.3 months in RCC, 7.5 months in squamous NSCLC, and 4.5 months in nonsquamous NSCLC, according to conventional methods. Accounting for expected future mortality trends, nivolumab patients are likely to gain an additional 1.2 months in RCC, 0.4 months in squamous NSCLC, and 0.5 months in nonsquamous NSCLC. These option values correspond to 18%, 5%, and 10% of the conventional value of nivolumab, respectively. Option value is important when valuing therapies like nivolumab that extend life in a rapidly evolving area of care.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

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

KNMI DataLab experiences in serving data-driven innovations

NASA Astrophysics Data System (ADS)

Noteboom, Jan Willem; Sluiter, Raymond

2016-04-01

Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data.

PubMed

Furquim, Gustavo; Filho, Geraldo P R; Jalali, Roozbeh; Pessin, Gustavo; Pazzi, Richard W; Ueyama, Jó

2018-03-19

The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN.

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

PubMed Central

Furquim, Gustavo; Filho, Geraldo P. R.; Pessin, Gustavo; Pazzi, Richard W.

2018-01-01

The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN. PMID:29562657

The real-time SEP forecasting tools of the 'HESPERIA' HORIZON 2020 project

NASA Astrophysics Data System (ADS)

Malandraki, Olga E.; Nunez, Marlon; Heber, Bernd; Labrenz, Johannes; Posner, Arik; Milas, Nick; Tsiropoula, Georgia; Pavlos, Evgenios; Sarlanis, Christos

2017-04-01

In this study, we describe the two real-time prediction tools, that have been developed in the framework of the HESPERIA project based upon the proven concepts UMASEP and REleASE. A major impact on human and robotic space exploration activities is the sudden and prompt occurrence of solar energetic ion events. The fact that near-relativistic electrons (1 MeV electrons have 95% of the speed of light) travel faster than ions (30 MeV protons have 25% of the speed of light) and are always present in Solar Energetic Particle (SEP) events can be used to forecast the arrival of protons from SEP events with real-time measurements of near relativistic electrons. The faster electrons arrive at L1 30 to 90 minutes before the slower protons. The Relativistic Electron Alert System for Exploration (REleASE) forecasting scheme (Posner, 2007) uses this effect to predict the proton flux by utilizing the actual electron flux and the increase of the electron flux in the last 60 minutes. In the framework of the HESPERIA project, a clone of the REleASE system was built in the open source programming language PYTHON. The same forecasting principle with use of the same forecasting matrices were in addition adapted to real-time electron flux measurements from the Electron, Proton & Alpha Monitor (EPAM) onboard the Advanced Composition Explorer (ACE). It is shown, that the REleASE forecasting scheme can be adapted to work with any near relativistic electron flux measurements. Solar energetic particles (SEPs) are sometimes energetic enough and the flux is high enough to cause air showers in the stratosphere and in the troposphere, which are an important ionization source in the atmosphere. >500 MeV solar protons are so energetic that they usually have effects on the ground, producing what is called a Ground Level Enhancement (GLE) event. Within the HESPERIA project a predictor of >500 SEP proton events at the near-earth (e.g. at geostationary orbit) has been developed. In order to predict these events, the UMASEP scheme (Núñez, 2011, 2015) has been used. UMASEP makes a lag-correlation of solar electromagnetic (EM) flux with the particle flux at near-earth. If the correlation is high, the model infers that there is a magnetic connection through which particles are arriving. If, additionally, the intensity of the flux of the associated solar event is also high, then the UMASEP scheme issues a SEP prediction. In the case of the prediction of >500 MeV SEP events, the implemented system, called UMASEP-500, correlates X-ray flux with each of the differential proton fluxes measured by the GOES satellites, and with each of the neutron density fluxes collected by neutron monitor stations around the world. When the correlation estimation surpasses a threshold, and the associated flare is greater than a specific X-ray peak flux, a >500 MeV SEP forecast is issued. Both forecasting tools are operational under the HESPERIA server maintained at the National Observatory of Athens. Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637324 (HESPERIA project).

Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems

PubMed Central

Liu, Yi; Hu, Jiameng; Snell-Feikema, Isaiah; VanBemmel, Michael S.; Lamsal, Aashis; Wimberly, Michael C.

2015-01-01

Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands. PMID:26644779

Illustrating anticipatory life cycle assessment for emerging photovoltaic technologies.

PubMed

Wender, Ben A; Foley, Rider W; Prado-Lopez, Valentina; Ravikumar, Dwarakanath; Eisenberg, Daniel A; Hottle, Troy A; Sadowski, Jathan; Flanagan, William P; Fisher, Angela; Laurin, Lise; Bates, Matthew E; Linkov, Igor; Seager, Thomas P; Fraser, Matthew P; Guston, David H

2014-09-16

Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.

Assimilation of AATSR, MERIS and MODIS Data in the Snowmelt Runoff Model (SRM) on the Upper Rio Grande (USA)

NASA Astrophysics Data System (ADS)

Bleiweiss, M. P.; Rampini, A.; Pepe, M.; Rango, A.; Steele, C.; Stein, W. L.; Schmugge, T.

2008-12-01

Current efforts for simulating or forecasting snowmelt are time-consuming and laborious; the AWARE project (A tool for monitoring and forecasting Available WAter REsource in mountain environments) has been motivated by the urgent need to facilitate the prediction of medium-term flows from snowmelt for an effective and sustainable water resources management. Its main goal is to provide innovative tools for monitoring and predicting water availability and distribution in drainage basins where snowmelt is a major component of the annual water balance. The particular objective of the effort reported here is to compare results obtained from the MODIS sensor on NASA Terra and Aqua satellite and next generation sensors AATSR and MERIS on board ESA Envisat satellite. The vehicle for this comparison is the AWARE Geoportal (http://www.aware- eu.info/eng/home.htm) which is a WWW implementation of the Snowmelt Runoff Model (SRM). The river basin chosen for analysis is the Upper Rio Grande of North America. The time period for analysis encompasses the Water Years 2005, 2006, and 2007 (October 2004 - September 2007). The reason for this is to ensure that data from all three sensors are available for use and to investigate variable climate conditions. A successful comparison between the various sensors will help demonstrate that the AWARE approach will facilitate future processing of several years' worth of snow cover data from a variety of sensors that covers large extremes in climate variability. This will allow greater success in developing forecasts and understanding of longer term climate change impacts.

Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

NASA Technical Reports Server (NTRS)

Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson

2013-01-01

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

Probability fire weather forecasts .. show promise in 3-year trial

Treesearch

Paul G. Scowcroft

1970-01-01

Probability fire weather forecasts were compared with categorical and climatological forecasts in a trial in southern California during the 1965-1967 fire seasons. Equations were developed to express the reliability of forecasts and degree of skill shown by the forecaster. Evaluation of 336 daily reports suggests that probability forecasts were more reliable. For...

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

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

Hodge, Brian S; Feng, Cong; Cui, Mingjian

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less

Load Forecasting of Central Urban Area Power Grid Based on Saturated Load Density Index

NASA Astrophysics Data System (ADS)

Huping, Yang; Chengyi, Tang; Meng, Yu

2018-03-01

In the current society, coordination between urban power grid development and city development has become more and more prominent. Electricity saturated load forecasting plays an important role in the planning and development of power grids. Electricity saturated load forecasting is a new concept put forward by China in recent years in the field of grid planning. Urban saturation load forecast is different from the traditional load forecasting method for specific years, the time span of it often relatively large, and involves a wide range of aspects. This study takes a county in eastern Jiangxi as an example, this paper chooses a variety of load forecasting methods to carry on the recent load forecasting calculation to central urban area. At the same time, this paper uses load density index method to predict the Longterm load forecasting of electric saturation load of central urban area lasted until 2030. And further study shows the general distribution of the urban saturation load in space.

Technology Alignment and Portfolio Prioritization (TAPP): Advanced Methods in Strategic Analysis, Technology Forecasting and Long Term Planning for Human Exploration and Operations, Advanced Exploration Systems and Advanced Concepts

NASA Technical Reports Server (NTRS)

Funaro, Gregory V.; Alexander, Reginald A.

2015-01-01

The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision­-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi­-criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.

Remote sensing of atmospheric particulates: Technological innovation and physical limitations in applications to short-range weather prediction

NASA Technical Reports Server (NTRS)

Curran, R. J.; Kropfil, R.; Hallett, J.

1984-01-01

Techniques for remote sensing of particles, from cloud droplet to hailstone size, using optical and microwave frequencies are reviewed. The inherent variability of atmospheric particulates is examined to delineate conditions when the signal can give information to be effectively utilized in a forecasting context. The physical limitations resulting from the phase, size, orientation and concentration variability of the particulates are assessed.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

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

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

A framework for probabilistic pluvial flood nowcasting for urban areas

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Murla, Damian; Wang, Lipen; Foresti, Loris; Reyniers, Maarten; Delobbe, Laurent; Van Herk, Kristine; Van Ootegem, Luc; Willems, Patrick

2016-04-01

Pluvial flood nowcasting is gaining ground not least because of the advancements in rainfall forecasting schemes. Short-term forecasts and applications have benefited from the availability of such forecasts with high resolution in space (~1km) and time (~5min). In this regard, it is vital to evaluate the potential of nowcasting products for urban inundation applications. One of the most advanced Quantitative Precipitation Forecasting (QPF) techniques is the Short-Term Ensemble Prediction System, which was originally co-developed by the UK Met Office and Australian Bureau of Meteorology. The scheme was further tuned to better estimate extreme and moderate events for the Belgian area (STEPS-BE). Against this backdrop, a probabilistic framework has been developed that consists of: (1) rainfall nowcasts; (2) sewer hydraulic model; (3) flood damage estimation; and (4) urban inundation risk mapping. STEPS-BE forecasts are provided at high resolution (1km/5min) with 20 ensemble members with a lead time of up to 2 hours using a 4 C-band radar composite as input. Forecasts' verification was performed over the cities of Leuven and Ghent and biases were found to be small. The hydraulic model consists of the 1D sewer network and an innovative 'nested' 2D surface model to model 2D urban surface inundations at high resolution. The surface components are categorized into three groups and each group is modelled using triangular meshes at different resolutions; these include streets (3.75 - 15 m2), high flood hazard areas (12.5 - 50 m2) and low flood hazard areas (75 - 300 m2). Functions describing urban flood damage and social consequences were empirically derived based on questionnaires to people in the region that were recently affected by sewer floods. Probabilistic urban flood risk maps were prepared based on spatial interpolation techniques of flood inundation. The method has been implemented and tested for the villages Oostakker and Sint-Amandsberg, which are part of the larger city of Gent, Belgium. After each of the different above-mentioned components were evaluated, they were combined and tested for recent historical flood events. The rainfall nowcasting, hydraulic sewer and 2D inundation modelling and socio-economical flood risk results each could be partly evaluated: the rainfall nowcasting results based on radar data and rain gauges; the hydraulic sewer model results based on water level and discharge data at pumping stations; the 2D inundation modelling results based on limited data on some recent flood locations and inundation depths; the results for the socio-economical flood consequences of the most extreme events based on claims in the database of the national disaster agency. Different methods for visualization of the probabilistic inundation results are proposed and tested.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Verification of space weather forecasts at the UK Met Office

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

A GLM Post-processor to Adjust Ensemble Forecast Traces

NASA Astrophysics Data System (ADS)

Thiemann, M.; Day, G. N.; Schaake, J. C.; Draijer, S.; Wang, L.

2011-12-01

The skill of hydrologic ensemble forecasts has improved in the last years through a better understanding of climate variability, better climate forecasts and new data assimilation techniques. Having been extensively utilized for probabilistic water supply forecasting, interest is developing to utilize these forecasts in operational decision making. Hydrologic ensemble forecast members typically have inherent biases in flow timing and volume caused by (1) structural errors in the models used, (2) systematic errors in the data used to calibrate those models, (3) uncertain initial hydrologic conditions, and (4) uncertainties in the forcing datasets. Furthermore, hydrologic models have often not been developed for operational decision points and ensemble forecasts are thus not always available where needed. A statistical post-processor can be used to address these issues. The post-processor should (1) correct for systematic biases in flow timing and volume, (2) preserve the skill of the available raw forecasts, (3) preserve spatial and temporal correlation as well as the uncertainty in the forecasted flow data, (4) produce adjusted forecast ensembles that represent the variability of the observed hydrograph to be predicted, and (5) preserve individual forecast traces as equally likely. The post-processor should also allow for the translation of available ensemble forecasts to hydrologically similar locations where forecasts are not available. This paper introduces an ensemble post-processor (EPP) developed in support of New York City water supply operations. The EPP employs a general linear model (GLM) to (1) adjust available ensemble forecast traces and (2) create new ensembles for (nearby) locations where only historic flow observations are available. The EPP is calibrated by developing daily and aggregated statistical relationships form historical flow observations and model simulations. These are then used in operation to obtain the conditional probability density function (PDF) of the observations to be predicted, thus jointly adjusting individual ensemble members. These steps are executed in a normalized transformed space ('z'-space) to account for the strong non-linearity in the flow observations involved. A data window centered on each calibration date is used to minimize impacts from sampling errors and data noise. Testing on datasets from California and New York suggests that the EPP can successfully minimize biases in ensemble forecasts, while preserving the raw forecast skill in a 'days to weeks' forecast horizon and reproducing the variability of climatology for 'weeks to years' forecast horizons.

Hurricane Sandy: Caught in the eye of the storm and a city's adaptation response

NASA Astrophysics Data System (ADS)

Orton, P. M.; Horton, R. M.; Blumberg, A. F.; Rosenzweig, C.; Solecki, W.; Bader, D.

2015-12-01

The NOAA RISA program has funded the seven-institution Consortium for Climate Risk in the Urban Northeast (CCRUN) for the past five years to serve stakeholder needs in assessing and managing risks from climate variability and change. When Hurricane Sandy struck, we were in an ideal position, making flood forecasts and communicating NOAA forecasts to the public with dozens of media placements, translating the poorly understood flood forecasts into human dimensions. In 2013 and 2015, by request of New York City (NYC), we worked through the NYC Panel on Climate Change to deliver updated climate risk assessment reports, to be used in the post-Sandy rebuilding and resiliency efforts. These utilized innovative methodologies for probabilistic local and regional sea level change projections, and contrasted methods of dynamic versus (the more common) static flood mapping. We participated in a federal-academic partnership that developed a Sea Level Tool for Sandy Recovery that integrates CCRUN sea level rise projections with policy-relevant FEMA flood maps, and now several updated flood maps and coastal flood mapping tools (NOAA, FEMA, and USACE) incorporate our projections. For the adaptation response, we helped develop NYC's $20 billion flood adaptation plan, and we were on a winning team under the Housing and Urban Development Rebuild By Design (RBD) competition, a few of the many opportunities that arose with negligible additional funding and which CCRUN funds supported. Our work at times disrupted standard lines of thinking, but NYC showed an openness to altering course. In one case we showed that an NYC plan of wetland restoration in Jamaica Bay would provide no reduction in flooding unless deep-dredged channels circumventing them were shallowed or narrowed. In another, the lead author's RBD team challenged the notion at one location that levees were the solution to accelerating sea level rise, developing a plan to use ecological breakwaters and layered components of physical and social resilience. CCRUN has succeeded in winning another five years of RISA funding, and this will enable us to continue our climate risk and adaptation work for the entire Urban Northeast.

Real-time Microseismic Processing for Induced Seismicity Hazard Detection

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

Matzel, Eric M.

Induced seismicity is inherently associated with underground fluid injections. If fluids are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures can trigger dynamic earthquake slip, which could both damage surface structures and create new migration pathways. The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterizationmore » phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.« less

Ecological Forecasting of Vibrio sp. in U.S. Coastal Waters Using an Operational Platform, a Pilot Project of the NOAA Ecological Forecasting Roadmap. Development of Web based Tools and Forecasts to Help the Public Avoid Exposure to Vibrio vulnificus and Shell Fish Harvesters Avoid Dangerous Concentrations of Vibrio parahaemolyticus.

NASA Astrophysics Data System (ADS)

Daniels, R. M.; Jacobs, J. M.; Paranjpye, R.; Lanerolle, L. W.

2016-02-01

The Pathogens group of the NOAA Ecological Forecasting Roadmap has begun a range of efforts to monitor and predict potential pathogen occurrences in shellfish and in U.S. Coastal waters. NOAA/NCOSS along with NMFS/NWFSC have led the Pathogens group and the development of web based tools and forecasts for both Vibrio vulnificus and Vibrio parahaemolyticus. A strong relationship with FDA has allowed the team to develop forecasts that will serve U.S. shellfish harvesters and consumers. NOAA/NOS/CSDL has provided modeling expertise to help the group use the hydrodynamic models and their forecasts of physical variables that drive the ecological predictions. The NOAA/NWS/Ocean Prediction Center has enabled these ecological forecasting efforts by providing the infrastructure, computing knowledge and experience in an operational culture. Daily forecasts have been demonstrated and are available from the web for the Chesapeake Bay, Delaware Bay, Northern Gulf of Mexico, Tampa Bay, Puget Sound and Long Island Sound. The forecast systems run on a daily basis being fed by NOS model data from the NWS/NCEP super computers. New forecast tools including V. parahaemolyticus post harvest growth and doubling time in ambient air temperature will be described.

Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

NASA Astrophysics Data System (ADS)

Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Design and Application of New Low-Cost Instruments for Marine Environmental Research

PubMed Central

Marcelli, Marco; Piermattei, Viviana; Madonia, Alice; Mainardi, Umberto

2014-01-01

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of current oceanographic research. These kinds of devices can be used for several applications, ranging from vertical profilers to stand-alone systems, and can be installed on different platforms (buoys, Voluntary Observing Ships, underwater vehicles, etc.). The availability of low-cost technologies enables the realization of extended observatory networks for the study of marine physical and biological processes through an integrated approach merging in situ observations, forecasting models and remotely sensed data. We present new low-cost sensors and probes developed to measure marine temperature, conductivity, chlorophyll a and Chromophoric Dissolved Organic Matter fluorescence, focusing on sensing strategies, general architecture, laboratory trials, in situ tests and comparison with standard instruments. Furthermore, we report the expendable (New T-FLaP), vertical profiler (T-FLaPpro) and stand-alone (Spectra) applications of these technological developments that were tested during several oceanographic surveys in the Mediterranean Sea. PMID:25490594

Project Assessment Framework through Design (PAFTD) - A Project Assessment Framework in Support of Strategic Decision Making

NASA Technical Reports Server (NTRS)

Depenbrock, Brett T.; Balint, Tibor S.; Sheehy, Jeffrey A.

2014-01-01

Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.

Design and application of new low-cost instruments for marine environmental research.

PubMed

Marcelli, Marco; Piermattei, Viviana; Madonia, Alice; Mainardi, Umberto

2014-12-05

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of current oceanographic research. These kinds of devices can be used for several applications, ranging from vertical profilers to stand-alone systems, and can be installed on different platforms (buoys, Voluntary Observing Ships, underwater vehicles, etc.). The availability of low-cost technologies enables the realization of extended observatory networks for the study of marine physical and biological processes through an integrated approach merging in situ observations, forecasting models and remotely sensed data. We present new low-cost sensors and probes developed to measure marine temperature, conductivity, chlorophyll a and Chromophoric Dissolved Organic Matter fluorescence, focusing on sensing strategies, general architecture, laboratory trials, in situ tests and comparison with standard instruments. Furthermore, we report the expendable (New T-FLaP), vertical profiler (T-FLaPpro) and stand-alone (Spectra) applications of these technological developments that were tested during several oceanographic surveys in the Mediterranean Sea.

Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

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

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.

Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Lambert, WInifred; Roeder, William

2007-01-01

This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Development of Gridded Innovations and Observations Supplement to MERRA-2

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Da Silva, Arlindo M.; Robertson, Franklin R.

2017-01-01

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths can be known, drift in certain instruments and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are in observation-space data formats and have not been made easily available to reanalysis users.A test data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). We refer to this proof-of-concept data as the MERRA-2 Gridded Innovations and Observations (GIO). In this paper, we present the data format and its strengths and limitations with some initial testing and validation of the methodology.

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.

Prospectively Evaluating the Collaboratory for the Study of Earthquake Predictability: An Evaluation of the UCERF2 and Updated Five-Year RELM Forecasts

NASA Astrophysics Data System (ADS)

Strader, Anne; Schneider, Max; Schorlemmer, Danijel; Liukis, Maria

2016-04-01

The Collaboratory for the Study of Earthquake Predictability (CSEP) was developed to rigorously test earthquake forecasts retrospectively and prospectively through reproducible, completely transparent experiments within a controlled environment (Zechar et al., 2010). During 2006-2011, thirteen five-year time-invariant prospective earthquake mainshock forecasts developed by the Regional Earthquake Likelihood Models (RELM) working group were evaluated through the CSEP testing center (Schorlemmer and Gerstenberger, 2007). The number, spatial, and magnitude components of the forecasts were compared to the respective observed seismicity components using a set of consistency tests (Schorlemmer et al., 2007, Zechar et al., 2010). In the initial experiment, all but three forecast models passed every test at the 95% significance level, with all forecasts displaying consistent log-likelihoods (L-test) and magnitude distributions (M-test) with the observed seismicity. In the ten-year RELM experiment update, we reevaluate these earthquake forecasts over an eight-year period from 2008-2016, to determine the consistency of previous likelihood testing results over longer time intervals. Additionally, we test the Uniform California Earthquake Rupture Forecast (UCERF2), developed by the U.S. Geological Survey (USGS), and the earthquake rate model developed by the California Geological Survey (CGS) and the USGS for the National Seismic Hazard Mapping Program (NSHMP) against the RELM forecasts. Both the UCERF2 and NSHMP forecasts pass all consistency tests, though the Helmstetter et al. (2007) and Shen et al. (2007) models exhibit greater information gain per earthquake according to the T- and W- tests (Rhoades et al., 2011). Though all but three RELM forecasts pass the spatial likelihood test (S-test), multiple forecasts fail the M-test due to overprediction of the number of earthquakes during the target period. Though there is no significant difference between the UCERF2 and NSHMP models, residual scores show that the NSHMP model is preferred in locations with earthquake occurrence, due to the lower seismicity rates forecasted by the UCERF2 model.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Real-time projections of cholera outbreaks through data assimilation and rainfall forecasting

NASA Astrophysics Data System (ADS)

Pasetto, Damiano; Finger, Flavio; Rinaldo, Andrea; Bertuzzo, Enrico

2017-10-01

Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions still exact high death tolls mostly due to the unpreparedness of health care infrastructures to face unforeseen emergencies. In this context, mathematical models for the prediction of the evolution of an ongoing outbreak are of paramount importance. Here, we test a real-time forecasting framework that readily integrates new information as soon as available and periodically issues an updated forecast. The spread of cholera is modeled by a spatially-explicit scheme that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. The framework presents two major innovations for cholera modeling: the use of a data assimilation technique, specifically an ensemble Kalman filter, to update both state variables and parameters based on the observations, and the use of rainfall forecasts to force the model. The exercise of simulating the state of the system and the predictive capabilities of the novel tools, set at the initial phase of the 2010 Haitian cholera outbreak using only information that was available at that time, serves as a benchmark. Our results suggest that the assimilation procedure with the sequential update of the parameters outperforms calibration schemes based on Markov chain Monte Carlo. Moreover, in a forecasting mode the model usefully predicts the spatial incidence of cholera at least one month ahead. The performance decreases for longer time horizons yet allowing sufficient time to plan for deployment of medical supplies and staff, and to evaluate alternative strategies of emergency management.

EcoPAD, an interactive platform for near real-time ecological forecasting by assimilating data into model

NASA Astrophysics Data System (ADS)

MA, S.; Huang, Y.; Stacy, M.; Jiang, J.; Sundi, N.; Ricciuto, D. M.; Hanson, P. J.; Luo, Y.; Saruta, V.

2017-12-01

Ecological forecasting is critical in various aspects of our coupled human-nature systems, such as disaster risk reduction, natural resource management and climate change mitigation. Novel advancements are in urgent need to deepen our understandings of ecosystem dynamics, boost the predictive capacity of ecology, and provide timely and effective information for decision-makers in a rapidly changing world. Our study presents a smart system - Ecological Platform for Assimilation of Data (EcoPAD) - which streamlines web request-response, data management, model execution, result storage and visualization. EcoPAD allows users to (i) estimate model parameters or state variables, (ii) quantify uncertainty of estimated parameters and projected states of ecosystems, (iii) evaluate model structures, (iv) assess sampling strategies, (v) conduct ecological forecasting, and (vi) detect ecosystem acclimation to climate change. One of the key innovations of the web-based EcoPAD is the automated near- or real-time forecasting of ecosystem dynamics with uncertainty fully quantified. The user friendly webpage enables non-modelers to explore their data for simulation and data assimilation. As a case study, we applied EcoPAD to the Spruce and Peatland Responses Under Climatic and Environmental Change Experiment (SPRUCE), a whole ecosystem warming and CO2 enrichment treatment project in the northern peatland, assimilated multiple data streams into a process based ecosystem model, enhanced timely feedback between modelers and experimenters, ultimately improved ecosystem forecasting and made better use of current knowledge. Built in a framework with flexible API, EcoPAD is easily portable and will benefit scientific communities, policy makers as well as the general public.

Research-Based Monitoring, Prediction, and Analysis Tools of the Spacecraft Charging Environment for Spacecraft Users

NASA Technical Reports Server (NTRS)

Zheng, Yihua; Kuznetsova, Maria M.; Pulkkinen, Antti A.; Maddox, Marlo M.; Mays, Mona Leila

2015-01-01

The Space Weather Research Center (http://swrc. gsfc.nasa.gov) at NASA Goddard, part of the Community Coordinated Modeling Center (http://ccmc.gsfc.nasa.gov), is committed to providing research-based forecasts and notifications to address NASA's space weather needs, in addition to its critical role in space weather education. It provides a host of services including spacecraft anomaly resolution, historical impact analysis, real-time monitoring and forecasting, tailored space weather alerts and products, and weekly summaries and reports. In this paper, we focus on how (near) real-time data (both in space and on ground), in combination with modeling capabilities and an innovative dissemination system called the integrated Space Weather Analysis system (http://iswa.gsfc.nasa.gov), enable monitoring, analyzing, and predicting the spacecraft charging environment for spacecraft users. Relevant tools and resources are discussed.

Virtual hydrology observatory: an immersive visualization of hydrology modeling

NASA Astrophysics Data System (ADS)

Su, Simon; Cruz-Neira, Carolina; Habib, Emad; Gerndt, Andreas

2009-02-01

The Virtual Hydrology Observatory will provide students with the ability to observe the integrated hydrology simulation with an instructional interface by using a desktop based or immersive virtual reality setup. It is the goal of the virtual hydrology observatory application to facilitate the introduction of field experience and observational skills into hydrology courses through innovative virtual techniques that mimic activities during actual field visits. The simulation part of the application is developed from the integrated atmospheric forecast model: Weather Research and Forecasting (WRF), and the hydrology model: Gridded Surface/Subsurface Hydrologic Analysis (GSSHA). Both the output from WRF and GSSHA models are then used to generate the final visualization components of the Virtual Hydrology Observatory. The various visualization data processing techniques provided by VTK are 2D Delaunay triangulation and data optimization. Once all the visualization components are generated, they are integrated into the simulation data using VRFlowVis and VR Juggler software toolkit. VR Juggler is used primarily to provide the Virtual Hydrology Observatory application with fully immersive and real time 3D interaction experience; while VRFlowVis provides the integration framework for the hydrologic simulation data, graphical objects and user interaction. A six-sided CAVETM like system is used to run the Virtual Hydrology Observatory to provide the students with a fully immersive experience.

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

NASA Technical Reports Server (NTRS)

Rohloff, Kurt

2010-01-01

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Satellite provided fixed communications services: A forecast of potential domestic demand through the year 2000: Volume 2: Main text

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Potential satellite-provided fixed communications services, baseline forecasts, net long haul forecasts, cost analysis, net addressable forecasts, capacity requirements, and satellite system market development are considered.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

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

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

An Econometric Model for Forecasting Income and Employment in Hawaii.

ERIC Educational Resources Information Center

Chau, Laurence C.

This report presents the methodology for short-run forecasting of personal income and employment in Hawaii. The econometric model developed in the study is used to make actual forecasts through 1973 of income and employment, with major components forecasted separately. Several sets of forecasts are made, under different assumptions on external…

Objective Lightning Probability Forecasts for East-Central Florida Airports

NASA Technical Reports Server (NTRS)

Crawford, Winfred C.

2013-01-01

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

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

The Utility of a Geostationary Doppler radar applied to the hurricane analysis and prediction problem: A Report on the 1st Nexrad in Space Workshop

NASA Astrophysics Data System (ADS)

Tripoli, G. J.; Chandrasekar, V.; Chen, S. S.; Holland, G. J.; Im, E.; Kakar, R.; Lewis, W. E.; Marks, F. D.; Smith, E. A.; Tanelli, S.

2007-12-01

Last April the first Nexrad in Space (NIS) workshop was held in Miami, Florida to discuss the value and requirements for a possible satellite mission featuring a Doppler radar in geostationary orbit capable of measuring the internal structure of tropical cyclones over a circular scan area 50 degrees latitude in diameter. The proposed NIS technology, based on the PR2 radar design developed at JPL and an innovative deployable antenna design developed at UCLA would be capable of 3D volume sampling with 12 km horizontal and 300 m vertical resolution and 1 hour scan period. The workshop participants consisted of the JPL and UCLA design teams and cross section of tropical cyclone forecasters, researchers and modelers who could potentially benefit from this technology. The consensus of the workshop included: (a) the NIS technology would provide observations to benefit hurricane forecasters, real time weather prediction models and model researchers, (b) the most important feature of NIS was its high frequency coverage together with its 3D observation capability. These features were found to fill a data gap, now developing within cloud resolving analysis and prediction systems for which there is no other proposed solution, particularly over the oceans where TCs form. Closing this data gap is important to the improvement of TC intensity prediction. A complete description of the potential benefits and recommended goals for this technology concluded by the workshop participants will be given at the oral presentation.

Impact of four-dimensional data assimilation (FDDA) on urban climate analysis

NASA Astrophysics Data System (ADS)

Pan, Linlin; Liu, Yubao; Liu, Yuewei; Li, Lei; Jiang, Yin; Cheng, Will; Roux, Gregory

2015-12-01

This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations.

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

The circulation in the Levantine Basin as inferred from in-situ data and numerical modelling (1995-2013)

NASA Astrophysics Data System (ADS)

Zodiatis, George; Radhakrishnan, Hari; Lardner, Robin; Hayes, Daniel; Gertman, Isaac; Menna, Milena; Poulain, Pierre-Marie

2014-05-01

The general anticlockwise circulation along the coastline of the Eastern Mediterranean Levantine Basin was first proposed by Nielsen in 1912. Half a century later the schematic of the circulation in the area was enriched with sub-basin flow structures. In late 1980s, a more detailed picture of the circulation composed of eddies, gyres and coastal-offshore jets was defined during the POEM cruises. In 2005, Millot and Taupier-Letage have used SST satellite imagery to argue for a simpler pattern similar to the one proposed almost a century ago. During the last decade, renewed in-situ multi-platforms investigations under the framework of CYBO, CYCLOPS, NEMED, GROOM, HaiSec and PERSEUS projects, as well the development of the operational ocean forecasts and hindcasts in the framework of the MFS, ECOOP, MERSEA and MyOcean projects, have made possible to obtain an improved, higher spatial and temporal resolution picture of the circulation in the area. After some years of scientific disputes on the circulation pattern of the region, the new in-situ data sets and the operational numerical simulations confirm the relevant POEM results. The existing POM-based Cyprus Coastal Ocean Forecasting System (CYCOFOS), downscaling the MyOcean MFS, has been providing operational forecasts in the Eastern Mediterranean Levantine Basin region since early 2002. Recently, Radhakrishnan et al. (2012) parallelized the CYCOFOS hydrodynamic flow model using MPI to improve the accuracy of predictions while reducing the computational time. The parallel flow model is capable of modeling the Eastern Mediterranean Levantine Basin flow at a resolution of 500 m. The model was run in hindcast mode during which the innovations were computed using the historical data collected using gliders and cruises. Then, DD-OceanVar (D'Amore et al., 2013), a data assimilation tool based on 3DVAR developed by CMCC was used to compute the temperature and salinity field corrections. Numerical modeling results after the data assimilation will be presented.

Counteracting structural errors in ensemble forecast of influenza outbreaks.

PubMed

Pei, Sen; Shaman, Jeffrey

2017-10-13

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

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

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

NASA Astrophysics Data System (ADS)

Sulaiman, M.; El-Shafie, A.; Karim, O.; Basri, H.

2011-10-01

Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

Forecasting daily emergency department visits using calendar variables and ambient temperature readings.

PubMed

Marcilio, Izabel; Hajat, Shakoor; Gouveia, Nelson

2013-08-01

This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources. © 2013 by the Society for Academic Emergency Medicine.

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

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

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

Statistical Short-Range Forecast Guidance for Cloud Ceilings Over the Shuttle Landing Facility

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2001-01-01

This report describes the results of the AMU's Short-Range Statistical Forecasting task. The cloud ceiling forecast over the Shuttle Landing Facility (SLF) is a critical element in determining whether a Shuttle should land. Spaceflight Meteorology Group (SMG) forecasters find that ceilings at the SLF are challenging to forecast. The AMU was tasked to develop ceiling forecast equations to minimize the challenge. Studies in the literature that showed success in improving short-term forecasts of ceiling provided the basis for the AMU task. A 20-year record of cool-season hourly surface observations from stations in east-central Florida was used for the equation development. Two methods were used: an observations-based (OBS) method that incorporated data from all stations, and a persistence climatology (PCL) method used as the benchmark. Equations were developed for 1-, 2-, and 3-hour lead times at each hour of the day. A comparison between the two methods indicated that the OBS equations performed well and produced an improvement over the PCL equations. Therefore, the conclusion of the AMU study is that OBS equations produced more accurate forecasts than the PCL equations, and can be used in operations. They provide another tool with which to make the ceiling forecasts that are critical to safe Shuttle landings at KSC.

Methodological Problems in the Forecasting of Education

ERIC Educational Resources Information Center

Kostanian, S. L.

1978-01-01

Examines how forecasting of educational development in the Soviet Union can be coordinated with forecasts of scientific and technical progress. Predicts that the efficiency of social forecasting will increase when more empirical data on macro- and micro-processes is collected. (Author/DB)

Louisiana Airport System Plan aviation activity forecasts 1990-2010.

DOT National Transportation Integrated Search

1991-07-01

This report documents the methodology used to develop the aviation activity forecasts prepared as a part of the update to the Louisiana Airport System Plan and provides Louisiana aviation forecasts for the years 1990 to 2010. In general, the forecast...

Improving governance action by an advanced water modelling system applied to the Po river basin in Italy

NASA Astrophysics Data System (ADS)

Alessandrini, Cinzia; Del Longo, Mauro; Pecora, Silvano; Puma, Francesco; Vezzani, Claudia

2013-04-01

In spite of the historical abundance of water due to rains and to huge storage capacity provided by alpine lakes, Po river basin, the most important Italian water district experienced in the past ten years five drought/water scarcity events respectively in 2003, 2006, 2007 and 2012 summers and in the 2011-2012 winter season. The basic approach to these crises was the observation and the post-event evaluation; from 2007 an advanced numerical modelling system, called Drought Early Warning System for the Po River (DEWS-Po) was developed, providing advanced tools to simulate the hydrological and anthropic processes that affect river flows and allowing to follow events with real-time evaluations. In early 2012 the same system enabled also forecasts. Dews-Po system gives a real-time representation of water distribution across the basin, characterized by high anthropogenic pressure, optimizing with specific tools water allocation in competing situations. The system represents an innovative approach in drought forecast and in water resource management in the Po basin, giving deterministic and probabilistic meteorological forecasts as input to a chain for numerical distributed modelling of hydrological and hydraulic simulations. The system architecture is designed to receive in input hydro-meteorological actually observed and forecasted variables: deterministic meteorological forecasts with a fifteen days lead time, withdrawals data for different uses, natural an artificial reservoirs storage and release data. The model details are very sharp, simulating also the interaction between Adriatic sea and Po river in the delta area in terms of salt intrusion forecasting. Calculation of return period through run-method and of drought stochastic-indicators are enabled to assess the characteristics of the on-going and forecasted event. An Inter-institutional Technical Board is constituted within the Po River Basin Authority since 2008 and meets regularly during water crises to act decisions regarding water management in order to prevent major impacts. The Board is made of experts from public administrations with a strong involvement of stakeholders representative of different uses. The Dews- Po was intensively used by the Technical Board as decision support system during the 2012 summer event, providing tools to understand the on-going situation of water availability and use across the basin, helping to evaluate water management choices in an objective way, through what-if scenarios considering withdrawals reduction and increased releases from regulated Alpine lakes. A description of the use of Dews- Po system within the Technical Board is given, especially focusing on those elements, prone to be considered "good management indicators", which proved to be most useful in ensuring the success of governance action. Strength and improvement needs of the system are then described

Researches on High Accuracy Prediction Methods of Earth Orientation Parameters

NASA Astrophysics Data System (ADS)

Xu, X. Q.

2015-09-01

The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients respectively, which are used to improve/re-evaluate the AR model. Comparing to the single AR model, the AR+Kalman method performs better in the prediction of UT1-UTC and ΔLOD, and the improvement in the prediction of the polar motion is significant. (3) Following the successful Earth Orientation Parameter Prediction Comparison Campaign (EOP PCC), the Earth Orientation Parameter Combination of Prediction Pilot Project (EOPC PPP) was sponsored in 2010. As one of the participants from China, we update and submit the short- and medium-term (1 to 90 days) EOP predictions every day. From the current comparative statistics, our prediction accuracy is on the medium international level. We will carry out more innovative researches to improve the EOP forecast accuracy and enhance our level in EOP forecast.

Extended-Range Forecasts at Climate Prediction Center: Current Status and Future Plans

NASA Astrophysics Data System (ADS)

Kumar, A.

2016-12-01

Motivated by a user need to provide forecast information on extended-range time-scales (i.e., weeks 2-4), in recent years Climate Prediction Center (CPC) has made considerable efforts towards developing and testing the feasibility for developing the required forecasts. The forecasts targeting this particular time-scale face a unique challenge in that while the forecast skill due to atmospheric initial conditions is small (because of rapid decay in the memory associated with the atmospheric initial conditions), short time averages for which forecasts are made do not benefit from skill associated with anomalous boundary conditions either. Despite these challenges, CPC has embarked on providing an experimental outlook for weeks 3-4 average. The talk will summarize the current status of CPC's current suite of extended-range forecast products, and further, will discuss some future plans.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania.

PubMed

Karpušenkaitė, Aistė; Ruzgas, Tomas; Denafas, Gintaras

2018-05-01

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

FHWA travel analysis framework : development of VMT forecasting models for use by the Federal Highway Administration

DOT National Transportation Integrated Search

2014-05-12

This document details the process that the Volpe National Transportation Systems Center (Volpe) used to develop travel forecasting models for the Federal Highway Administration (FHWA). The purpose of these models is to allow FHWA to forecast future c...

KSC-06pd1284

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, Shuttle Weather Officer Kathy Winters briefs the media on how the launch weather forecast is developed. Attendees also were able to meet the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Innovative type of Reproduction of Agriculture of the Komi Republic - the Basis of its Sustainable Development

NASA Astrophysics Data System (ADS)

Ponomareva, Anna

2013-04-01

The necessity of transition of agriculture to sustainability is complicated by the necessity to increase production of local environmentally safe food, unemployment indigenous growth of living standards of the peasant community, stable and balanced nature management. Due to the difficult economic conditions of natural and agricultural development for the Komi Republic principle of food self-sufficiency is unacceptable, but the production of basic food products, for which favorable there are conditions, is objective necessity in the short term. Priority directions of development of the agricultural and fisheries sectors: the production of socially significant food products - potatoes, vegetables of the local range, milk, fresh meat, eggs, dietary, preservation and development of traditional industries, and collecting wild mushrooms and berries and its processing. Off forecast in the northern agricultural areas three scenarios selected: a base (slow), optimistic and pessimistic. For all versions of the forecast to be considered systemic crisis of the agricultural sector of the North is ongoing. Functioning of on sector under a particular scenario will depend on the factors and conditions that affect the stability of the agricultural enterprises and farms. At the base, especially under unfavorable conditions, negative external factors and conditions will prevail. The baseline scenario of recent years assumes the maintenance of the rate of change indicators of agriculture, of the levels of state industry conditions of interbranch exchange in agriculture, of access to economic entities in the financial markets, of the pricing and taxation policies, of relatively low investment opportunities to upgrade production capacity. In this embodiment the growth of agricultural production and its reduction will occur in suburban (peripheral areas). The optimistic scenario will be characterized by protectionist policies of the state, increase investment to improve soil fertility, renewal of the production capacity, attracting qualified personnel a field, the development of industrial infrastructure, a significant improvement of social and living conditions in rural areas. This version is based on the innovative development of the agricultural sector. Unfavorable effects of negative factors and conditions stimulate many problems in the agricultural sector of the North. If you save the disparity in prices for agricultural products and inputs, suppling the village, I and III monopoly agribusiness, reducing level of state industry, access to financial markets will be difficult for farmers, there will be a decrease in income of agricultural producers, the degradation of productive capacity, the outflow of cadres from the village, the reduction of potatoes, vegetables, dairy products and beef. In rural areas, to further deterioration of the demographic situation and as a consequence this populated area will decrease. The most acceptable scenario of post-crisis development of agriculture of the North is an optimistic scenario, providing innovative type of reproduction. To implement it, you, first of all, need to overcome the negative effects of a sharp transition to a market sector with the monetarist approaches to form multi-purpose agricultural policy aimed at modernizing agricultural production and improving the welfare of farmers. There is opportunities for the implementation of the optimistic scenario in the Komi Republic.

Uncovering Innovation Features and Emerging Technologies in Molecular Biology through Patent Analysis.

PubMed

Pereira, Cristiano Gonçalves; Porto, Geciane Silveira

2018-01-01

Scientific research at universities has a crucial role in leveraging a country's innovative potential. Sectors that require greater investments in technology for the development of their research, such as biotechnology, need to be aware of the frontier state-of-the-art technology and the knowledge incrusted within it. Although the information available in scientific articles is well explored in academic environment, the patent literature, where much of the technological information is present, is still poorly accessed. This chapter is intended to instruct students and researchers at universities to look at patent document analysis as a source of scientific and technological information and explore its applications. Within this chapter, we use the technological area regarding immunoglobulins inventions (monoclonal and polyclonal antibodies) as example to provide directions on how to develop a patent landscape to get an overview of the inventions in a certain field; how to map a collaborative network of inventors/assignees to help the pursuit and identification of future partnerships; and lastly we describe the steps of how to set up a network of patent citations with the aim of forecasting emerging technologies. We strongly believe that incorporate data from patents in planning phase of research projects at academia, as well as to establish partnerships and join R&D efforts to invest on promising technologies, is of great relevance to leverage the growth of the biotechnology sector.

Future sustainability forecasting by exchange markets: basic theory and an application.

PubMed

Malyshkina, Nataliya; Niemeier, Deb

2010-12-01

Setting sustainability targets and evaluating systems progress are of great importance nowadays due to threats to the human society, to economic development and to ecosystems, posed by unsustainable human activities. This research establishes a probabilistic theoretical approach based on market expectations reflected in prices of publicly traded securities to estimate the time horizon until the appearance of new technologies related to replacement of nonrenewable resources, for example, crude oil and oil products. To assess time T when technological innovations are likely to appear, we apply advanced pricing equations, based on a stochastic discount factor to those traded securities whose future cash flows critically depend on appearance of such innovations. In a simple approximation of the proposed approach applied to replacement of crude oil and oil products, we obtain T ≈ (P(0)(oil)/C(0))·ln (Δ·P(0)(oil)/P(0)(alt)), where P(0)(oil) and P(0)(alt) are the current aggregate market capitalizations of oil and alternative-energy companies, C(0) is the annual aggregate dividends that oil companies pay to their shareholders at the present, and Δ is the fraction of the oil (oil products) replaced at time T. This formula gives T ≈ 131 years for replacement of gasoline and diesel. The proposed market-expectations approach may allow policymakers to effectively develop policies and plan for long-term changes.

3D Exploration of Meteorological Data: Facing the challenges of operational forecasters

NASA Astrophysics Data System (ADS)

Koutek, Michal; Debie, Frans; van der Neut, Ian

2016-04-01

In the past years the Royal Netherlands Meteorological Institute (KNMI) has been working on innovation in the field of meteorological data visualization. We are dealing with Numerical Weather Prediction (NWP) model data and observational data, i.e. satellite images, precipitation radar, ground and air-borne measurements. These multidimensional multivariate data are geo-referenced and can be combined in 3D space to provide more intuitive views on the atmospheric phenomena. We developed the Weather3DeXplorer (W3DX), a visualization framework for processing and interactive exploration and visualization using Virtual Reality (VR) technology. We managed to have great successes with research studies on extreme weather situations. In this paper we will elaborate what we have learned from application of interactive 3D visualization in the operational weather room. We will explain how important it is to control the degrees-of-freedom during interaction that are given to the users: forecasters/scientists; (3D camera and 3D slicing-plane navigation appear to be rather difficult for the users, when not implemented properly). We will present a novel approach of operational 3D visualization user interfaces (UI) that for a great deal eliminates the obstacle and the time it usually takes to set up the visualization parameters and an appropriate camera view on a certain atmospheric phenomenon. We have found our inspiration in the way our operational forecasters work in the weather room. We decided to form a bridge between 2D visualization images and interactive 3D exploration. Our method combines WEB-based 2D UI's, pre-rendered 3D visualization catalog for the latest NWP model runs, with immediate entry into interactive 3D session for selected visualization setting. Finally, we would like to present the first user experiences with this approach.

Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Dasilva, Arindo M.

2012-01-01

Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.

Collaborative Initiative toward Developing River Forecasting in South America

NASA Astrophysics Data System (ADS)

Cabrera, R.

2015-12-01

In the United States, river floods have been discussed as early as 1884. Following a disastrous flooding in 1903, Congress passed legislation and river and flood services became a separate division within the U.S. Weather Bureau. The first River Forecast Center started in 1946 and today the whole country is served by thirteen River Forecast Centers. News from Latin American and Caribbean Countries often report of devastating flooding. However, river forecast services are not fully developed yet. This presentation suggests the utilization of a multinational collaborative approach toward the development of river forecasts in order to mitigate flooding in South America. The benefit of an international strategy resides in the strength created by a team of professionals with different capabilities and expertise.

Innovative approaches helpful to enhance knowledge on weather-related stroke events over a wide geographical area and a large population.

PubMed

Morabito, Marco; Crisci, Alfonso; Vallorani, Roberto; Modesti, Pietro Amedeo; Gensini, Gian Franco; Orlandini, Simone

2011-03-01

Results on the effect of weather on stroke occurrences are still confusing and controversial. The aim of this study was to retrospectively investigate in Tuscany (central Italy) the weather-related stroke events through the use of an innovative source of weather data (Reanalysis) together with an original statistical approach to quantify the prompt/delayed health effects of both cold and heat exposures. Daily stroke hospitalizations and meteorologic data from the Reanalysis 2 Achieve were obtained for the period 1997 to 2007. Generalized linear and additive models and an innovative modeling approach, the constrained segmented distributed lag model, were applied. Both daily averages and day-to-day changes of air temperature and geopotential height (a measure that approximates the mean surface pressure) were selected as independent predictors of all stroke occurrences. In particular, a 5°C temperature decrease was associated with 16.5% increase of primary intracerebral hemorrhage of people ≥65 years of age. A general short-term cold effect on hospitalizations limited to 1 week after exposure was observed and, for the first time, a clear harvesting effect (deficit of hospitalization) for cold-related primary intracerebral hemorrhage was described. Day-to-day changes of meteorologic parameters disclosed characteristic U- and J-shaped relationships with stroke occurrences. Thanks to the intrinsic characteristic of Reanalysis, these results might simply be implemented in an operative forecast system regarding weather-related stroke events with the aim to develop preventive health plans.

CARA: Cognitive Architecture for Reasoning About Adversaries

DTIC Science & Technology

2012-01-20

synthesis approach taken here the KIDS principle (Keep It Descriptive, Stupid ) applies, and agents and organizations are profiled in great detail...developed two algorithms to make forecasts about adversarial behavior. We developed game-theoretical approaches to reason about group behavior. We...to automatically make forecasts about group behavior together with methods to quantify the uncertainty inherent in such forecasts; • Developed

Telecommunications forecast for ITU Region 2 to the year 1995

NASA Technical Reports Server (NTRS)

Hollansworth, J. E.; Salzman, J. A.; Ramler, J. R.

1985-01-01

Telecommunications activity was studied. The primary objective was to forecast the need for fixed service satellites (FSS) by countries within ITU Region 2 excluding the United States and Greenland. Forecasts of telecommunications equipment needs were developed as a yardstick of the relative level of telecommunications activity among developing countries within the region. A likely scenario for the implementation of domestic and regional communications satellites is forecasted to provide services to and among countries in ITU Region 2. By 1995, it is forecast that 15 fixed service satellites will be implemented. A forecast of the countries requirements indicates that, with the possible exception of Canada, this constellation of satellites will meet these countries' needs to beyond the year 2000.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

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

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

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.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

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

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Forecasting the Risks of Pollution from Ships along the Portuguese Coast

NASA Astrophysics Data System (ADS)

Fernandes, Rodrigo; Neves, Ramiro; Lourenço, Filipe; Braunschweig, Frank

2013-04-01

Pollution risks in coastal and marine environments are in general based in a static approach, considering historical data, reference situations, and typical scenarios. This approach is quite important in a planning stage. However, an alternative approach can be studied, due to the latest implementation of several different real-time monitoring tools as well as faster performances in the generation of numerical forecasts for metocean properties and trajectories of pollutants spilt at sea or costal zones. These developments provide the possibility of developing an integrated support system for better decision-making in emergency or planning issues associated to pollution risks. An innovative methodology to dynamically produce quantified risks in real-time, integrating best available information from numerical forecasts and the existing monitoring tools, has been developed and applied to the Portuguese Coast. The developed system provides coastal pollution risk levels associated to potential (or real) oil spill incidents from ship collision, grounding or foundering, taking into account regional statistic information on vessel accidents and coastal sensitivity indexes, real-time vessel information (positioning, cargo type, speed and vessel type) obtained from AIS, best-available metocean numerical forecasts (hydrodynamics, meteorology - including visibility, wave conditions) and simulated scenarios by the oil spill fate and behaviour component of MOHID Water Modelling System. Different spill fate and behaviour simulations are continuously generated and processed in background (assuming hypothetical spills from vessels), based on variable vessel information and metocean conditions. Results from these simulations are used in the quantification of consequences of potential spills. All historic information is continuously stored in a database (for risk analysis at a later stage). This dynamic approach improves the accuracy in quantification of consequences to the shoreline, as well as the decision support model, allowing a more effective prioritization of individual ships and geographical areas. This system was initially implemented in Portugal for oil spills. The implementation in other Atlantic Regions (starting in Galician Coast, Spain) is being executed in the scope of ARCOPOL+ project (2011-1/150), as well as other relevant updates. The system is being adapted to include risk modelling of chemical spills, as well as fire & explosion accidents and operational illegal discharges. Also the integration of EMSA's THETIS "ship risk profile" (according to Annex 7 from Paris Memorandum of Understanding) in the risk model is being tested. Finally, a new component is being developed to compute the risk for specific time periods, taking advantage of the information previously stored in the database on the positioning of vessels and / or results of numerical models. This component provides the possibility of obtaining a support tool for detailed characterization of risk profiles in certain periods or a sensitivity analysis on different parameters.

Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

NASA Astrophysics Data System (ADS)

Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

2017-12-01

The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

Using Landslide Failure Forecast Models in Near Real Time: the Mt. de La Saxe case-study

NASA Astrophysics Data System (ADS)

Manconi, Andrea; Giordan, Daniele

2014-05-01

Forecasting the occurrence of landslide phenomena in space and time is a major scientific challenge. The approaches used to forecast landslides mainly depend on the spatial scale analyzed (regional vs. local), the temporal range of forecast (long- vs. short-term), as well as the triggering factor and the landslide typology considered. By focusing on short-term forecast methods for large, deep seated slope instabilities, the potential time of failure (ToF) can be estimated by studying the evolution of the landslide deformation over time (i.e., strain rate) provided that, under constant stress conditions, landslide materials follow creep mechanism before reaching rupture. In the last decades, different procedures have been proposed to estimate ToF by considering simplified empirical and/or graphical methods applied to time series of deformation data. Fukuzono, 1985 proposed a failure forecast method based on the experience performed during large scale laboratory experiments, which were aimed at observing the kinematic evolution of a landslide induced by rain. This approach, known also as the inverse-velocity method, considers the evolution over time of the inverse value of the surface velocity (v) as an indicator of the ToF, by assuming that failure approaches while 1/v tends to zero. Here we present an innovative method to aimed at achieving failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and then apply straightforward statistical methods to obtain confidence intervals on the time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to different failure forecast methods. We applied for the first time the herein presented approach in near real time during the emergency scenario relevant to the reactivation of the La Saxe rockslide, a large mass wasting menacing the population of Courmayeur, northern Italy, and the important European route E25. We show how the application of simplified but robust forecast models can be a convenient method to manage and support early warning systems during critical situations. References: Fukuzono T. (1985), A New Method for Predicting the Failure Time of a Slope, Proc. IVth International Conference and Field Workshop on Landslides, Tokyo.

Innovative Techniques to Model, Analyze and Monitor Space Effects on Air Force Space-Based Systems

DTIC Science & Technology

2010-03-20

of Comets in the Heliosphere as Observed by SMEI 4 2.8. Zodiacal Light Observations and Modeling 5 2.9. Space Weather Forecasting Lab (SWFL...This research resulted in two publications and a presentation at the 2007 American Geophysical Union Fall Meeting. 2.8. Zodiacal Light Observations...and Modeling One of the backgrounds removed from SMEI imagery is the scattered zodiacal light from solar system dust. The zodiacal light has

An Innovative Network to Improve Sea Ice Prediction in a Changing Arctic

DTIC Science & Technology

2015-09-30

fall (July – Oct), the CFSv2 forecasts are too extensive (positive bias), while in the rest of the year they are not extensive enough (negative bias...the year (January to September), but a negative bias in the early fall (October to December), during the ice growing season. This illustrate the...Overland, and S. Yang, Polar-Low latitude linkages and their role in weather and climate prediction, 2015: Bull. Amer. Meteor . Soc. [in press]. HONORS

An impact analysis of forecasting methods and forecasting parameters on bullwhip effect

NASA Astrophysics Data System (ADS)

Silitonga, R. Y. H.; Jelly, N.

2018-04-01

Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.

NASA GSFC Space Weather Center - Innovative Space Weather Dissemination: Web-Interfaces, Mobile Applications, and More

NASA Technical Reports Server (NTRS)

Maddox, Marlo; Zheng, Yihua; Rastaetter, Lutz; Taktakishvili, A.; Mays, M. L.; Kuznetsova, M.; Lee, Hyesook; Chulaki, Anna; Hesse, Michael; Mullinix, Richard;

Pharmaceutical expenditure forecast model to support health policy decision making

PubMed Central

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

2014-01-01

Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of generics had a major impact on savings. However, very aggressive pricing of generic and biosimilar products might make this market unattractive and can be counterproductive. Worth noting, delaying time to access innovative products was also identified as an effective leverage to increase savings but might not be a desirable policy for breakthrough products. Increasing patient financial contributions, either directly or indirectly via their private insurances, is a more likely scenario rather than expanding the national pharmaceutical expenditure coverage. PMID:27226830

Evolving the Land Information System into a Cloud Computing Service

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

Houser, Paul R.

The Land Information System (LIS) was developed to use advanced flexible land surface modeling and data assimilation frameworks to integrate extremely large satellite- and ground-based observations with advanced land surface models to produce continuous high-resolution fields of land surface states and fluxes. The resulting fields are extremely useful for drought and flood assessment, agricultural planning, disaster management, weather and climate forecasting, water resources assessment, and the like. We envisioned transforming the LIS modeling system into a scientific cloud computing-aware web and data service that would allow clients to easily setup and configure for use in addressing large water management issues.more » The focus of this Phase 1 project was to determine the scientific, technical, commercial merit and feasibility of the proposed LIS-cloud innovations that are currently barriers to broad LIS applicability. We (a) quantified the barriers to broad LIS utility and commercialization (high performance computing, big data, user interface, and licensing issues); (b) designed the proposed LIS-cloud web service, model-data interface, database services, and user interfaces; (c) constructed a prototype LIS user interface including abstractions for simulation control, visualization, and data interaction, (d) used the prototype to conduct a market analysis and survey to determine potential market size and competition, (e) identified LIS software licensing and copyright limitations and developed solutions, and (f) developed a business plan for development and marketing of the LIS-cloud innovation. While some significant feasibility issues were found in the LIS licensing, overall a high degree of LIS-cloud technical feasibility was found.« less

Bridging the Gap between the Technological Singularity and Medicine: Highlighting a Course on Technology and the Future of Medicine

PubMed Central

Solez, Kim; Bernier, Ashlyn; Crichton, Joel; Graves, Heather; Kuttikat, Preeti; Lockwood, Ross; Marovitz, William F.; Monroe, Damon; Pallen, Mark; Pandya, Shawna; Pearce, David; Saleh, Abdullah; Sandhu, Neelam; Sergi, Consolato; Tuszynski, Jack; Waugh, Earle; White, Jonathan; Wong, Julielynn; Woodside, Michael; Wyndham, Roger; Zaiane, Osmar; Zakus, David

2013-01-01

The “technological singularity” is forecasted to occur in the mid-21st century and is defined as the point when machines will become smarter than humans and thus trigger the merging of humans and machines. It is hypothesized that this will have a profound influence on medicine and population health. This paper describes a new course entitled “Technology and the Future of Medicine” developed by a multi-disciplinary group of experts. The course began as a continuing medical education course and then transitioned to an accredited graduate-level course. We describe the philosophy of the course and the innovative solutions to the barriers that were encountered, with a focus on YouTube audience retention analytics. Our experience may provide a useful template for others. PMID:24171879

Business grants

NASA Astrophysics Data System (ADS)

Twelve small businesses who are developing equipment and computer programs for geophysics have won Small Business Innovative Research (SBIR) grants from the National Science Foundation for their 1989 proposals. The SBIR program was set up to encourage the private sector to undertake costly, advanced experimental work that has potential for great benefit.The geophysical research projects are a long-path intracavity laser spectrometer for measuring atmospheric trace gases, optimizing a local weather forecast model, a new platform for high-altitude atmospheric science, an advanced density logging tool, a deep-Earth sampling system, superconducting seismometers, a phased-array Doppler current profiler, monitoring mesoscale surface features of the ocean through automated analysis, krypton-81 dating in polar ice samples, discrete stochastic modeling of thunderstorm winds, a layered soil-synthetic liner base system to isolate buildings from earthquakes, and a low-cost continuous on-line organic-content monitor for water-quality determination.

Developement of an Optimum Interpolation Analysis Method for the CYBER 205

NASA Technical Reports Server (NTRS)

Nestler, M. S.; Woollen, J.; Brin, Y.

1985-01-01

A state-of-the-art technique to assimilate the diverse observational database obtained during FGGE, and thus create initial conditions for numerical forecasts is described. The GLA optimum interpolation (OI) analysis method analyzes pressure, winds, and temperature at sea level, mixing ratio at six mandatory pressure levels up to 300 mb, and heights and winds at twelve levels up to 50 mb. Conversion to the CYBER 205 required a major re-write of the Amdahl OI code to take advantage of the CYBER vector processing capabilities. Structured programming methods were used to write the programs and this has resulted in a modular, understandable code. Among the contributors to the increased speed of the CYBER code are a vectorized covariance-calculation routine, an extremely fast matrix equation solver, and an innovative data search and sort technique.

Bringing Space Weather Down to Earth

NASA Astrophysics Data System (ADS)

Reiff, P. H.; Sumners, C.

2005-05-01

Most of the public has no idea what Space Weather is, but a number of innovative programs, web sites, magazine articles, TV shows and planetarium shows have taken space weather from an unknown quantity to a much more visible field. This paper reviews new developments, including the new Space Weather journal, the very popular spaceweather.com website, new immersive planetarium shows that can go "on the road", and well-publicized Sun-Earth Day activities. Real-time data and reasonably accurate spaceweather forecasts are available from several websites, with many subscribers. Even the renaissance of amateur radio because of Homeland Security brings a new generation of learners to wonder what is going on in the Sun today. The NSF Center for Integrated Space Weather Modeling has a dedicated team to reach both the public and a greater diversity of new scientists.

Long-run evolution of the global economy: 2. Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-03-01

Long-range climate forecasts rely upon integrated assessment models that link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework, outlined in Part 1, that is based on physical principles rather than explicitly resolved societal dynamics. Relative to a reference model of persistence in trends, model hindcasts that are initialized with data from 1950 to 1960 reproduce trends in global economic production and energy consumption between 2000 and 2010 with a skill score greater than 90%. In part, such high skill appears to be because civilization has responded to an impulse of fossil fuel discovery in the mid-twentieth century. Forecasting the coming century will be more of a challenge because the effect of the impulse appears to have nearly run its course. Nonetheless, the model offers physically constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Policy modeling for industrial energy use

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

Worrell, Ernst; Park, Hi-Chun; Lee, Sang-Gon

2003-03-01

The international workshop on Policy Modeling for Industrial Energy Use was jointly organized by EETA (Professional Network for Engineering Economic Technology Analysis) and INEDIS (International Network for Energy Demand Analysis in the Industrial Sector). The workshop has helped to layout the needs and challenges to include policy more explicitly in energy-efficiency modeling. The current state-of-the-art models have a proven track record in forecasting future trends under conditions similar to those faced in the recent past. However, the future of energy policy in a climate-restrained world is likely to demand different and additional services to be provided by energy modelers. Inmore » this workshop some of the international models used to make energy consumption forecasts have been discussed as well as innovations to enable the modeling of policy scenarios. This was followed by the discussion of future challenges, new insights in the data needed to determine the inputs into energy model s, and methods to incorporate decision making and policy in the models. Based on the discussion the workshop participants came to the following conclusions and recommendations: Current energy models are already complex, and it is already difficult to collect the model inputs. Hence, new approaches should be transparent and not lead to extremely complex models that try to ''do everything''. The model structure will be determined by the questions that need to be answered. A good understanding of the decision making framework of policy makers and clear communication on the needs are essential to make any future energy modeling effort successful. There is a need to better understand the effects of policy on future energy use, emissions and the economy. To allow the inclusion of policy instruments in models, evaluation of programs and instruments is essential, and need to be included in the policy instrument design. Increased efforts are needed to better understand the effects of innovative (no n-monetary) policy instruments through evaluation and to develop approaches to model both conventional and innovative policies. The explicit modeling of barriers and decision making in the models seems a promising way to enable modeling of conventional and innovative policies. A modular modeling approach is essential to not only provide transparency, but also to use the available resources most effectively and efficiently. Many large models have been developed in the past, but have been abandoned after only brief periods of use. A development path based on modular building blocks needs the establishment of a flexible but uniform modeling framework. The leadership of international agencies and organizations is essential in the establishment of such a framework. A preference is given for ''softlinks'' between different modules and models, to increase transparency and reduce complexity. There is a strong need to improve the efficiency of data collection and interpretation efforts to produce reliable model inputs. The workshop participants support the need for the establishment of an (in-)formal exchanges of information, as well as modeling approaches. The development of an informal network of research institutes and universities to help build a common dataset and exchange ideas on specific areas is proposed. Starting with an exchange of students would be a relative low-cost way to start such collaboration. It would be essential to focus on specific topics. It is also essential to maintain means of regular exchange of ideas between researchers in the different focus points.« less

The Met Office Coupled Atmosphere/Land/Ocean/Sea-Ice Data Assimilation System

NASA Astrophysics Data System (ADS)

Lea, Daniel; Mirouze, Isabelle; King, Robert; Martin, Matthew; Hines, Adrian

2015-04-01

The Met Office has developed a weakly-coupled data assimilation (DA) system using the global coupled model HadGEM3 (Hadley Centre Global Environment Model, version 3). At present the analysis from separate ocean and atmosphere DA systems are combined to produced coupled forecasts. The aim of coupled DA is to produce a more consistent analysis for coupled forecasts which may lead to less initialisation shock and improved forecast performance. The HadGEM3 coupled model combines the atmospheric model UM (Unified Model) at 60 km horizontal resolution on 85 vertical levels, the ocean model NEMO (Nucleus for European Modelling of the Ocean) at 25 km (at the equator) horizontal resolution on 75 vertical levels, and the sea-ice model CICE at the same resolution as NEMO. The atmosphere and the ocean/sea-ice fields are coupled every 1-hour using the OASIS coupler. The coupled model is corrected using two separate 6-hour window data assimilation systems: a 4D-Var for the atmosphere with associated soil moisture content nudging and snow analysis schemes on the one hand, and a 3D-Var FGAT for the ocean and sea-ice on the other hand. The background information in the DA systems comes from a previous 6-hour forecast of the coupled model. To isolate the impact of the coupled DA, 13-month experiments have been carried out, including 1) a full atmosphere/land/ocean/sea-ice coupled DA run, 2) an atmosphere-only run forced by OSTIA SSTs and sea-ice with atmosphere and land DA, and 3) an ocean-only run forced by atmospheric fields from run 2 with ocean and sea-ice DA. In addition, 5-day and 10-day forecast runs, have been produced from initial conditions generated by either run 1 or a combination of runs 2 and 3. The different results have been compared to each other and, whenever possible, to other references such as the Met Office atmosphere and ocean operational analyses or the OSTIA SST data. The performance of the coupled DA is similar to the existing separate ocean and atmosphere DA systems. This is despite the fact that the assimilation error covariances have not yet been tuned for coupled DA. In addition, the coupled model also exhibits some biases which do not affect the uncoupled models. An example is precipitation and run off errors affecting the ocean salinity. This of course impacts the performance of the ocean data assimilation. This does, however, highlight a particular benefit of data assimilation in that it can help to identify short term model biases by using, for example, the differences between the observations and model background (innovations) and the mean increments. Coupled DA has the distinct advantage that this gives direct information about the coupled model short term biases. By identifying the biases and developing solutions this will improve the short range coupled forecasts, and may also improve the coupled model on climate timescales.

[Demography perspectives and forecasts of the demand for electricity].

PubMed

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

Use of the Box and Jenkins time series technique in traffic forecasting

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

Nihan, N.L.; Holmesland, K.O.

The use of recently developed time series techniques for short-term traffic volume forecasting is examined. A data set containing monthly volumes on a freeway segment for 1968-76 is used to fit a time series model. The resultant model is used to forecast volumes for 1977. The forecast volumes are then compared with actual volumes in 1977. Time series techniques can be used to develop highly accurate and inexpensive short-term forecasts. The feasibility of using these models to evaluate the effects of policy changes or other outside impacts is considered. (1 diagram, 1 map, 14 references,2 tables)

Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

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

Hamann, Hendrik F.

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

Operational seasonal forecasting of crop performance.

PubMed

Stone, Roger C; Meinke, Holger

2005-11-29

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

Operational seasonal forecasting of crop performance

PubMed Central

Stone, Roger C; Meinke, Holger

2005-01-01

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

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

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-10-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2008-01-01

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average…

The Importance of Hurricane Research to Life, Property, the Economy, and National Security.

NASA Astrophysics Data System (ADS)

Busalacchi, A. J.

2017-12-01

The devastating 2017 Atlantic hurricane season has brought into stark relief how much hurricane forecasts have improved - and how important it is to make them even better. Whereas the error in 48-hour track forecasts has been reduced by more than half, according to the National Hurricane Center, intensity forecasts remain challenging, especially with storms such as Harvey that strengthened from a tropical depression to a Category 4 hurricane in less than three days. The unusually active season, with Hurricane Irma sustaining 185-mph winds for a record 36 hours and two Atlantic hurricanes reaching 150-mph winds simultaneously for the first time, also highlighted what we do, and do not, know about how tropical cyclones will change as the climate warms. The extraordinary toll of Hurricanes Harvey, Irma, and Maria - which may ultimately be responsible for hundreds of deaths and an estimated $200 billion or more in damages - underscores why investments into improved forecasting must be a national priority. At NCAR and UCAR, scientists are working with their colleagues at federal agencies, the private sector, and the university community to advance our understanding of these deadly storms. Among their many projects, NCAR researchers are making experimental tropical cyclone forecasts using an innovative Earth system model that allows for variable resolution. We are working with NOAA to issue flooding, inundation, and streamflow forecasts for areas hit by hurricanes, and we have used extremely high-resolution regional models to simulate successfully the rapid hurricane intensification that has proved so difficult to predict. We are assessing ways to better predict the damage potential of tropical cyclones by looking beyond wind speed to consider such important factors as the size and forward motion of the storm. On the important question of climate change, scientists have experimented with running coupled climate models at a high enough resolution to spin up a hurricane, and we have used a convection-permitting regional model to examine how named storms of the past might look if they were to formed in a warmer, wetter future. Finally, research is also being performed to better communicate forecasts to help residents make informed choices when a damaging storm approaches.

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.

Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource allocation are provided.

Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System

DTIC Science & Technology

2011-01-01

USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water

The analysis of rapidly developing fog at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Wheeler, Mark M.; Atchison, Michael K.; Schumann, Robin; Taylor, Greg E.; Yersavich, Ann; Warburton, John D.

1994-01-01

This report documents fog precursors and fog climatology at Kennedy Space Center (KSC) Florida from 1986 to 1990. The major emphasis of this report focuses on rapidly developing fog events that would affect the less than 7-statute mile visibility rule for End-Of-Mission (EOM) Shuttle landing at KSC (Rule 4-64(A)). The Applied Meteorology Unit's (AMU's) work is to: develop a data base for study of fog associated weather conditions relating to violations of this landing constraint; develop forecast techniques or rules-of-thumb to determine whether or not current conditions are likely to result in an acceptable condition at landing; validate the forecast techniques; and transition techniques to operational use. As part of the analysis the fog events were categorized as either advection, pre-frontal or radiation. As a result of these analyses, the AMU developed a fog climatological data base, identified fog precursors and developed forecaster tools and decision trees. The fog climatological analysis indicates that during the fog season (October to April) there is a higher risk for a visibility violation at KSC during the early morning hours (0700 to 1200 UTC), while 95 percent of all fog events have dissipated by 1600 UTC. A high number of fog events are characterized by a westerly component to the surface wind at KSC (92 percent) and 83 percent of the fog events had fog develop west of KSC first (up to 2 hours). The AMU developed fog decision trees and forecaster tools that would help the forecaster identify fog precursors up to 12 hours in advance. Using the decision trees as process tools ensures the important meteorological data are not overlooked in the forecast process. With these tools and a better understanding of fog formation in the local KSC area, the Shuttle weather support forecaster should be able to give the Launch and Flight Directors a better KSC fog forecast with more confidence.

Space Weather Forecasting: An Enigma

NASA Astrophysics Data System (ADS)

Sojka, J. J.

2012-12-01

The space age began in earnest on October 4, 1957 with the launch of Sputnik 1 and was fuelled for over a decade by very strong national societal concerns. Prior to this single event the adverse effects of space weather had been registered on telegraph lines as well as interference on early WWII radar systems, while for countless eons the beauty of space weather as mid-latitude auroral displays were much appreciated. These prior space weather impacts were in themselves only a low-level science puzzle pursued by a few dedicated researchers. The technology boost and innovation that the post Sputnik era generated has almost single handedly defined our present day societal technology infrastructure. During the decade following Neil's walk on the moon on July 21, 1969 an international thrust to understand the science of space, and its weather, was in progress. However, the search for scientific understand was parsed into independent "stove pipe" categories: The ionosphere-aeronomy, the magnetosphere, the heliosphere-sun. The present day scientific infrastructure of funding agencies, learned societies, and international organizations are still hampered by these 1960's logical divisions which today are outdated in the pursuit of understanding space weather. As this era of intensive and well funded scientific research progressed so did societies innovative uses for space technologies and space "spin-offs". Well over a decade ago leaders in technology, science, and the military realized that there was indeed an adverse side to space weather that with each passing year became more severe. In 1994 several U.S. agencies established the National Space Weather Program (NSWP) to focus scientific attention on the system wide issue of the adverse effects of space weather on society and its technologies. Indeed for the past two decades a significant fraction of the scientific community has actively engaged in understanding space weather and hence crossing the "stove-pipe" disciplines. The perceived progress in space weather understanding differs significantly depending upon which community (scientific, technology, forecaster, society) is addressing the question. Even more divergent are these thoughts when the question is how valuable is the scientific capability of forecasting space weather. This talk will discuss present day as well as future potential for forecasting space weather for a few selected examples. The author will attempt to straddle the divergent community opinions.

Selection and Classification Using a Forecast Applicant Pool.

ERIC Educational Resources Information Center

Hendrix, William H.

The document presents a forecast model of the future Air Force applicant pool. By forecasting applicants' quality (means and standard deviations of aptitude scores) and quantity (total number of applicants), a potential enlistee could be compared to the forecasted pool. The data used to develop the model consisted of means, standard deviation, and…

Acquisition for the Future: Imagination, Innovation, and Implementation. Research and Reality: Closing the Gap

DTIC Science & Technology

1993-01-01

adequate to this author to attend an out-of- assure the driver and passengers town class on planning . Since that the auto was in good the customer...Theory." Advances in Financial Planning Society of Cost Estimating & Analysis and Forecasting 2 (1987): 269-302. (October 1991): 4-8. 13. . "International...assessed if these milestones are U.S. firms plans to replace with newer not met. Unless you have a good equipment. However, credibility could be prior

Communications to 2000 - A forecast of the demand and capacity of U.S. domestic communications satellites

NASA Astrophysics Data System (ADS)

Fordyce, S. W.

The market demand for the U.S. domestic communications satellites accelerated in the late 70's, exceeding the capacity of the satellites currently in orbit. Satellite carriers have been authorized to build an additional 24 domsats. This paper examines the anticipated market demands, and the capability of the domsats to fulfill these demands. With various practical technical innovations, the domsats appear able to meet the expected market demands until the end of this century.

Forecasting for a Remote Island: A Class Exercise.

NASA Astrophysics Data System (ADS)

Riordan, Allen J.

2003-06-01

Students enrolled in a satellite meteorology course at North Carolina State University, Raleigh, recently had an unusual opportunity to apply their forecast skills to predict wind and weather conditions for a remote site in the Southern Hemisphere. For about 40 days starting in early February 2001, students used satellite and model guidance to develop forecasts to support a research team stationed on Bouvet Island (54°26S, 3°24E). Internet products together with current output from NCEP's Aviation (AVN) model supported the activity. Wind forecasts were of particular interest to the Bouvet team because violent winds often developed unexpectedly and posed a safety hazard.Results were encouraging in that 24-h wind speed forecasts showed reasonable reliability over a wide range of wind speeds. Forecasts for 48 h showed only marginal skill, however. Two critical events were well forecasted-the major February storm with wind speeds of over 120 kt and a brief calm period following several days of strong winds in early March. The latter forecast proved instrumental in recovering the research team.

Statistical Short-Range Guidance for Peak Wind Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station, Phase III

NASA Technical Reports Server (NTRS)

Crawford, Winifred

2010-01-01

This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations.The tool includes climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Crawford, Winifred; Roeder, William

2008-01-01

This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

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

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Operational forecasting of human-biometeorological conditions

NASA Astrophysics Data System (ADS)

Giannaros, T. M.; Lagouvardos, K.; Kotroni, V.; Matzarakis, A.

2018-03-01

This paper presents the development of an operational forecasting service focusing on human-biometeorological conditions. The service is based on the coupling of numerical weather prediction models with an advanced human-biometeorological model. Human thermal perception and stress forecasts are issued on a daily basis for Greece, in both point and gridded format. A user-friendly presentation approach is adopted for communicating the forecasts to the public via the worldwide web. The development of the presented service highlights the feasibility of replacing standard meteorological parameters and/or indices used in operational weather forecasting activities for assessing the thermal environment. This is of particular significance for providing effective, human-biometeorology-oriented, warnings for both heat waves and cold outbreaks.

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.

Stochastic Forcing for High-Resolution Regional and Global Ocean and Atmosphere-Ocean Coupled Ensemble Forecast System

NASA Astrophysics Data System (ADS)

Rowley, C. D.; Hogan, P. J.; Martin, P.; Thoppil, P.; Wei, M.

2017-12-01

An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC), and a global ocean ensemble generation capability to represent uncertainty in the ocean initial conditions has been developed. At extended forecast times, the uncertainty due to the model error overtakes the initial condition as the primary source of forecast uncertainty. Recently, stochastic parameterization or stochastic forcing techniques have been applied to represent the model error in research and operational atmospheric, ocean, and coupled ensemble forecasts. A simple stochastic forcing technique has been developed for application to US Navy high resolution regional and global ocean models, for use in ocean-only and coupled atmosphere-ocean-ice-wave ensemble forecast systems. Perturbation forcing is added to the tendency equations for state variables, with the forcing defined by random 3- or 4-dimensional fields with horizontal, vertical, and temporal correlations specified to characterize different possible kinds of error. Here, we demonstrate the stochastic forcing in regional and global ensemble forecasts with varying perturbation amplitudes and length and time scales, and assess the change in ensemble skill measured by a range of deterministic and probabilistic metrics.

Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

Remote sensing validation through SOOP technology: implementation of Spectra system

NASA Astrophysics Data System (ADS)

Piermattei, Viviana; Madonia, Alice; Bonamano, Simone; Consalvi, Natalizia; Caligiore, Aurelio; Falcone, Daniela; Puri, Pio; Sarti, Fabio; Spaccavento, Giovanni; Lucarini, Diego; Pacci, Giacomo; Amitrano, Luigi; Iacullo, Salvatore; D'Andrea, Salvatore; Marcelli, Marco

2017-04-01

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of marine research. The availability of low-cost technologies allows the realization of extended observatory networks for the study of marine phenomena through an integrated approach merging observations, remote sensing and operational oceanography. Marine services and practical applications critically depends on the availability of large amount of data collected with sufficiently dense spatial and temporal sampling. This issue directly influences the robustness both of ocean forecasting models and remote sensing observations through data assimilation and validation processes, particularly in the biological domain. For this reason it is necessary the development of cheap, small and integrated smart sensors, which could be functional both for satellite data validation and forecasting models data assimilation as well as to support early warning systems for environmental pollution control and prevention. This is particularly true in coastal areas, which are subjected to multiple anthropic pressures. Moreover, coastal waters can be classified like case 2 waters, where the optical properties of inorganic suspended matter and chromophoric dissolved organic matter must be considered and separated by the chlorophyll a contribution. Due to the high costs of mooring systems, research vessels, measure platforms and instrumentation a big effort was dedicated to the design, development and realization of a new low cost mini-FerryBox system: Spectra. Thanks to the modularity and user-friendly employment of the system, Spectra allows to acquire continuous in situ measures of temperature, conductivity, turbidity, chlorophyll a and chromophoric dissolved organic matter (CDOM) fluorescences from voluntary vessels, even by non specialized operators (Marcelli et al., 2014; 2016). This work shows the preliminary application of this technology to remote sensing data validation.

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.

Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

DTIC Science & Technology

2010-09-30

In part 2 (Bonazzi et al., 2010), the impact of the ensemble forecast methodology based on MFS-Wind-BHM perturbations is documented. Forecast...absence of dt data stage inputs, the forecast impact of MFS-Error-BHM is neutral. Experiments are underway now to introduce dt back into the MFS-Error...BHM and quantify forecast impacts at MFS. MFS-SuperEnsemble-BHM We have assembled all needed datasets and completed algorithmic development

Modeling and forecasting U.S. sex differentials in mortality.

PubMed

Carter, L R; Lee, R D

1992-11-01

"This paper examines differentials in observed and forecasted sex-specific life expectancies and longevity in the United States from 1900 to 2065. Mortality models are developed and used to generate long-run forecasts, with confidence intervals that extend recent work by Lee and Carter (1992). These results are compared for forecast accuracy with univariate naive forecasts of life expectancies and those prepared by the Actuary of the Social Security Administration." excerpt

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

2009-01-01

The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

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

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

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

Forecasting volcanic unrest using seismicity: The good, the bad and the time consuming

NASA Astrophysics Data System (ADS)

Salvage, Rebecca; Neuberg, Jurgen W.

2013-04-01

Volcanic eruptions are inherently unpredictable in nature, with scientists struggling to forecast the type and timing of events, in particular in real time scenarios. Current understanding suggests that the use of statistical patterns within precursory datasets of seismicity prior to eruptive events could hold the potential to be used as real time forecasting tools. They allow us to determine times of clear deviation in data, which might be indicative of volcanic unrest. The identification of low frequency seismic swarms and the acceleration of this seismicity prior to observed volcanic unrest may be key in developing forecasting tools. The development of these real time forecasting models which can be implemented at volcano observatories is of particular importance since the identification of early warning signals allows danger to the proximal population to be minimized. We concentrate on understanding the significance and development of these seismic swarms as unrest develops at the volcano. In particular, analysis of accelerations in event rate, amplitude and energy rates released by seismicity prior to eruption suggests that these are important indicators of developing unrest. Real time analysis of these parameters simultaneously allows possible improvements to forecasting models. Although more time and computationally intense, cross correlation techniques applied to continuous seismicity prior to volcanic unrest scenarios allows all significant seismic events to be analysed, rather than only those which can be detected by an automated identification system. This may allow a more accurate forecast since all precursory seismicity can be taken into account. In addition, the classification of seismic events based on spectral characteristics may allow us to isolate individual types of signals which are responsible for certain types of unrest. In this way, we may be able to better forecast the type of eruption that may ensue, or at least some of its prevailing characteristics.

Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.

Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city.

PubMed

Rimaityte, Ingrida; Ruzgas, Tomas; Denafas, Gintaras; Racys, Viktoras; Martuzevicius, Dainius

2012-01-01

Forecasting of generation of municipal solid waste (MSW) in developing countries is often a challenging task due to the lack of data and selection of suitable forecasting method. This article aimed to select and evaluate several methods for MSW forecasting in a medium-scaled Eastern European city (Kaunas, Lithuania) with rapidly developing economics, with respect to affluence-related and seasonal impacts. The MSW generation was forecast with respect to the economic activity of the city (regression modelling) and using time series analysis. The modelling based on social-economic indicators (regression implemented in LCA-IWM model) showed particular sensitivity (deviation from actual data in the range from 2.2 to 20.6%) to external factors, such as the synergetic effects of affluence parameters or changes in MSW collection system. For the time series analysis, the combination of autoregressive integrated moving average (ARIMA) and seasonal exponential smoothing (SES) techniques were found to be the most accurate (mean absolute percentage error equalled to 6.5). Time series analysis method was very valuable for forecasting the weekly variation of waste generation data (r (2) > 0.87), but the forecast yearly increase should be verified against the data obtained by regression modelling. The methods and findings of this study may assist the experts, decision-makers and scientists performing forecasts of MSW generation, especially in developing countries.

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Implementation of weather stations at Ghanaian high schools

NASA Astrophysics Data System (ADS)

Pieron, M.

2012-04-01

The Trans-African Hydro-Meteorological Observatory (www.tahmo.org) is an initiative that aims to develop a dense weather observation network in Sub-Sahara Africa. The ambition is to have 20.000 low-cost innovative weather stations in place in 2015. An increased amount of weather data is locally required to provide stakeholders that are dependent on the weather, such as farmers and fishermen, with accurate forecasts. As a first proof of concept, showing that sensors can be built at costs lower than commercially available, a disdrometer was developed. In parallel with the design of the measurement instruments, a high school curriculum is developed that covers environmental sciences. In order to find out which requirements the TAHMO weather station and accompanying educational materials should meet for optimal use at Junior High Schools research was done at Ghanaian schools. Useful insights regarding the future African context of the weather station and requirements for an implementation strategy were obtained during workshops with teachers and students, visits to WMO observatories and case studies regarding use of educational materials. The poster presents the conclusions of this research, which is part of the bigger TAHMO framework.

Forecasting Influenza Epidemics in Hong Kong.

PubMed

Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y; Shaman, Jeffrey

2015-07-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

Forecasting Influenza Epidemics in Hong Kong

PubMed Central

Yang, Wan; Cowling, Benjamin J.; Lau, Eric H. Y.; Shaman, Jeffrey

2015-01-01

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions. PMID:26226185

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

NASA Technical Reports Server (NTRS)

Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.;

Tool for evaluating the evolution Space Weather Regional Warning Centers under the innovation point of view: the Case Study of the Embrace Space Weather Program Early Stages

NASA Astrophysics Data System (ADS)

Denardini, Clezio Marcos

2016-07-01

We have developed a tool for measuring the evolutional stage of the space weather regional warning centers using the approach of the innovative evolution starting from the perspective presented by Figueiredo (2009, Innovation Management: Concepts, metrics and experiences of companies in Brazil. Publisher LTC, Rio de Janeiro - RJ). It is based on measuring the stock of technological skills needed to perform a certain task that is (or should) be part of the scope of a space weather center. It also addresses the technological capacity for innovation considering the accumulation of technological and learning capabilities, instead of the usual international indices like number of registered patents. Based on this definition, we have developed a model for measuring the capabilities of the Brazilian Study and Monitoring Program Space Weather (Embrace), a program of the National Institute for Space Research (INPE), which has gone through three national stages of development and an international validation step. This program was created in 2007 encompassing competence from five divisions of INPE in order to carry out the data collection and maintenance of the observing system in space weather; to model processes of the Sun-Earth system; to provide real-time information and to forecast space weather; and provide diagnostic their effects on different technological systems. In the present work, we considered the issues related to the innovation of micro-processes inherent to the nature of the Embrace program, not the macro-economic processes, despite recognizing the importance of these. During the development phase, the model was submitted to five scientists/managers from five different countries member of the International Space Environment Service (ISES) who presented their evaluations, concerns and suggestions. It was applied to the Embrace program through an interview form developed to be answered by professional members of regional warning centers. Based on the returning answers we have derived a map that led to the discussions presented in this work. Among them, we point out that the advent of a regional warning center demanded some internal effort to perform the new tasks normally assigned to such center. However, the research activities potential stock available at INPE undoubtedly reduced the leap time among the evolutionary steps. In the other way, once the center was established it produced considerable increase in domestic skills of employees involved in this program. The other conclusions, reflections and recommendations are presented, including a personal reflection on learning processes for technological capability accumulation of what we understand to be present in the Embrace program. Contacting Author: C. M. Denardini (clezio.denardin@inpe.br)

A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2015-04-01

Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre-Processor approach currently used by National Weather Service River Forecast Centers in USA.

Forecasting residential electricity demand in provincial China.

PubMed

Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan

2017-03-01

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

Design and development of surface rainfall forecast products on GRAPES_MESO model

NASA Astrophysics Data System (ADS)

Zhili, Liu

2016-04-01

In this paper, we designed and developed the surface rainfall forecast products using medium scale GRAPES_MESO model precipitation forecast products. The horizontal resolution of GRAPES_MESO model is 10km*10km, the number of Grids points is 751*501, vertical levels is 26, the range is 70°E－145.15°E, 15°N－64.35 °N. We divided the basin into 7 major watersheds. Each watersheds was divided into a number of sub regions. There were 95 sub regions in all. Tyson polygon method is adopted in the calculation of surface rainfall. We used 24 hours forecast precipitation data of GRAPES_MESO model to calculate the surface rainfall. According to the site of information and boundary information of the 95 sub regions, the forecast surface rainfall of each sub regions was calculated. We can provide real-time surface rainfall forecast products every day. We used the method of fuzzy evaluation to carry out a preliminary test and verify about the surface rainfall forecast product. Results shows that the fuzzy score of heavy rain, rainstorm and downpour level forecast rainfall were higher, the fuzzy score of light rain level was lower. The forecast effect of heavy rain, rainstorm and downpour level surface rainfall were better. The rate of missing and empty forecast of light rainfall level surface rainfall were higher, so it's fuzzy score were lower.

Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

NASA Astrophysics Data System (ADS)

Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

2016-04-01

Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling

An interdisciplinary approach for earthquake modelling and forecasting

NASA Astrophysics Data System (ADS)

Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

2016-12-01

Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis

NASA Astrophysics Data System (ADS)

de Weger, Letty A.; Beerthuizen, Thijs; Hiemstra, Pieter S.; Sont, Jacob K.

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature ( R 2 = 0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures ( R 2 = 0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis.

PubMed

de Weger, Letty A; Beerthuizen, Thijs; Hiemstra, Pieter S; Sont, Jacob K

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities

NASA Technical Reports Server (NTRS)

Hesse, Michael; Pulkkinen, Antti; Zheng, Yihua; Maddox, Marlo; Berrios, David; Taktakishvili, Sandro; Kuznetsova, Masha; Chulaki, Anna; Lee, Hyesook; Mullinix, Rick;

KSC-06pd1285

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, media were able to meet members of the weather team who review data used for forecasts as part of a tour of the facility. The team will play a role in the July 1 launch of Space Shuttle Discovery on mission STS-121. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Forecasting seasonal outbreaks of influenza.

PubMed

Shaman, Jeffrey; Karspeck, Alicia

2012-12-11

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

Forecasting seasonal outbreaks of influenza

PubMed Central

Shaman, Jeffrey; Karspeck, Alicia

2012-01-01

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969

Multilayer Stock Forecasting Model Using Fuzzy Time Series

PubMed Central

Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

2014-01-01

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS. PMID:24605058

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.

NeXOS, developing and evaluating a new generation of insitu ocean observation systems.

NASA Astrophysics Data System (ADS)

Delory, Eric; del Rio, Joaquin; Golmen, Lars; Roar Hareide, Nils; Pearlman, Jay; Rolin, Jean-Francois; Waldmann, Christoph; Zielinski, Oliver

2017-04-01

Ocean biological, chemical or physical processes occur over widely varying scales in space and time: from micro- to kilometer scales, from less than seconds to centuries. While space systems supply important data and information, insitu data is necessary for comprehensive modeling and forecasting of ocean dynamics. Yet, collection of in-situ observation on these scales is inherently challenging and remains generally difficult and costly in time and resources. This paper address the innovations and significant developments for a new generation of insitu sensors in FP7 European Union project "Next generation, Cost- effective, Compact, Multifunctional Web Enabled Ocean Sensor Systems Empowering Marine, Maritime and Fisheries Management" or "NeXOS" for short. Optical and acoustics sensors are the focus of NeXOS but NeXOS moves beyond just sensors as systems that simultaneously address multiple objectives and applications are becoming increasingly important. Thus NeXOS takes a perspective of both sensors and sensor systems with significant advantages over existing observing capabilities via the implementation of innovations such as multiplatform integration, greater reliability through better antifouling management and greater sensor and data interoperability through use of OGC standards. This presentation will address the sensor system development and field-testing of the new NeXOS sensor systems. This is being done on multiple platforms including profiling floats, gliders, ships, buoys and subsea stations. The implementation of a data system based on SWE and PUCK furthers interoperability across measurements and platforms. This presentation will review the sensor system capabilities, the status of field tests and recommendations for long-term ocean monitoring.

Development of low-cost high-performance multispectral camera system at Banpil

NASA Astrophysics Data System (ADS)

Oduor, Patrick; Mizuno, Genki; Olah, Robert; Dutta, Achyut K.

2014-05-01

Banpil Photonics (Banpil) has developed a low-cost high-performance multispectral camera system for Visible to Short- Wave Infrared (VIS-SWIR) imaging for the most demanding high-sensitivity and high-speed military, commercial and industrial applications. The 640x512 pixel InGaAs uncooled camera system is designed to provide a compact, smallform factor to within a cubic inch, high sensitivity needing less than 100 electrons, high dynamic range exceeding 190 dB, high-frame rates greater than 1000 frames per second (FPS) at full resolution, and low power consumption below 1W. This is practically all the feature benefits highly desirable in military imaging applications to expand deployment to every warfighter, while also maintaining a low-cost structure demanded for scaling into commercial markets. This paper describes Banpil's development of the camera system including the features of the image sensor with an innovation integrating advanced digital electronics functionality, which has made the confluence of high-performance capabilities on the same imaging platform practical at low cost. It discusses the strategies employed including innovations of the key components (e.g. focal plane array (FPA) and Read-Out Integrated Circuitry (ROIC)) within our control while maintaining a fabless model, and strategic collaboration with partners to attain additional cost reductions on optics, electronics, and packaging. We highlight the challenges and potential opportunities for further cost reductions to achieve a goal of a sub-$1000 uncooled high-performance camera system. Finally, a brief overview of emerging military, commercial and industrial applications that will benefit from this high performance imaging system and their forecast cost structure is presented.

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.

Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

PubMed

Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

2017-03-15

As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Forecast Based Financing for Managing Weather and Climate Risks to Reduce Potential Disaster Impacts

NASA Astrophysics Data System (ADS)

Arrighi, J.

2017-12-01

There is a critical window of time to reduce potential impacts of a disaster after a forecast for heightened risk is issued and before an extreme event occurs. The concept of Forecast-based Financing focuses on this window of opportunity. Through advanced preparation during system set-up, tailored methodologies are used to 1) analyze a range of potential extreme event forecasts, 2) identify emergency preparedness measures that can be taken when factoring in forecast lead time and inherent uncertainty and 3) develop standard operating procedures that are agreed on and tied to guaranteed funding sources to facilitate emergency measures led by the Red Cross or government actors when preparedness measures are triggered. This presentation will focus on a broad overview of the current state of theory and approaches used in developing a forecast-based financing systems - with a specific focus on hydrologic events, case studies of success and challenges in various contexts where this approach is being piloted, as well as what is on the horizon to be further explored and developed from a research perspective as the application of this approach continues to expand.

Precipitation and floodiness: forecasts of flood hazard at the regional scale

NASA Astrophysics Data System (ADS)

Stephens, Liz; Day, Jonny; Pappenberger, Florian; Cloke, Hannah

2016-04-01

In 2008, a seasonal forecast of an increased likelihood of above-normal rainfall in West Africa led the Red Cross to take early humanitarian action (such as prepositioning of relief items) on the basis that this forecast implied heightened flood risk. However, there are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard, so in this presentation we use a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010) to quantify this non-linearity. Using these data, we introduce the concept of floodiness to measure the incidence of floods over a large area, and quantify the link between monthly precipitation, river discharge and floodiness anomalies. Our analysis shows that floodiness is not well correlated with precipitation, demonstrating the problem of using seasonal precipitation forecasts as a proxy for forecasting flood hazard. This analysis demonstrates the value of developing hydrometeorological forecasts of floodiness for decision-makers. As a result, we are now working with the European Centre for Medium-Range Weather Forecasts and the Joint Research Centre, as partners of the operational Global Flood Awareness System (GloFAS), to implement floodiness forecasts in real-time.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Evaluating Observation Influence on Regional Water Budgets in Reanalyses

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Chern, Jiun-Dar; Mocko, David; Robertson, Franklin R.; daSilva, Arlindo M.

2014-01-01

The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model, or perhaps inconsistencies in the observing system and its assimilation. In the MERRA reanalysis, an area of long term moisture flux divergence over land has been identified over the Central United States. Here, we evaluate the water vapor budget in this region, taking advantage of two unique features of the MERRA diagnostic output; 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output data set of the assimilated observations and their innovations (e.g. forecast departures). In the Central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRAs Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 06Z and 18Z analysis cycles. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSUA (mainly window channels) and AIRS. This effort also shows the complexities of the observing system, and the reactions of the regional water budgets in reanalyses to the assimilated observations.

Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

NASA Technical Reports Server (NTRS)

Garner, Gregory G.; Thompson, Anne M.

2013-01-01

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for

Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

NASA Astrophysics Data System (ADS)

Jordan, T. H.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Jackson, D. D.; Rhoades, D. A.; Zechar, J. D.; Marzocchi, W.

2016-12-01

The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 442 models under evaluation. The California testing center, started by SCEC, Sept 1, 2007, currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. Our tests are now based on the hypocentral locations and magnitudes of cataloged earthquakes, but we plan to test focal mechanisms, seismic hazard models, ground motion forecasts, and finite rupture forecasts as well. We have increased computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model, introduced Bayesian ensemble models, and implemented support for non-Poissonian simulation-based forecasts models. We are currently developing formats and procedures to evaluate externally hosted forecasts and predictions. CSEP supports the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. We found that earthquakes as small as magnitude 2.5 provide important information on subsequent earthquakes larger than magnitude 5. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence showed that some physics-based and hybrid models outperform catalog-based (e.g., ETAS) models. This experiment also demonstrates the ability of the CSEP infrastructure to support retrospective forecast testing. Current CSEP development activities include adoption of the Comprehensive Earthquake Catalog (ComCat) as an authorized data source, retrospective testing of simulation-based forecasts, and support for additive ensemble methods. We describe the open-source CSEP software that is available to researchers as they develop their forecast models. We also discuss how CSEP procedures are being adapted to intensity and ground motion prediction experiments as well as hazard model testing.

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

NASA Astrophysics Data System (ADS)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

Evaluation of the product ratio coherent model in forecasting mortality rates and life expectancy at births by States

NASA Astrophysics Data System (ADS)

Shair, Syazreen Niza; Yusof, Aida Yuzi; Asmuni, Nurin Haniah

2017-05-01

Coherent mortality forecasting models have recently received increasing attention particularly in their application to sub-populations. The advantage of coherent models over independent models is the ability to forecast a non-divergent mortality for two or more sub-populations. One of the coherent models was recently developed by [1] known as the product-ratio model. This model is an extension version of the functional independent model from [2]. The product-ratio model has been applied in a developed country, Australia [1] and has been extended in a developing nation, Malaysia [3]. While [3] accounted for coherency of mortality rates between gender and ethnic group, the coherency between states in Malaysia has never been explored. This paper will forecast the mortality rates of Malaysian sub-populations according to states using the product ratio coherent model and its independent version— the functional independent model. The forecast accuracies of two different models are evaluated using the out-of-sample error measurements— the mean absolute forecast error (MAFE) for age-specific death rates and the mean forecast error (MFE) for the life expectancy at birth. We employ Malaysian mortality time series data from 1991 to 2014, segregated by age, gender and states.

Research activities at the Australian Bureau of Meteorology for the regional ionospheric specification and forecasting

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael

2016-07-01

The Australian Space Forecast Centre (ASFC) provides space weather forecasts to a diverse group of customers. Space Weather Services (SWS) within the Australian Bureau of Meteorology is focussed both on developing tailored products and services for the key customer groups, and supporting ASFC operations. Research in SWS is largely centred on the development of data-driven models using a range of solar-terrestrial data. This paper will cover some data requirements , approaches and recent SWS activities for data driven modelling with a focus on the regional Ionospheric specification and forecasting.

Observation Impact over the Antarctic During the Concordiasi Field Campaign

NASA Technical Reports Server (NTRS)

Boullot, Nathalie; Rabier, Florence; Langland, Rolf; Gelaro, Ron; Cardinali, Carla; Guidard, Vincent; Bauer, Peter; Doerenbecher, Alexis

2014-01-01

The impact of observations on analysis uncertainty and forecast performance was investigated for Austral Spring 2010 over the Southern polar area for four different systems (NRL, GMAO, ECMWF and Meteo-France), at the time of the Concordiasi field experiment. The largest multi model variance in 500 hPa height analyses is found in the southern sub-Antarctic oceanic region, where there are strong atmospheric dynamics, rapid forecast error growth, and fewer upper air wind observation data to constrain the analyses. In terms of data impact the most important observation components are shown to be AMSU, IASI, AIRS, GPS-RO, radiosonde, surface and atmospheric motion vector observations. For sounding data, radiosondes and dropsondes, one can note a large impact of temperature at low levels and a large impact of wind at high levels. Observing system experiments using the Concordiasi dropsondes show a large impact of the observations over the Antarctic plateau extending to lower latitudes with the forecast range, with a large impact around 50 to 70deg South. These experiments indicate there is a potential benefit of better using radiance data over land and sea-ice and innovative atmospheric motion vectors obtained from a combination of various satellites to fill the current data gaps and improve NWP in this region.

A plan for the economic assessment of the benefits of improved meteorological forecasts

NASA Technical Reports Server (NTRS)

Bhattacharyya, R.; Greenberg, J.

1975-01-01

Benefit-cost relationships for the development of meteorological satellites are outlined. The weather forecast capabilities of the various weather satellites (Tiros, SEOS, Nimbus) are discussed, and the development of additional satellite systems is examined. A rational approach is development that leads to the establishment of the economic benefits which may result from the utilization of meteorological satellite data. The economic and social impacts of improved weather forecasting for industries and resources management are discussed, and significant weather sensitive industries are listed.

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.

AN OPERATIONAL EVALUATION OF THE ETA-CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in collaboration with the Environmental Protection Agency (EPA), are developing an Air Quality Forecasting Program that will eventually result in an operational Nationwide Air Quality Forecasting System. The initial pha...

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

NASA Technical Reports Server (NTRS)

Lambert, Winfred; Wheeler, Mark; Roeder, William

2005-01-01

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Air Pollution Forecasts: An Overview

PubMed Central

Bai, Lu; Wang, Jianzhou; Lu, Haiyan

2018-01-01

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

Air Pollution Forecasts: An Overview.

PubMed

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

2018-04-17

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

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

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

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

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

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Real-time demonstration and evaluation of over-the-loop short to medium-range ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Newman, A. J.; Nijssen, B.; Clark, M. P.; Gangopadhyay, S.; Arnold, J. R.

2015-12-01

The US National Weather Service River Forecasting Centers are beginning to operationalize short range to medium range ensemble predictions that have been in development for several years. This practice contrasts with the traditional single-value forecast practice at these lead times not only because the ensemble forecasts offer a basis for quantifying forecast uncertainty, but also because the use of ensembles requires a greater degree of automation in the forecast workflow than is currently used. For instance, individual ensemble member forcings cannot (practically) be manually adjusted, a step not uncommon with the current single-value paradigm, thus the forecaster is required to adopt a more 'over-the-loop' role than before. The relative lack of experience among operational forecasters and forecast users (eg, water managers) in the US with over-the-loop approaches motivates the creation of a real-time demonstration and evaluation platform for exploring the potential of over-the-loop workflows to produce usable ensemble short-to-medium range forecasts, as well as long range predictions. We describe the development and early results of such an effort by a collaboration between NCAR and the two water agencies, the US Army Corps of Engineers and the US Bureau of Reclamation. Focusing on small to medium sized headwater basins around the US, and using multi-decade series of ensemble streamflow hindcasts, we also describe early results, assessing the skill of daily-updating, over-the-loop forecasts driven by a set of ensemble atmospheric outputs from the NCEP GEFS for lead times from 1-15 days.

Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

NASA Astrophysics Data System (ADS)

Dixon, Kenneth

A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.

Extended Range Prediction of Indian Summer Monsoon: Current status

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Travel demand forecasting models: a comparison of EMME/2 and QUR II using a real-world network.

DOT National Transportation Integrated Search

2000-10-01

In order to automate the travel demand forecasting process in urban transportation planning, a number of : commercial computer based travel demand forecasting models have been developed, which have provided : transportation planners with powerful and...

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Reale, O.; Atlas, R.; Jusem, J. C.

2004-01-01

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

Seasonal forecasting of dolphinfish distribution in eastern Australia to aid recreational fishers and managers

NASA Astrophysics Data System (ADS)

Brodie, Stephanie; Hobday, Alistair J.; Smith, James A.; Spillman, Claire M.; Hartog, Jason R.; Everett, Jason D.; Taylor, Matthew D.; Gray, Charles A.; Suthers, Iain M.

2017-06-01

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish (Coryphaena hippurus), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

Forecast of dengue incidence using temperature and rainfall.

PubMed

Hii, Yien Ling; Zhu, Huaiping; Ng, Nawi; Ng, Lee Ching; Rocklöv, Joacim

2012-01-01

An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.

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.

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

Objective Lightning Forecasting at Kennedy Space Center/Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

NASA Technical Reports Server (NTRS)

Lambert, Winifred; Wheeler, Mark

2004-01-01

The 45th Weather Squadron (45 WS) forecasters at Cape Canaveral Air Force Station (CCAFS) in Florida include a probability of thunderstorm occurrence in their daily morning briefings. This information is used by personnel involved in determining the possibility of violating Launch Commit Criteria, evaluating Flight Rules for the Space Shuttle, and daily planning for ground operation activities on Kennedy Space Center (KSC)/CCAFS. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data. The forecasters requested that a lightning probability forecast tool based on statistical analysis of historical warm-season (May - September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The tool is a set of statistical lightning forecast equations that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season. This study used 15 years (1989-2003) of warm season data to develop the objective forecast equations. The local CCAFS 1000 UTC sounding was used to calculate stability parameters for equation predictors. The Cloud-to-Ground Lightning Surveillance System (CGLSS) data were used to determine lightning occurrence for each day. The CGLSS data have been found to be more reliable indicators of lightning in the area than surface observations through local informal analyses. This work was based on the results from two earlier research projects. Everitt (1999) used surface observations and rawinsonde data to develop logistic regression equations that forecast the daily thunderstorm probability at CCAFS. The Everitt (1999) equations showed an improvement in skill over the Neumann-Pfeffer thunderstorm index (Neumann 1971), which uses multiple linear regression, and also persistence and climatology forecasts. Lericos et al. (2002) developed lightning distributions over the Florida peninsula based on specific flow regimes. The flow regimes were inferred from the average wind direction in the 1000-700 mb layer at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), Florida, and the lightning data were from the National Lightning Detection Network. The results suggested that the daily flow regime may be an important predictor of lightning occurrence on KSC/CCAFS.

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.

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.

Atmospheric and oceanographic research review, 1978. [global weather, ocean/air interactions, and climate

NASA Technical Reports Server (NTRS)

1978-01-01

Research activities related to global weather, ocean/air interactions, and climate are reported. The global weather research is aimed at improving the assimilation of satellite-derived data in weather forecast models, developing analysis/forecast models that can more fully utilize satellite data, and developing new measures of forecast skill to properly assess the impact of satellite data on weather forecasting. The oceanographic research goal is to understand and model the processes that determine the general circulation of the oceans, focusing on those processes that affect sea surface temperature and oceanic heat storage, which are the oceanographic variables with the greatest influence on climate. The climate research objective is to support the development and effective utilization of space-acquired data systems in climate forecast models and to conduct sensitivity studies to determine the affect of lower boundary conditions on climate and predictability studies to determine which global climate features can be modeled either deterministically or statistically.

Newsvendor problem under complete uncertainty: a case of innovative products.

PubMed

Gaspars-Wieloch, Helena

2017-01-01

The paper presents a new scenario-based decision rule for the classical version of the newsvendor problem (NP) under complete uncertainty (i.e. uncertainty with unknown probabilities). So far, NP has been analyzed under uncertainty with known probabilities or under uncertainty with partial information (probabilities known incompletely). The novel approach is designed for the sale of new, innovative products, where it is quite complicated to define probabilities or even probability-like quantities, because there are no data available for forecasting the upcoming demand via statistical analysis. The new procedure described in the contribution is based on a hybrid of Hurwicz and Bayes decision rules. It takes into account the decision maker's attitude towards risk (measured by coefficients of optimism and pessimism) and the dispersion (asymmetry, range, frequency of extremes values) of payoffs connected with particular order quantities. It does not require any information about the probability distribution.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

Scenario analysis and path selection of low-carbon transformation in China based on a modified IPAT model.

PubMed

Chen, Liang; Yang, Zhifeng; Chen, Bin

2013-01-01

This paper presents a forecast and analysis of population, economic development, energy consumption and CO2 emissions variation in China in the short- and long-term steps before 2020 with 2007 as the base year. The widely applied IPAT model, which is the basis for calculations, projections, and scenarios of greenhouse gases (GHGs) reformulated as the Kaya equation, is extended to analyze and predict the relations between human activities and the environment. Four scenarios of CO2 emissions are used including business as usual (BAU), energy efficiency improvement scenario (EEI), low carbon scenario (LC) and enhanced low carbon scenario (ELC). The results show that carbon intensity will be reduced by 40-45% as scheduled and economic growth rate will be 6% in China under LC scenario by 2020. The LC scenario, as the most appropriate and the most feasible scheme for China's low-carbon development in the future, can maximize the harmonious development of economy, society, energy and environmental systems. Assuming China's development follows the LC scenario, the paper further gives four paths of low-carbon transformation in China: technological innovation, industrial structure optimization, energy structure optimization and policy guidance.

Scenario Analysis and Path Selection of Low-Carbon Transformation in China Based on a Modified IPAT Model

PubMed Central

Chen, Liang; Yang, Zhifeng; Chen, Bin

2013-01-01

This paper presents a forecast and analysis of population, economic development, energy consumption and CO2 emissions variation in China in the short- and long-term steps before 2020 with 2007 as the base year. The widely applied IPAT model, which is the basis for calculations, projections, and scenarios of greenhouse gases (GHGs) reformulated as the Kaya equation, is extended to analyze and predict the relations between human activities and the environment. Four scenarios of CO2 emissions are used including business as usual (BAU), energy efficiency improvement scenario (EEI), low carbon scenario (LC) and enhanced low carbon scenario (ELC). The results show that carbon intensity will be reduced by 40–45% as scheduled and economic growth rate will be 6% in China under LC scenario by 2020. The LC scenario, as the most appropriate and the most feasible scheme for China’s low-carbon development in the future, can maximize the harmonious development of economy, society, energy and environmental systems. Assuming China's development follows the LC scenario, the paper further gives four paths of low-carbon transformation in China: technological innovation, industrial structure optimization, energy structure optimization and policy guidance. PMID:24204922

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

PubMed Central

Zhu, Qing; Zou, Yingchao; Lai, Kin Keung

2014-01-01

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614

Time series analysis for psychological research: examining and forecasting change

PubMed Central

Jebb, Andrew T.; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials. PMID:26106341

Time series analysis for psychological research: examining and forecasting change.

PubMed

Jebb, Andrew T; Tay, Louis; Wang, Wei; Huang, Qiming

2015-01-01

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

Are We Reaching the Limits of Homo sapiens?

PubMed Central

Marck, Adrien; Antero, Juliana; Berthelot, Geoffroy; Saulière, Guillaume; Jancovici, Jean-Marc; Masson-Delmotte, Valérie; Boeuf, Gilles; Spedding, Michael; Le Bourg, Éric; Toussaint, Jean-François

2017-01-01

Echoing scientific and industrial progress, the Twentieth century was an unprecedented period of improvement for human capabilities and performances, with a significant increase in lifespan, adult height, and maximal physiological performance. Analyses of historical data show a major slow down occurring in the most recent years. This triggered large and passionate debates in the academic scene within multiple disciplines; as such an observation could be interpreted as our upper biological limits. Such a new phase of human history may be related to structural and functional limits determined by long term evolutionary constraints, and the interaction between complex systems and their environment. In this interdisciplinary approach, we call into question the validity of subsequent forecasts and projections through innovative and related biomarkers such as sport, lifespan, and height indicators. We set a theoretical framework based on biological and environmental relevance rather than using a typical single-variable forecasting approach. As demonstrated within the article, these new views will have major social, economical, and political implications. PMID:29123486

Are We Reaching the Limits of Homo sapiens?

PubMed

Marck, Adrien; Antero, Juliana; Berthelot, Geoffroy; Saulière, Guillaume; Jancovici, Jean-Marc; Masson-Delmotte, Valérie; Boeuf, Gilles; Spedding, Michael; Le Bourg, Éric; Toussaint, Jean-François

2017-01-01

Echoing scientific and industrial progress, the Twentieth century was an unprecedented period of improvement for human capabilities and performances, with a significant increase in lifespan, adult height, and maximal physiological performance. Analyses of historical data show a major slow down occurring in the most recent years. This triggered large and passionate debates in the academic scene within multiple disciplines; as such an observation could be interpreted as our upper biological limits. Such a new phase of human history may be related to structural and functional limits determined by long term evolutionary constraints, and the interaction between complex systems and their environment. In this interdisciplinary approach, we call into question the validity of subsequent forecasts and projections through innovative and related biomarkers such as sport, lifespan, and height indicators. We set a theoretical framework based on biological and environmental relevance rather than using a typical single-variable forecasting approach. As demonstrated within the article, these new views will have major social, economical, and political implications.

Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

PubMed

Zhu, Qing; He, Kaijian; Zou, Yingchao; Lai, Kin Keung

2014-01-01

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

Real-time short-term forecast of water inflow into Bureyskaya reservoir

NASA Astrophysics Data System (ADS)

Motovilov, Yury

2017-04-01

During several recent years, a methodology for operational optimization in hydrosystems including forecasts of the hydrological situation has been developed on example of Burea reservoir. The forecasts accuracy improvement of the water inflow into the reservoir during planning of water and energy regime was one of the main goals for implemented research. Burea river is the second left largest Amur tributary after Zeya river with its 70.7 thousand square kilometers watershed and 723 km-long river course. A variety of natural conditions - from plains in the southern part to northern mountainous areas determine a significant spatio-temporal variability in runoff generation patterns and river regime. Bureyskaya hydropower plant (HPP) with watershed area 65.2 thousand square kilometers is a key station in the Russian Far Eastern energy system providing its reliable operation. With a spacious reservoir, Bureyskaya HPP makes a significant contribution to the protection of the Amur region from catastrophic floods. A physically-based distributed model of runoff generation based on the ECOMAG (ECOlogical Model for Applied Geophysics) hydrological modeling platform has been developed for the Burea River basin. The model describes processes of interception of rainfall/snowfall by the canopy, snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface, ground and river flow. The governing model's equations are derived from integration of the basic hydro- and thermodynamics equations of water and heat vertical transfer in snowpack, frozen/unfrozen soil, horizontal water flow under and over catchment slopes, etc. The model setup for Bureya river basin included watershed and river network schematization with GIS module by DEM analysis, meteorological time-series preparation, model calibration and validation against historical observations. The results showed good model performance as compared to observed inflow data into the Bureya reservoir and high diagnostic potential of data-modeling system of the runoff formation. With the use of this system the following flowchart for short-range forecasting inflow into Bureyskoe reservoir and forecast correction technique using continuously updated hydrometeorological data has been developed: 1 - Daily renewal of weather observations and forecasts database via the Internet; 2 - Daily runoff calculation from the beginning of the current year to current date is conducted; 3 - Short-range (up to 7 days) forecast is generated based on weather forecast. The idea underlying the model assimilation of newly obtained hydro meteorological information to adjust short-range hydrological forecasts lies in the assumption of the forecast errors inertia. Then the difference between calculated and observed streamflow at the forecast release date is "scattered" with specific weights to calculated streamflow for the forecast lead time. During 2016 this forecasts method of the inflow into the Bureyskaya reservoir up to 7 days is tested in online mode. Satisfactory evaluated short-range inflow forecast success rate is obtained. Tests of developed method have shown strong sensitivity to the results of short-term precipitation forecasts.

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-09-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.

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.

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

Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change

NASA Astrophysics Data System (ADS)

Storer, Luke N.; Williams, Paul D.; Gill, Philip G.

2018-03-01

Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

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.

Current and Potential Use of Technology Forecasting Tools in the Federal Government

DTIC Science & Technology

2016-03-01

that is near or at the point of late-stage trials. This could allow for earlier research investment in useful health care technologies or innovations ...pursuant to the copyright license under the clause at DFARS 252.227-7013 (a)(16) [Jun 2013]. I N S T I T U T E F O R D E F E N S E A N A L Y S E S IDA...long-term technology investments , and • Understanding economic and policy implications of the evolution and global availability of commercial

Current and Future Opportunities for Wind Power in the Southeast

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

Tinnesand, Heidi; Roberts, Owen; Lantz, Eric

This presentation discusses future wind opportunities in the Southeast including factors such as changes in wind turbine technology, historical innovation trends, and forecast demand growth among regions. The presentation covers the current status of wind in the United States at 80-m hub height and the near-future outlook with a hub height at 110 to 140 meters. Future cost reductions in 2030 and beyond are also explored. Heidi Tinnesand presented this information to a utility advisory group meeting in Charlotte, North Carolina, on October 5, 2016.

Near real time wind energy forecasting incorporating wind tunnel modeling

NASA Astrophysics Data System (ADS)

Lubitz, William David

A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power production of a wind farm in the Altamont Pass, California, USA from 12 to 48 hours in advance. It is of modular construction and designed to also allow diagnostic forecasting using archived RSF data, thereby allowing different methods of completing each forecasting step to be tested and compared using the same input data. Wind-tunnel investigations of the effect of wind direction and hill geometry on wind speed-up above a hill were conducted. Field data from an Altamont Pass, California site was used to evaluate several speed-up prediction algorithms, both with and without wind direction adjustment. These algorithms were found to be of limited usefulness for the complex terrain case evaluated. Wind-tunnel and numerical simulation-based methods were developed for determining a wind farm power curve (the relation between meteorological conditions at a point in the wind farm and the power production of the wind farm). Both methods, as well as two methods based on fits to historical data, ultimately showed similar levels of accuracy: mean absolute errors predicting power production of 5 to 7 percent of the wind farm power capacity. The downscaling of RSF forecast data to the wind farm was found to be complicated by the presence of complex terrain. Poor results using the geostrophic drag law and regression methods motivated the development of a database search method that is capable of forecasting not only wind speeds but also power production with accuracy better than persistence.

Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

NASA Astrophysics Data System (ADS)

Manukalo, V.

2012-12-01

Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of current information, forecasts and warnings to consumers automatically. Besides scientific and technical issues the implementation of these objectives requires solution of a number of organizational issues. Thus, as a result of the increased complexity of types of hydrometeorological data and in order to develop forecasting methods, a reconsideration of meteorological and hydrological measurement networks should be carried out. The "optimal density of measuring networks" is proposed taking into account principal terms: a) minimizing an uncertainty in characterizing the spacial distribution of hydrometeorological parameters; b) minimizing the Total Life Cycle Cost of creation and maintenance of measurement networks. Much attention will be given to training Ukrainian disaster management authorities from the Ministry of Emergencies and the Water Management Agency to identify the flood hazard risk level and to indicate the best protection measures on the basis of continuous monitoring and forecasts of evolution of meteorological and hydrological conditions in the river basin.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

2017-12-01

Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

A review and update of the Virginia Department of Transportation's cash flow forecasting model : interim report.

DOT National Transportation Integrated Search

1995-01-01

The Virginia Department of Transportation uses a cash flow forecasting model to predict operations expenditures by month. Components of this general forecasting model estimate line items in the VDOT budget. The cash flow model was developed in the ea...

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

An empirical investigation on different methods of economic growth rate forecast and its behavior from fifteen countries across five continents

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Hock-Eam, Lim

2012-09-01

Our empirical results show that we can predict GDP growth rate more accurately in continent with fewer large economies, compared to smaller economies like Malaysia. This difficulty is very likely positively correlated with subsidy or social security policies. The stage of economic development and level of competiveness also appears to have interactive effects on this forecast stability. These results are generally independent of the forecasting procedures. Countries with high stability in their economic growth, forecasting by model selection is better than model averaging. Overall forecast weight averaging (FWA) is a better forecasting procedure in most countries. FWA also outperforms simple model averaging (SMA) and has the same forecasting ability as Bayesian model averaging (BMA) in almost all countries.

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

The Forecast Interpretation Tool—a Monte Carlo technique for blending climatic distributions with probabilistic forecasts

USGS Publications Warehouse

Husak, Gregory J.; Michaelsen, Joel; Kyriakidis, P.; Verdin, James P.; Funk, Chris; Galu, Gideon

2011-01-01

Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.

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

NASA Astrophysics Data System (ADS)

Shafiee-Jood, M.; Cai, X.

2017-12-01

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

Objective Lightning Forecasting at Kennedy Space Center and Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

NASA Technical Reports Server (NTRS)

Lambert, Winfred; Wheeler, Mark; Roeder, William

2005-01-01

The 45th Weather Squadron (45 WS) at Cape Canaveral Air-Force Station (CCAFS)ln Florida issues a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts. This information is used for general planning of operations at CCAFS and Kennedy Space Center (KSC). These facilities are located in east-central Florida at the east end of a corridor known as 'Lightning Alley', an indication that lightning has a large impact on space-lift operations. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data and an objective forecast tool developed over 30 years ago. The 45 WS requested that a new lightning probability forecast tool based on statistical analysis of more recent historical warm season (May-September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The resulting tool is a set of statistical lightning forecast equations, one for each month of the warm season, that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season.

Satellite based Ocean Forecasting, the SOFT project

NASA Astrophysics Data System (ADS)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 3

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 3 of the four major tasks included in the study. Task 3 compares flight plans developed on the Suitland forecast with actual data observed by the aircraft (and averaged over 10 degree segments). The results show that the average difference between the forecast and observed wind speed is 9 kts. without considering direction, and the average difference in the component of the forecast wind parallel to the direction of the observed wind is 13 kts. - both indicating that the Suitland forecast underestimates the wind speeds. The Root Mean Square (RMS) vector error is 30.1 kts. The average absolute difference in direction between the forecast and observed wind is 26 degrees and the temperature difference is 3 degree Centigrade. These results indicate that the forecast model as well as the verifying analysis used to develop comparison flight plans in Tasks 1 and 2 is a limiting factor and that the average potential fuel savings or penalty are up to 3.6 percent depending on the direction of flight.

Ocean state and uncertainty forecasts using HYCOM with Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, Mozheng; Hogan, Pat; Rowley, Clark; Smedstad, Ole-Martin; Wallcraft, Alan; Penny, Steve

2017-04-01

An ensemble forecast system based on the US Navy's operational HYCOM using Local Ensemble Transfer Kalman Filter (LETKF) technology has been developed for ocean state and uncertainty forecasts. One of the advantages is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates the operational observations using ensemble method. The background covariance during this assimilation process is supplied with the ensemble, thus it avoids the difficulty of developing tangent linear and adjoint models for 4D-VAR from the complicated hybrid isopycnal vertical coordinate in HYCOM. Another advantage is that the ensemble system provides the valuable uncertainty estimate corresponding to every state forecast from HYCOM. Uncertainty forecasts have been proven to be critical for the downstream users and managers to make more scientifically sound decisions in numerical prediction community. In addition, ensemble mean is generally more accurate and skilful than the single traditional deterministic forecast with the same resolution. We will introduce the ensemble system design and setup, present some results from 30-member ensemble experiment, and discuss scientific, technical and computational issues and challenges, such as covariance localization, inflation, model related uncertainties and sensitivity to the ensemble size.

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Station

DTIC Science & Technology

2010-09-30

and climate forecasting and use of satellite data assimilation for model evaluation. He is a task leader on another NSF_EPSCoR project for the...1 DISTRIBUTION STATEMENT A: Approved for public release; distribution is unlimited. Data Analysis, Modeling, and Ensemble Forecasting to...observations including remotely sensed data . OBJECTIVES The main objectives of the study are: 1) to further develop, test, and continue twice daily

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Ahmad Kamaruddin, Saadi Bin; Md Ghani, Nor Azura; Mohamed Ramli, Norazan

2013-04-01

The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsular Malaysia, comprising price indexes of aggregate, sand, steel reinforcement, ready mix concrete, bricks and partition, roof material, floor and wall finishes, ceiling, plumbing materials, sanitary fittings, paint, glass, steel and metal sections, timber and plywood. At the end of the study, it was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating unitary charges price indexes in every states in central region Peninsular Malaysia based on the Root Mean Squared Errors, where the values for both estimation and evaluation sets were approximately zero and highly significant at p < 0.01. Therefore, artificial neural network is sufficient to forecast construction materials price indexes in Malaysia. The estimated price indexes of construction materials will contribute significantly to the value for money of PFI as well as towards Malaysian economical growth.

Transition from Research to Operations: Assessing Value of Experimental Forecast Products within the NWSFO Environment

NASA Technical Reports Server (NTRS)

Lapenta, William M.; Wohlman, Richard; Bradshaw, Tom; Burks, Jason; Jedlovec, Gary; Goodman, Steve; Darden, Chris; Meyer, Paul

2003-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NWS forecast operations and decision-making. To meet long-term program expectations, it is not sufficient simply to give forecasters sophisticated workstations or new forecast products without fully assessing the ways in which they will be utilized. Close communication must be established between the research and operational communities so that developers have a complete understanding of user needs. In turn, forecasters must obtain a more comprehensive knowledge of the modeling and sensing tools available to them. A major goal of the SPoRT Program is to develop metrics and conduct assessment studies with NWS forecasters to evaluate the impacts and benefits of ESE experimental products on forecast skill. At a glance the task seems relatively straightforward. However, performing assessment of experimental products in an operational environment is demanding. Given the tremendous time constraints placed on NWS forecasters, it is imperative that forecaster input be obtained in a concise unobtrusive manor. Great care must also be taken to ensure that forecasters understand their participation will eventually benefit them and WFO operations in general. Two requirements of the assessment plan developed under the SPoRT activity are that it 1) Can be implemented within the WFO environment; and 2) Provide tangible results for BOTH the research and operational communities. Supplemental numerical quantitative precipitation forecasts (QPF) were chosen as the first experimental SPoRT product to be evaluated during a Pilot Assessment Program conducted 1 May 2003 within the Huntsville AL National Weather Service Forecast Office. Forecast time periods were broken up into six- hour bins ranging from zero to twenty-four hours. Data were made available for display in AWIPS on an operational basis so they could be efficiently incorporated into the forecast process. The methodology used to assess the value of experimental QPFs compared to available operational products is best described as a three-tier approach involving both forecasters and research scientists. Tier-one is a web-based survey completed by duty forecasters on the aviation and public desks. The survey compiles information on how the experimental product was used in the forecast decision making process. Up to 6 responses per twenty-four hours can be compiled during a precipitation event. Tier-two consists of an event post mortem and experimental product assessment performed daily by the NASA/NWS Liaison. Tier-three is a detailed breakdown/analysis of specific events targeted by either the NWS SO0 or SPoRT team members. The task is performed by both NWS and NASA research scientists and may be conducted once every couple of months. The findings from the Pilot Assessment Program will be reported at the meeting.

A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...

Predicting the US Drought Monitor (USDM) using precipitation, soil noisture, and evapotranspiration anomalies, Part II: Intraseasonal drought intensification forecasts

USDA-ARS?s Scientific Manuscript database

Probabilistic forecasts of US Drought Monitor (USDM) intensification over two, four and eight week time periods are developed based on recent anomalies in precipitation, evapotranspiration and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. Wh...

School District Enrollment Projections: A Comparison of Three Methods.

ERIC Educational Resources Information Center

Pettibone, Timothy J.; Bushan, Latha

This study assesses three methods of forecasting school enrollments: the cohort-sruvival method (grade progression), the statistical forecasting procedure developed by the Statistical Analysis System (SAS) Institute, and a simple ratio computation. The three methods were used to forecast school enrollments for kindergarten through grade 12 in a…

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.

Solar Resource Assessment with Sky Imagery and a Virtual Testbed for Sky Imager Solar Forecasting

NASA Astrophysics Data System (ADS)

Kurtz, Benjamin Bernard

In recent years, ground-based sky imagers have emerged as a promising tool for forecasting solar energy on short time scales (0 to 30 minutes ahead). Following the development of sky imager hardware and algorithms at UC San Diego, we present three new or improved algorithms for sky imager forecasting and forecast evaluation. First, we present an algorithm for measuring irradiance with a sky imager. Sky imager forecasts are often used in conjunction with other instruments for measuring irradiance, so this has the potential to decrease instrumentation costs and logistical complexity. In particular, the forecast algorithm itself often relies on knowledge of the current irradiance which can now be provided directly from the sky images. Irradiance measurements are accurate to within about 10%. Second, we demonstrate a virtual sky imager testbed that can be used for validating and enhancing the forecast algorithm. The testbed uses high-quality (but slow) simulations to produce virtual clouds and sky images. Because virtual cloud locations are known, much more advanced validation procedures are possible with the virtual testbed than with measured data. In this way, we are able to determine that camera geometry and non-uniform evolution of the cloud field are the two largest sources of forecast error. Finally, with the assistance of the virtual sky imager testbed, we develop improvements to the cloud advection model used for forecasting. The new advection schemes are 10-20% better at short time horizons.

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

A Decision Support System for effective use of probability forecasts

NASA Astrophysics Data System (ADS)

De Kleermaeker, Simone; Verkade, Jan

2013-04-01

Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Multi-Spacecraft Data Assimilation and Reanalysis During the THEMIS and Van Allen Probes Era

NASA Astrophysics Data System (ADS)

Kellerman, A. C.; Shprits, Y.; Kondrashov, D. A.; Podladchikova, T.; Drozdov, A.; Subbotin, D.

2013-12-01

Earth's radiation belts are a dynamic system, controlled by competition between source, acceleration, loss and transport of particles. Solar wind pressure enhancements and outward transport are responsible for loss of electrons to the magnetopause, while wave-particle interactions inside the magnetosphere, driven by solar wind pressure and velocity variations, may lead to acceleration and radial diffusion of 10's of keV to MeV energy electrons, and pitch-angle scattering loss to the atmosphere. An understanding of the mechanisms behind the observed dynamics is critical to accurate modeling and hence forecasting of radiation belt conditions, important for design, and protection of our space-borne assets. The Versatile Electron Radiation Belt (VERB) model solves the Fokker-Planck diffusion equation in three dimensional invariant coordinates, which allows one to more effectively separate adiabatic and non-adiabatic changes in the radiation belt electron population. The model includes geomagnetic storm intensity dependent parameterizations of the following dominant magnetospheric waves: day- and night-side chorus, plasmaspheric hiss (in the inner magnetosphere and inside the plume region), lightning and anthropogenic generated waves, and electro-magnetic ion cyclotron (EMIC) waves, also inside of plasmaspheric plumes. The model is used to forecast the future state of the radiation belt electron population, while real-time data may be used to update the current state of the belts through assimilation with the model. The Kalman filter provides a computationally inexpensive method to assimilate data with a model, while taking into account the errors associated with each. System identification is performed to determine the model and observational bias and errors. The Kalman filter outputs an optimal estimate of the actual system state and the Kalman-gain weighted corrections (innovation) may be used to identify systematic differences between data and the model. Careful consideration of the innovation vector may lead to a new physical understanding of the radiation belt system, which can later be used to improve our model forecasts. In the current study, we explore the radiation belt dynamics of the current era including data from the THEMIS, Van Allen Probes, GPS satellites, Akebono, NOAA and Cluster spacecraft. Intercalibration is performed between spacecraft on an individual energy channel basis, and in invariant coordinates. The global reanalysis allows an unprecedented analysis of the source-acceleration-transport-loss relationship in Earth's radiation belts. This analysis is used to refine our model capabilities, and to prepare the 3-D reanalysis for real-time data. The global 3-D reanalysis is an important step towards full-scale modeling and operational forecasting of this dynamic region of space.

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

NASA Technical Reports Server (NTRS)

Hall, Carlton; Phillips, Lynne

2016-01-01

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, Fl., USA

NASA Astrophysics Data System (ADS)

Hall, C. R.; Phillips, L. V.; Foster, T.; Stolen, E.; Duncan, B.; Hunt, D.; Schaub, R.

2016-12-01

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

Ozone Satellite Data Synergy and Combination with Non-satellite Data in the AURORA project

NASA Astrophysics Data System (ADS)

Cortesi, U.; Tirelli, C.; Arola, A.; Dragani, R.; Keppens, A.; Loenen, E.; Masini, A.; Tsiakos, , C.; van der A, R.; Verberne, K.

2017-12-01

The geostationary satellite constellation composed of TEMPO (North America), SENTINEL-4 (Europe) and GEMS (Asia) missions is a major instance of space component in the fundamentally new paradigm aimed at integrating information on air quality from a wide variety of sources. Space-borne data on tropospheric composition from new generation satellites have a growing impact in this context because of their unprecedented quantity and quality, while merging with non-satellite measurements and other types of auxiliary data via state-of-the-art modelling capabilities remains essential to fit the purpose of highly accurate information made readily available at high temporal and spatial resolution, both in analysis and forecast mode. Proper and effective implementation of this paradigm poses severe challenges to science, technology and applications that must be addressed in a closely interconnected manner to pave the way to high quality products and innovative services. Novel ideas and tools built on these three pillars are currently under investigation in the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) Horizon 2020 project of the European Commission. The primary goal of the project is the proof of concept of a synergistic approach to the exploitation of Sentinel-4 and -5 Ozone measurements in the UV, Visible and Thermal Infrared based on the combination of an innovative data fusion method and assimilation models. The scientific objective shares the same level of priority with the technological effort to realize a prototype data processor capable to manage the full data processing chain and with the development of two downstream applications for demonstration purposes. The presentation offers a first insight in mid-term results of the project, which is mostly based on the use of synthetic data from the atmospheric Sentinels. Specific focus is given to the role of satellite data synergy in integrated systems for air quality monitoring, in particular when testing the impact of TEMPO and GEMS Ozone data in AURORA. As a further element relevant for the integration of multiple data sources, we describe the AIR-Portal application, which is going to combine AURORA partial columns of tropospheric ozone with other source of information for air quality analysis and forecast in metropolitan areas.

Analysis of rock mass dynamic impact influence on the operation of a powered roof support control system

NASA Astrophysics Data System (ADS)

Szurgacz, Dawid; Brodny, Jaroław

2018-01-01

A powered roof support is a machine responsible for protection of an underground excavation against deformation generated by rock mass. In the case of dynamic impact of rock mass, the proper level of protection is hard to achieve. Therefore, the units of the roof support and its components are subject to detailed tests aimed at acquiring greater reliability, efficiency and efficacy. In the course of such test, however, it is not always possible to foresee values of load that may occur in actual conditions. The article presents a case of a dynamic load impacting the powered roof support during a high-energy tremor in an underground hard coal mine. The authors discuss the method for selecting powered roof support units proper for specific forecasted load conditions. The method takes into account the construction of the support and mining and geological conditions of an excavation. Moreover, the paper includes tests carried out on hydraulic legs and yield valves which were responsible for additional yielding of the support. Real loads impacting the support unit during tremors are analysed. The results indicated that the real registered values of the load were significantly greater than the forecasted values. The analysis results of roof support operation during dynamic impact generated by the rock mass (real life conditions) prompted the authors to develop a set of recommendations for manufacturers and users of powered roof supports. These include, inter alia, the need for innovative solutions for testing hydraulic section systems.

Sub-seasonal predictability of water scarcity at global and local scale

NASA Astrophysics Data System (ADS)

Wanders, N.; Wada, Y.; Wood, E. F.

2016-12-01

Forecasting the water demand and availability for agriculture and energy production has been neglected in previous research, partly due to the fact that most large-scale hydrological models lack the skill to forecast human water demands at sub-seasonal time scale. We study the potential of a sub-seasonal water scarcity forecasting system for improved water management decision making and improved estimates of water demand and availability. We have generated 32 years of global sub-seasonal multi-model water availability, demand and scarcity forecasts. The quality of the forecasts is compared to a reference forecast derived from resampling historic weather observations. The newly developed system has been evaluated for both the global scale and in a real-time local application in the Sacramento valley for the Trinity, Shasta and Oroville reservoirs, where the water demand for agriculture and hydropower is high. On the global scale we find that the reference forecast shows high initial forecast skill (up to 8 months) for water scarcity in the eastern US, Central Asia and Sub-Saharan Africa. Adding dynamical sub-seasonal forecasts results in a clear improvement for most regions in the world, increasing the forecasts' lead time by 2 or more months on average. The strongest improvements are found in the US, Brazil, Central Asia and Australia. For the Sacramento valley we can accurately predict anomalies in the reservoir inflow, hydropower potential and the downstream irrigation water demand 6 months in advance. This allow us to forecast potential water scarcity in the Sacramento valley and adjust the reservoir management to prevent deficits in energy or irrigation water availability. The newly developed forecast system shows that it is possible to reduce the vulnerability to upcoming water scarcity events and allows optimization of the distribution of the available water between the agricultural and energy sector half a year in advance.

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

PubMed Central

Medina, Daniel C.; Findley, Sally E.; Guindo, Boubacar; Doumbia, Seydou

2007-01-01

Background Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. Methodology/Principal Findings In this longitudinal retrospective (01/1996–06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. Conclusions/Significance The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel. PMID:18030322

Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali.

PubMed

Medina, Daniel C; Findley, Sally E; Guindo, Boubacar; Doumbia, Seydou

2007-11-21

Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel.

A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project

DOE PAGES

Gottschalck, J.; Wheeler, M.; Weickmann, K.; ...

2010-09-01

The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii)more » facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body that fosters the development of atmospheric models for NWP and climate studies. The Climate Prediction Center (CPC) within the National Centers for Environmental Prediction (NCEP) is hosting the acquisition of the forecast data, application of the MJO diagnostic, and real-time display of the standardized forecasts. The activity has contributed to the production of 1–2-week operational outlooks at NCEP and activities at other centers. Further enhancements of the diagnostic's implementation, including more extensive analysis, comparison, illustration, and verification of the contributions from the participating centers, will increase the usefulness and application of these forecasts and potentially lead to more skillful predictions of the MJO and indirectly extratropical and other weather variability (e.g., tropical cyclones) influenced by the MJO. The purpose of this article is to inform the larger scientific and operational forecast communities of the MJOWG forecast effort and invite participation from additional operational centers.« less

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-04-01

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

Network bandwidth utilization forecast model on high bandwidth networks

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

Yoo, Wuchert; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

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

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Operational Use of OGC Web Services at the Met Office

NASA Astrophysics Data System (ADS)

Wright, Bruce

2010-05-01

The Met Office has adopted the Service-Orientated Architecture paradigm to deliver services to a range of customers through Rich Internet Applications (RIAs). The approach uses standard Open Geospatial Consortium (OGC) web services to provide information to web-based applications through a range of generic data services. "Invent", the Met Office beta site, is used to showcase Met Office future plans for presenting web-based weather forecasts, product and information to the public. This currently hosts a freely accessible Weather Map Viewer, written in JavaScript, which accesses a Web Map Service (WMS), to deliver innovative web-based visualizations of weather and its potential impacts to the public. The intention is to engage the public in the development of new web-based services that more accurately meet their needs. As the service is intended for public use within the UK, it has been designed to support a user base of 5 million, the analysed level of UK web traffic reaching the Met Office's public weather information site. The required scalability has been realised through the use of multi-tier tile caching: - WMS requests are made for 256x256 tiles for fixed areas and zoom levels; - a Tile Cache, developed in house, efficiently serves tiles on demand, managing WMS request for the new tiles; - Edge Servers, externally hosted by Akamai, provide a highly scalable (UK-centric) service for pre-cached tiles, passing new requests to the Tile Cache; - the Invent Weather Map Viewer uses the Google Maps API to request tiles from Edge Servers. (We would expect to make use of the Web Map Tiling Service, when it becomes an OGC standard.) The Met Office delivers specialist commercial products to market sectors such as transport, utilities and defence, which exploit a Web Feature Service (WFS) for data relating forecasts and observations to specific geographic features, and a Web Coverage Service (WCS) for sub-selections of gridded data. These are locally rendered as maps or graphs, and combined with the WMS pre-rendered images and text, in a FLEX application, to provide sophisticated, user impact-based view of the weather. The OGC web services supporting these applications have been developed in collaboration with commercial companies. Visual Weather was originally a desktop application for forecasters, but IBL have developed it to expose the full range of forecast and observation data through standard web services (WCS and WMS). Forecasts and observations relating to specific locations and geographic features are held in an Oracle Database, and exposed as a WFS using Snowflake Software's GO-Publisher application. The Met Office has worked closely with both IBL and Snowflake Software to ensure that the web services provided strike a balance between conformance to the standards and performance in an operational environment. This has proved challenging in areas where the standards are rapidly evolving (e.g. WCS) or do not allow adequate description of the Met-Ocean domain (e.g. multiple time coordinates and parametric vertical coordinates). It has also become clear that careful selection of the features to expose, based on the way in which you expect users to query those features, in necessary in order to deliver adequate performance. These experiences are providing useful 'real-world' input in to the recently launched OGC MetOcean Domain Working Group and World Meteorological Organisation (WMO) initiatives in this area.

Mobile and static sensors in a citizen-based observatory of water

NASA Astrophysics Data System (ADS)

Brauchli, Tristan; Weijs, Steven V.; Lehning, Michael; Huwald, Hendrik

2014-05-01

Understanding and forecasting water resources and components of the water cycle require spatially and temporally resolved observations of numerous water-related variables. Such observations are often obtained from wireless networks of automated weather stations. The "WeSenseIt" project develops a citizen- and community-based observatory of water to improve the water and risk management at the catchment scale and to support decision-making of stakeholders. It is implemented in three case studies addressing various questions related to flood, drought, water resource management, water quality and pollution. Citizens become potential observers and may transmit water-related measurements and information. Combining the use of recent technologies (wireless communication, internet, smartphone) with the development of innovative low cost sensors enables the implementation of heterogeneous observatories, which (a) empower citizens and (b) expand and complement traditional operational sensing networks. With the goal of increasing spatial coverage of observations and decreasing cost for sensors, this study presents the examples of measuring (a) flow velocity in streams using smartphones and (b) sensible heat flux using simple sensors at the nodes of wireless sensor networks.

Safety on Earth From MARSS

NASA Technical Reports Server (NTRS)

2002-01-01

ENSCO, Inc., developed the Meteorological and Atmospheric Real-time Safety Support (MARSS) system for real-time assessment of meteorological data displays and toxic material spills. MARSS also provides mock scenarios to guide preparations for emergencies involving meteorological hazards and toxic substances. Developed under a Small Business Innovation Research (SBIR) contract with Kennedy Space Center, MARSS was designed to measure how safe NASA and Air Force range safety personnel are while performing weather sensitive operations around launch pads. The system augments a ground operations safety plan that limits certain work operations to very specific weather conditions. It also provides toxic hazard prediction models to assist safety managers in planning for and reacting to releases of hazardous materials. MARSS can be used in agricultural, industrial, and scientific applications that require weather forecasts and predictions of toxic smoke movement. MARSS is also designed to protect urban areas, seaports, rail facilities, and airports from airborne releases of hazardous chemical substances. The system can integrate with local facility protection units and provide instant threat detection and assessment data that is reportable for local and national distribution.

Technological change, depletion and environmental policy in the offshore oil and gas industry

NASA Astrophysics Data System (ADS)

Managi, Shunsuke

Technological change is central to maintaining standards of living in modern economies with finite resources and increasingly stringent environmental goals. Successful environmental policies can contribute to efficiency by encouraging, rather than inhibiting, technological innovation. However, little research to date has focused on the design and implementation of environmental regulations that encourage technological progress, or in insuring productivity improvements in the face of depletion of natural resources and increasing stringency of environmental regulations. This study models and measures productivity change, with an application to offshore oil and gas production in the Gulf of Mexico using Data Envelopment Analysis. This is an important application because energy resources are central to sustaining our economy. The net effects of technological progress and depletion on productivity of offshore oil and gas production are measured using a unique field-level set of data of production from all wells in the Gulf of Mexico over the time period from 1946--1998. Results are consistent with the hypothesis that technological progress has mitigated depletion effects over the study period, but the pattern differs from the conventional wisdom for nonrenewable resource industries. The Porter Hypothesis was recast, and revised version was tested. The Porter Hypothesis states that well designed environmental regulations can potentially contribute to productive efficiency in the long run by encouraging innovation. The Porter Hypothesis was recast to include market and nonmarket outputs. Our results support the recast version of Porter hypothesis, which examine productivity of joint production of market and environmental outputs. But we find no evidence for the standard formulation of the Porter hypothesis, that increased stringency of environmental regulation lead to increased productivity of market outputs and therefore increased industry profits. The model is used to forecast market and environmental outputs under alternative policy scenarios. Reliable baseline forecast and response to different policy actions of production and pollution are critical to the formation of sound energy and environmental policy. Forecast of production and pollution until year 2050 are generated from the model. Detailed policy scenarios provide quantitative assessments of potential benefits that indicate the significance of potential benefits of technological change and well-designed environmental policy.

Design of a Forecasting Service System for Monitoring of Vulnerabilities of Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Jae-Gu; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

This study aims to reduce security vulnerabilities of sensor networks which transmit data in an open environment by developing a forecasting service system. The system is to remove or monitor causes of breach incidents in advance. To that end, this research first examines general security vulnerabilities of sensor networks and analyzes characteristics of existing forecasting systems. Then, 5 steps of a forecasting service system are proposed in order to improve security responses.

Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

NASA Astrophysics Data System (ADS)

Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

1998-11-01

Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009).

PubMed

Nishiura, Hiroshi

2011-02-16

Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions. The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds. Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.

Experiments with a three-dimensional statistical objective analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, Wayman E.; Bloom, Stephen C.; Woollen, John S.; Nestler, Mark S.; Brin, Eugenia

1987-01-01

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include: (1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and (2) the capability to use an error correlation function which is geographically dependent. A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions. The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

Forecasting Air Force Logistics Command Second Destination Transportation: An Application of Multiple Regression Analysis and Neural Networks

DTIC Science & Technology

1990-09-01

without the help from the DSXR staff. William Lyons, Charles Ramsey , and Martin Meeks went above and beyond to help complete this research. Special...develop a valid forecasting model that is significantly more accurate than the one presently used by DSXR and suggested the development and testing of a...method, Strom tested DSXR’s iterative linear regression forecasting technique by examining P1 in the simple regression equation to determine whether

Stochastic Simulations of Long-Range Forecasting Models for Less Developed Regions

DTIC Science & Technology

1975-06-01

descriptors—nation national alignment, internal insl; less developed regions of Africa, report describes (1) the regions’ (2) the strategic importance of...imr.T.ARSTFTi?n SPI unty ( I,is*iif it at i 3200.0 (Att ] to End l) Mar 7, 66 *. ( * y. o 1 < n i Forecasting for Planning Strategic Importance...the long range. The forecasts that have been produced so far have been direct inputs into the Joint Long-Range Strategic Study (JLRSS), prepared by

Developing a Markov Model for Forecasting End Strength of Selected Marine Corps Reserve (SMCR) Officers

DTIC Science & Technology

2013-03-01

moving average ( ARIMA ) model because the data is not a times series. The best a manpower planner can do at this point is to make an educated assumption...MARKOV MODEL FOR FORECASTING END STRENGTH OF SELECTED MARINE CORPS RESERVE (SMCR) OFFICERS by Anthony D. Licari March 2013 Thesis Advisor...March 2013 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE DEVELOPING A MARKOV MODEL FOR FORECASTING END STRENGTH OF

Three ingredients for Improved global aftershock forecasts: Tectonic region, time-dependent catalog incompleteness, and inter-sequence variability

USGS Publications Warehouse

Page, Morgan T.; Van Der Elst, Nicholas; Hardebeck, Jeanne L.; Felzer, Karen; Michael, Andrew J.

2016-01-01

Following a large earthquake, seismic hazard can be orders of magnitude higher than the long‐term average as a result of aftershock triggering. Because of this heightened hazard, emergency managers and the public demand rapid, authoritative, and reliable aftershock forecasts. In the past, U.S. Geological Survey (USGS) aftershock forecasts following large global earthquakes have been released on an ad hoc basis with inconsistent methods, and in some cases aftershock parameters adapted from California. To remedy this, the USGS is currently developing an automated aftershock product based on the Reasenberg and Jones (1989) method that will generate more accurate forecasts. To better capture spatial variations in aftershock productivity and decay, we estimate regional aftershock parameters for sequences within the García et al. (2012) tectonic regions. We find that regional variations for mean aftershock productivity reach almost a factor of 10. We also develop a method to account for the time‐dependent magnitude of completeness following large events in the catalog. In addition to estimating average sequence parameters within regions, we develop an inverse method to estimate the intersequence parameter variability. This allows for a more complete quantification of the forecast uncertainties and Bayesian updating of the forecast as sequence‐specific information becomes available.

Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 2008 Olympics Research and Development Project

NASA Astrophysics Data System (ADS)

Kunii, Masaru; Saito, Kazuo; Seko, Hiromu; Hara, Masahiro; Hara, Tabito; Yamaguchi, Munehiko; Gong, Jiandong; Charron, Martin; Du, Jun; Wang, Yong; Chen, Dehui

2011-05-01

During the period around the Beijing 2008 Olympic Games, the Beijing 2008 Olympics Research and Development Project (B08RDP) was conducted as part of the World Weather Research Program short-range weather forecasting research project. Mesoscale ensemble prediction (MEP) experiments were carried out by six organizations in near-real time, in order to share their experiences in the development of MEP systems. The purpose of this study is to objectively verify these experiments and to clarify the problems associated with the current MEP systems through the same experiences. Verification was performed using the MEP outputs interpolated into a common verification domain with a horizontal resolution of 15 km. For all systems, the ensemble spreads grew as the forecast time increased, and the ensemble mean improved the forecast errors compared with individual control forecasts in the verification against the analysis fields. However, each system exhibited individual characteristics according to the MEP method. Some participants used physical perturbation methods. The significance of these methods was confirmed by the verification. However, the mean error (ME) of the ensemble forecast in some systems was worse than that of the individual control forecast. This result suggests that it is necessary to pay careful attention to physical perturbations.

Forecasting and Communicating Water-Related Disasters in Africa

NASA Astrophysics Data System (ADS)

Hong, Y.; Clark, R. A.; Mandl, D.; Gourley, J. J.; Flamig, Z.; Zhang, K.; Macharia, D.; Frye, S. W.; Cappelaere, P. G.; Handy, M.

2016-12-01

Accurate forecasting and communication of water and water-related hazards in developing regions could save untold lives and property. To this end, the CREST (Coupled Routing and Excess Storage) hydrologic model has been implemented over East Africa, and in dozens of other countries as a user-friendly, flexible, and highly extensible platform for monitoring water resources, floods, droughts, and landslides since 2009. We will present the updated CREST/EF5 hydrologic ensemble modeling framework with new model physics and better forecasts of streamflow, soil moisture, and other hydrologic states to RCMRD (the Regional Centre for Mapping of Resources for Development) and SERVIR global hub network. The central goal of this project is to develop an ensemble hydrologic prediction system, forced by weather and climate forecasts in a single continuum, to communicate forecasts on scales ranging from sub-daily to seasonal and in formats designed for better decision making about water and water-related disasters. The CREST/EF5 is a proven performer at getting researcher and officials in emerging regions excited about and confident in their ability to independently monitor, forecast, and understand water and water-related disasters, through a series of training workshops and capacity building activities in USA, Africa, Mesoamerica, and South Asia and is thus particularly well-suited for hydrologic capacity building in emerging countries.

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.

Visualizing water

NASA Astrophysics Data System (ADS)

Baart, F.; van Gils, A.; Hagenaars, G.; Donchyts, G.; Eisemann, E.; van Velzen, J. W.

2016-12-01

A compelling visualization is captivating, beautiful and narrative. Here we show how melding the skills of computer graphics, art, statistics, and environmental modeling can be used to generate innovative, attractive and very informative visualizations. We focus on the topic of visualizing forecasts and measurements of water (water level, waves, currents, density, and salinity). For the field of computer graphics and arts, water is an important topic because it occurs in many natural scenes. For environmental modeling and statistics, water is an important topic because the water is essential for transport, a healthy environment, fruitful agriculture, and a safe environment.The different disciplines take different approaches to visualizing water. In computer graphics, one focusses on creating water as realistic looking as possible. The focus on realistic perception (versus the focus on the physical balance pursued by environmental scientists) resulted in fascinating renderings, as seen in recent games and movies. Visualization techniques for statistical results have benefited from the advancement in design and journalism, resulting in enthralling infographics. The field of environmental modeling has absorbed advances in contemporary cartography as seen in the latest interactive data-driven maps. We systematically review the design emerging types of water visualizations. The examples that we analyze range from dynamically animated forecasts, interactive paintings, infographics, modern cartography to web-based photorealistic rendering. By characterizing the intended audience, the design choices, the scales (e.g. time, space), and the explorability we provide a set of guidelines and genres. The unique contributions of the different fields show how the innovations in the current state of the art of water visualization have benefited from inter-disciplinary collaborations.

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

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

Essays on Energy Technology Innovation Policy

NASA Astrophysics Data System (ADS)

Chan, Gabriel Angelo Sherak

Motivated by global climate change, enhancing innovation systems for energy technologies is seen as one of the largest public policy challenges of the near future. The role of policy in enhancing energy innovation systems takes several forms: public provision of research and develop funding, facilitating the private sector's capability to develop new technologies, and creating incentives for private actors to adopt innovative and appropriate technologies. This dissertation explores research questions that span this range of policies to develop insights in how energy technology innovation policy can be reformed in the face of climate change. The first chapter of this dissertation explores how decision making to allocate public research and development funding could be improved through the integration of expert technology forecasts. I present a framework to evaluate and optimize the U.S. Department of Energy's research and development portfolio of applied energy projects, accounting for spillovers from technical complimentary and competition for the same market share. This project integrates one of the largest and most comprehensive sets of expert elicitations on energy technologies (Anadon et al., 2014b) in a benefit evaluation framework. This work entailed developing a new method for probability distribution sampling that accommodates the information that can be provided by expert elicitations. The results of this project show that public research and development in energy storage and solar photovoltaic technologies has the greatest marginal returns to economic surplus, but the methodology developed in this chapter is broadly applicable to other public and private R&D-sponsoring organizations. The second chapter of this dissertation explores how policies to transfer technologies from federally funded research laboratories to commercialization partners, largely private firms, create knowledge spillovers that lead to further innovation. In this chapter, I study the U.S. Department of Energy's National Laboratories, and provide the first quantitative evidence that technology transfer agreements at the Labs lead to greatly increased rates of innovation spillovers. This chapter also makes a key methodological contribution by introducing a technique to utilize automated text analysis in an empirical matching design that is broadly applicable to other types of social science studies. This work has important implications for how policies should be designed to maximize the social benefits of the $125 billion in annual federal funding allocated to research and development and the extent to which private firms can benefit from technology partnerships with the government. The final chapter of this dissertation explores the effectiveness of international policy to facilitate the deployment of low-emitting energy technologies in developing countries. Together with Joern Huenteler, I examine wind energy deployment in China supported through international climate finance flows under the Kyoto Protocol's Clean Development Mechanism. Utilizing a project-level financial model of wind energy projects parameterized with high-resolution observations of Chinese wind speeds, we find that the environmental benefits of projects financed under the Clean Development Mechanism are substantially lower than reported, as many Chinese wind projects would have been built without the Mechanism's support, and thus do not represent additional clean energy generation. Together, the essays in this dissertation suggest several limitations of energy technology innovation policy and areas for reform. Public funds for energy research and development could be made more effective if decision making approaches were better grounded in available technical expertise and developed in framework that captures the important interactions of technologies in a research and development portfolio. The first chapter of this dissertation suggests a politically feasible path towards this type of reform. Policies to "unlock" publicly sponsored inventions from the organizations that develop them have broad impact on private sector innovation. These policies multiply the effect of public research and development funds, but should be strengthened to more rapidly advance the scientific frontier. The second chapter of this dissertation provides some of the first quantitative evidence to support reform in this area. Finally, international policies to facilitate the deployment of climate-friendly technologies in developing countries face serious implementation challenges. The current paradigm of utilizing carbon markets to fund individual projects that would not have otherwise occurred has failed to encourage energy technology deployment in one of the sectors with the greatest experience with such policies. The third chapter of this dissertation suggests that this failure has been largely due to poorly designed procedural rules, but options for reform are available. Mitigation of global climate change will require broad policy response across the full range of scales, sectors, and policy spheres. Undoubtedly, climate mitigation will result in widespread transformation of energy systems. This dissertation focuses on the role of innovation policy in accelerating the transformation of these systems. The range of policies studied in this dissertation can make climate change mitigation more politically feasible and more cost effective by expanding the set of technological choices available to public and private actors faced with incentives and requirements to lower their greenhouse gas emissions to collectively safe levels.

Bayesian Population Forecasting: Extending the Lee-Carter Method.

PubMed

Wiśniowski, Arkadiusz; Smith, Peter W F; Bijak, Jakub; Raymer, James; Forster, Jonathan J

2015-06-01

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

Demand forecast model based on CRM

NASA Astrophysics Data System (ADS)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Error models for official mortality forecasts.

PubMed

Alho, J M; Spencer, B D

1990-09-01

"The Office of the Actuary, U.S. Social Security Administration, produces alternative forecasts of mortality to reflect uncertainty about the future.... In this article we identify the components and assumptions of the official forecasts and approximate them by stochastic parametric models. We estimate parameters of the models from past data, derive statistical intervals for the forecasts, and compare them with the official high-low intervals. We use the models to evaluate the forecasts rather than to develop different predictions of the future. Analysis of data from 1972 to 1985 shows that the official intervals for mortality forecasts for males or females aged 45-70 have approximately a 95% chance of including the true mortality rate in any year. For other ages the chances are much less than 95%." excerpt

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

Using EFSO/PQC to Improve the Quality of Observations and Analyses

NASA Astrophysics Data System (ADS)

Chen, T. C.; Kalnay, E.

2017-12-01

Massive amounts of observations are assimilated every day into modern Numerical Weather Prediction (NWP) systems. This makes difficult to estimate the impact of a new observing system using the current approach (Observing System Experiments, OSEs) because there is already so much information provided by existing observations. In addition, the large volume of data also prevents monitoring the impact of each assimilated observation with OSEs. We demonstrate here how using Ensemble Forecast Sensitivity to Observations (EFSO) allows monitoring and improving the impact of observations on the analyses and forecasts in the Hybrid GSI/LETKF system. In addition, we show how EFSO can identify flow dependent detrimental observations from observing systems that, on the average, are beneficial. For example, using EFSO we find that positive zonal wind innovations in MODIS polar winds are generally detrimental, whereas no such bias is present in other satellite wind systems. We also show how EFSO can be used to identify and reject very detrimental observations (Proactive Quality Control, PQC). The withdrawal of these detrimental observations leads to improved analyses and 5-day forecasts, which also serves as a verification of EFSO. The operational implementation of PQC/EFSO is computationally very feasible, and will provide detailed QC monitoring of every observing system. Finally, we provide a theoretical justification of the PQC and its connection to dynamical instabilities with a simple Lorenz 96 model.

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.

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.

Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior under varied global and local scale climatic influences from the developed BHMM.

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

Code of Federal Regulations, 2010 CFR

2010-01-01

... narrative shall address the overall approach, time periods, and expected internal and external uses of the forecast. Examples of internal uses include providing information for developing or monitoring demand side... suppliers. Examples of external uses include meeting state and Federal regulatory requirements, obtaining...

Wildfire suppression cost forecasts from the US Forest Service

Treesearch

Karen L. Abt; Jeffrey P. Prestemon; Krista M. Gebert

2009-01-01

The US Forest Service and other land-management agencies seek better tools for nticipating future expenditures for wildfire suppression. We developed regression models for forecasting US Forest Service suppression spending at 1-, 2-, and 3-year lead times. We compared these models to another readily available forecast model, the 10-year moving average model,...

Thermospheric Data Assimilation

DTIC Science & Technology

2016-05-05

forecasting longer than 3 days. Furthermore, validation of assimilation analyses with independent CHAMP mass density observations confirms that the...approach developed in this project. 15. SUBJECT TERMS Data assimilation, Ensemble forecasting , Thermosphere-ionosphere coupled data assimilation...Neutral mass density specification and forecasting , 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 6 19a. NAME

Load Modeling and Forecasting | Grid Modernization | NREL

Science.gov Websites

Load Modeling and Forecasting Load Modeling and Forecasting NREL's work in load modeling is focused resources (such as rooftop photovoltaic systems) and changing customer energy use profiles, new load models distribution system. In addition, NREL researchers are developing load models for individual appliances and

Developing Environmental Scanning/Forecasting Systems To Augment Community College Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Held, William G.

A description is provided of a conference session that was conducted to explore the structure and function of an environmental scanning/forecasting system that could be used in a community college to facilitate planning. Introductory comments argue that a college that establishes an environmental scanning and forecasting system is able to identify…

Projected Applications of a ``Climate in a Box'' Computing System at the NASA Short-term Prediction Research and Transition (SPoRT) Center

NASA Astrophysics Data System (ADS)

Jedlovec, G.; Molthan, A.; Zavodsky, B.; Case, J.; Lafontaine, F.

2010-12-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to “Climate in a Box” systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the “Climate in a Box” system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA’s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the “Climate in a Box” system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

Projected Applications of a "Climate in a Box" Computing System at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Molthan, Andrew L.; Zavodsky, Bradley; Case, Jonathan L.; LaFontaine, Frank J.

2010-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique observations and research capabilities to the operational weather community, with a goal of improving short-term forecasts on a regional scale. Advances in research computing have lead to "Climate in a Box" systems, with hardware configurations capable of producing high resolution, near real-time weather forecasts, but with footprints, power, and cooling requirements that are comparable to desktop systems. The SPoRT Center has developed several capabilities for incorporating unique NASA research capabilities and observations with real-time weather forecasts. Planned utilization includes the development of a fully-cycled data assimilation system used to drive 36-48 hour forecasts produced by the NASA Unified version of the Weather Research and Forecasting (WRF) model (NU-WRF). The horsepower provided by the "Climate in a Box" system is expected to facilitate the assimilation of vertical profiles of temperature and moisture provided by the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua satellite. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA s Aqua and Terra satellites provide high-resolution sea surface temperatures and vegetation characteristics. The development of MODIS normalized difference vegetation index (NVDI) composites for use within the NASA Land Information System (LIS) will assist in the characterization of vegetation, and subsequently the surface albedo and processes related to soil moisture. Through application of satellite simulators, NASA satellite instruments can be used to examine forecast model errors in cloud cover and other characteristics. Through the aforementioned application of the "Climate in a Box" system and NU-WRF capabilities, an end goal is the establishment of a real-time forecast system that fully integrates modeling and analysis capabilities developed within the NASA SPoRT Center, with benefits provided to the operational forecasting community.

The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges

NASA Astrophysics Data System (ADS)

Fry, C. D.; Eccles, J. V.; Reich, J. P.

2010-12-01

Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.

Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting

NASA Astrophysics Data System (ADS)

Tang, L.; Titov, V. V.; Chamberlin, C. D.

2009-12-01

The study describes the development, testing and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast and warning system. The model development process includes sensitivity studies of tsunami wave characteristics in the nearshore and inundation, for a range of model grid setups, resolutions and parameters. To demonstrate the process, four forecast models in Hawaii, at Hilo, Kahului, Honolulu, and Nawiliwili are described. The models were validated with fourteen historical tsunamis and compared with numerical results from reference inundation models of higher resolution. The accuracy of the modeled maximum wave height is greater than 80% when the observation is greater than 0.5 m; when the observation is below 0.5 m the error is less than 0.3 m. The error of the modeled arrival time of the first peak is within 3% of the travel time. The developed forecast models were further applied to hazard assessment from simulated magnitude 7.5, 8.2, 8.7 and 9.3 tsunamis based on subduction zone earthquakes in the Pacific. The tsunami hazard assessment study indicates that use of a seismic magnitude alone for a tsunami source assessment is inadequate to achieve such accuracy for tsunami amplitude forecasts. The forecast models apply local bathymetric and topographic information, and utilize dynamic boundary conditions from the tsunami source function database, to provide site- and event-specific coastal predictions. Only by combining a Deep-ocean Assessment and Reporting of Tsunami-constrained tsunami magnitude with site-specific high-resolution models can the forecasts completely cover the evolution of earthquake-generated tsunami waves: generation, deep ocean propagation, and coastal inundation. Wavelet analysis of the tsunami waves suggests the coastal tsunami frequency responses at different sites are dominated by the local bathymetry, yet they can be partially related to the locations of the tsunami sources. The study also demonstrates the nonlinearity between offshore and nearshore maximum wave amplitudes.

KSC-06pd1283

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - A Rawinsonde weather balloon sails into the sky after release from the Cape Canaveral forecast facility in Florida. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

KSC-06pd1281

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, a worker carries a Rawinsonde weather balloon outside for release. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

KSC-06pd1282

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, a worker releases a Rawinsonde weather balloon outside for release. Rawinsonde balloons are GPS-tracked and can collect such data as atmospheric pressure, temperature, humidity and wind speed and direction up to 100,000 feet. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Extended Statistical Short-Range Guidance for Peak Wind Speed Analyses at the Shuttle Landing Facility: Phase II Results

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2003-01-01

This report describes the results from Phase II of the AMU's Short-Range Statistical Forecasting task for peak winds at the Shuttle Landing Facility (SLF). The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The 45th Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A seven year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. A PC-based Graphical User Interface (GUI) tool was created to display the data quickly.

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

PubMed

Bou Assi, Elie; Nguyen, Dang K; Rihana, Sandy; Sawan, Mohamad

2018-06-01

The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection. We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for seizure onset zone localization in bilateral intracranial EEG recordings. Electrodes identified as seizure activity sources and sinks are then used to implement a seizure-forecasting algorithm on long-term continuous recordings in dogs with naturally-occurring epilepsy. A precision-recall genetic algorithm is proposed for feature selection in line with a probabilistic support vector machine classifier. Epileptic activity generators were focal in all dogs confirming the diagnosis of focal epilepsy in these animals while sinks spanned both hemispheres in 2 of 3 dogs. Seizure forecasting results show performance improvement compared to previous studies, achieving average sensitivity of 84.82% and time in warning of 0.1. Achieved performances highlight the feasibility of seizure forecasting in canine epilepsy. The ability to improve seizure forecasting provides promise for the development of EEG-triggered closed-loop seizure intervention systems for ambulatory implantation in patients with refractory epilepsy.

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.

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.

Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

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

2013-12-01

Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail end of high flow periods. These improvements allowed DEP to more effectively manage water quality control and spill mitigation operations immediately after storm events. Later on, post-processed hydrologic forecasts from the National Weather Service (NWS) including the Advanced Hydrologic Prediction Service (AHPS) and the Hydrologic Ensemble Forecast Service (HEFS) were implemented into OST. These forecasts further increased the predictive skill over the initial statistical models as current basin conditions (e.g. soil moisture, snowpack) and meteorological forecasts (with HEFS) are now explicitly represented. With the post-processed HEFS forecasts, DEP may now truly quantify impacts associated with wet weather events on the horizon, rather than relying on statistical representations of current hydrologic trends. This presentation will highlight the benefits of the improved forecasts using examples from actual system operations.

Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

NASA Astrophysics Data System (ADS)

Zheng, Fei; Zhu, Jiang

2017-04-01

How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Niño-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.

Innovative hyperchaotic encryption algorithm for compressed video

NASA Astrophysics Data System (ADS)

Yuan, Chun; Zhong, Yuzhuo; Yang, Shiqiang

2002-12-01

It is accepted that stream cryptosystem can achieve good real-time performance and flexibility which implements encryption by selecting few parts of the block data and header information of the compressed video stream. Chaotic random number generator, for example Logistics Map, is a comparatively promising substitute, but it is easily attacked by nonlinear dynamic forecasting and geometric information extracting. In this paper, we present a hyperchaotic cryptography scheme to encrypt the compressed video, which integrates Logistics Map with Z(232 - 1) field linear congruential algorithm to strengthen the security of the mono-chaotic cryptography, meanwhile, the real-time performance and flexibility of the chaotic sequence cryptography are maintained. It also integrates with the dissymmetrical public-key cryptography and implements encryption and identity authentification on control parameters at initialization phase. In accord with the importance of data in compressed video stream, encryption is performed in layered scheme. In the innovative hyperchaotic cryptography, the value and the updating frequency of control parameters can be changed online to satisfy the requirement of the network quality, processor capability and security requirement. The innovative hyperchaotic cryprography proves robust security by cryptoanalysis, shows good real-time performance and flexible implement capability through the arithmetic evaluating and test.

Monthly forecasting of agricultural pests in Switzerland

NASA Astrophysics Data System (ADS)

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

2012-04-01

Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the probabilistic forecasts vs. the mean absolute errors of the deterministic system. Also, the application of the climate conserving recalibration (CCR, Weigel et al. 2009) technique allows for successful correction of the under-confidence in the forecasted occurrences of codling moth life phases. Reference: Weigel, A. P.; Liniger, M. A. & Appenzeller, C. (2009). Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels? Mon. Wea. Rev., 137, 1460-1479.

Pollen Dispersion Forecast At Regional Scale

NASA Astrophysics Data System (ADS)

Mangin, A.; Asthma Forecast System Team

The forecast of the pollen concentration is generally based on an identification of sim- ilar coincidence of measured pollen at given points and meteorological data that is searched in an archive and which, with the help of experts, allows building a predicted value. This may be classified under the family of statistical approaches for forecast- ing. While palynologists make these methods more and more accurate with the help of innovative techniques of regression against empirical rules and/or evolving mathe- matical structures (e.g. neural networks), the spatial dispersion of the pollen is not or poorly considered, mainly because it requires a lot of means and technique that are not familiar to this scientific discipline. The research on pollen forecasts are presently mainly focused on the problematic of modeling the behavior of pollen trends and sea- sons at one location regardless of the topography, the locations of emitters, the relative strengths of emitter, in one word the Sspatial backgroundT. This research work was a & cedil;successful attempt to go a step further combining this SlocalT approach with a trans- & cedil;port/dispersion modeling allowing the access to mapping of concentration. The areas of interest that were selected for the demonstration of feasibility were 200x200km zones centered on Cordoba, Barcelona and Bologna and four pollen types were ex- amined, namely: Cupressaceae, Olea europaea, Poaceae and Parietaria. At the end of this three-year European project in December 2001, the system was fully deployed and validated. The multidisciplinary team will present the original methodologies that were derived for modeling the numerous aspects of this problem and also some con- clusions regarding potential extent to other areas and taxa.

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system simulations will help to develop a seamless integration with the boundary systems in the service-gap area with new insights into our scientific understanding of such complex systems. A visualization system is being developed to allow stake holders and the community to have access to the flood forecasting for their region with sufficient lead time.

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.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.

PubMed

Withanage, Gayan P; Viswakula, Sameera D; Nilmini Silva Gunawardena, Y I; Hapugoda, Menaka D

2018-04-24

Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

Innovative fiber-laser architecture-based compact wind lidar

NASA Astrophysics Data System (ADS)

Prasad, Narasimha S.; Tracy, Allen; Vetorino, Steve; Higgins, Richard; Sibell, Russ

2016-03-01

This paper describes an innovative, compact and eyesafe coherent lidar system developed for use in wind and wake vortex sensing applications. This advanced lidar system is field ruggedized with reduced size, weight, and power consumption (SWaP) configured based on an all-fiber and modular architecture. The all-fiber architecture is developed using a fiber seed laser that is coupled to uniquely configured fiber amplifier modules and associated photonic elements including an integrated 3D scanner. The scanner provides user programmable continuous 360 degree azimuth and 180 degree elevation scan angles. The system architecture eliminates free-space beam alignment issues and allows plug and play operation using graphical user interface software modules. Besides its all fiber architecture, the lidar system also provides pulsewidth agility to aid in improving range resolution. Operating at 1.54 microns and with a PRF of up to 20 KHz, the wind lidar is air cooled with overall dimensions of 30" x 46" x 60" and is designed as a Class 1 system. This lidar is capable of measuring wind velocities greater than 120 +/- 0.2 m/s over ranges greater than 10 km and with a range resolution of less than 15 m. This compact and modular system is anticipated to provide mobility, reliability, and ease of field deployment for wind and wake vortex measurements. The current lidar architecture is amenable for trace gas sensing and as such it is being evolved for airborne and space based platforms. In this paper, the key features of wind lidar instrumentation and its functionality are discussed followed by results of recent wind forecast measurements on a wind farm.

Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...

Forecast and virtual weather driven plant disease risk modeling system

USDA-ARS?s Scientific Manuscript database

We describe a system in use and development that leverages public weather station data, several spatialized weather forecast types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...

Applications of subseasonal-to-seasonal (S2S) predictions

NASA Astrophysics Data System (ADS)

White, Christopher; Lamb, Rob; Carlsen, Henrik; Robertson, Andrew; Klein, Richard; Lazo, Jeffrey; Kumar, Arun; Vitart, Frederic; Coughlan de Perez, Erin; Ray, Andrea; Murray, Virginia; Graham, Richard; Buontempo, Carlo

2017-04-01

While long-range seasonal outlooks have been operational for many years, until recently the extended-range timescale - referred to as 'subseasonal-to-seasonal' (S2S) and which sits between the medium- to long-range forecasting timescales - has received relatively little attention. The S2S timescale has long been seen as a 'predictability desert', yet a new generation of S2S predictions are starting to bridge the gap between weather forecasts and longer-range prediction. Decisions in a range of sectors are made in this extended-range lead time, therefore there is a strong demand for this new generation of predictions. At least ten international weather centres now have some capability for issuing experimental or operational S2S predictions, including the European Centre for Medium-Range Weather Forecasting (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) that now have operational S2S outputs. International efforts are now underway to identify key sources of predictability, improve forecast skill and operationalise aspects of S2S forecasts, however challenges remain in advancing this new frontier. If S2S predictions are to be utilised effectively, it is important that along with science advances, we learn how to develop, communicate and apply these forecasts appropriately. In this study, we present the potential of the emerging operational S2S forecasts to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. We explore the value of applications-relevant S2S predictions, and highlight the opportunities and challenges facing their uptake. We show how social sciences can be integrated with S2S development - from communication to decision-making and valuation of forecasts - to enhance the benefits of 'climate services' approaches for extended-range forecasting. We highlight the availability of data repositories of near real-time S2S forecasts and hindcasts, including the WWRP-WCRP (http://apps.ecmwf.int/datasets/data/s2s) and North American Multimodel Ensemble (NMME; http://www.cpc.ncep.noaa.gov/products/NMME/data.html) repositories, and discuss how they are promoting the use (and aiding the development) of S2S predictions. While S2S forecasting is at a relatively early stage of development, we conclude that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

How accurate are the weather forecasts for Bierun (southern Poland)?

NASA Astrophysics Data System (ADS)

Gawor, J.

2012-04-01

Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why are some weather elements easier to verify than others? 5) What factors may contribute to the quality of the weather forecast?

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

NASA Astrophysics Data System (ADS)

Pattanayak, Sujata; Mohanty, U. C.

2018-06-01

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April-3 May 2008), Aila (23-26 May 2009) and Jal (4-8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.