Freeway travel-time estimation and forecasting.

DOT National Transportation Integrated Search

2013-03-01

Real-time traffic information provided by GDOT has proven invaluable for commuters in the : Georgia freeway network. The increasing number of Variable Message Signs, addition of : services such as My-NaviGAtor, NaviGAtor-to-go etc. and the advancemen...

Bicycle and pedestrian travel demand forecasting : summary of data collection activities

DOT National Transportation Integrated Search

1997-09-01

This report summarizes data collection activities performed at eight different sites in Texas urban areas. The data : were collected to help develop and test bicycle and pedestrian travel demand forecasting techniques. The : research team collected d...

Understanding urban travel demand : problems, solutions, and the role of forecasting

DOT National Transportation Integrated Search

1999-08-01

This report is a general examination and critique of transportation policy making, focusing on the role of traffic and land use forecasting. There are four major components: (1) Current, historical, and projected travel behavior in the Twin Cities; (...

Studies of air traffic forecasts, airspace load and the effect of ADS-B via satellites on flight times

NASA Astrophysics Data System (ADS)

Zhong, Z. W.; Ridhwan Salleh, Saiful; Chow, W. X.; Ong, Z. M.

2016-10-01

Air traffic forecasting is important as it helps stakeholders to plan their budgets and facilities. Thus, three most commonly used forecasting models were compared to see which model suited the air passenger traffic the best. General forecasting equations were also created to forecast the passenger traffic. The equations could forecast around 6.0% growth from 2015 onwards. Another study sought to provide an initial work for determining a theoretical airspace load with relevant calculations. The air traffic was simulated to investigate the current airspace load. Logical and reasonable results were obtained from the modelling and simulations. The current utilization percentages for airspace load per hour and the static airspace load in the interested airspace were found to be 6.64% and 11.21% respectively. Our research also studied how ADS-B would affect the time taken for aircraft to travel. 6000 flights departing from and landing at the airport were studied. New flight plans were simulated with improved flight paths due to the implementation of ADS-B, and flight times of all studied flights could be improved.

Hydrological Analysis for Inflow Forecasting into Temengor Dam

NASA Astrophysics Data System (ADS)

Najid, MI; Sidek, LM; Hidayah, B.; Roseli, ZA

2016-03-01

These days, natural disaster such as flood is the main concern for hydrologists. One of solutions in understanding the reason of flood is by prediction of the event sooner than normal occurrence. One of the criteria is lead time or travel time that is important in the study of fresh waters and flood events. Therefore, estimation of lead or travel time for flood event can be beneficial primary information. The objective of this study is to estimate the lead time or travel time for outlet of Temengor dam in Malaysia. Tenaga Nasional Berhad (TNB) Sungai Perak dam operation has the main contribution on decision support for early water released and flood warning to authorities and locals resident for in the down streams area. For this study, hydrological analysis carried out will help to determine which years that give more rainfall contribution into the reservoir. Rainfall contribution of reservoir help to understanding rainfall distribution and peak discharge on that period. It also help for calibration of forecasting model system for better accuracy of flood hydrograph. There may be various methods to determine the rainfall contribution of catchment. The result has shown that, the rainfall contribution for Temengor catchment, is more on November in each year which is the monsoon season in Malaysia. TNB dam operational decision support systems can prepare and be more aware at this time for flood control and flood mitigation.

Traffic-load forecasting using weigh-in-motion data

DOT National Transportation Integrated Search

1997-03-01

Vehicular traffic loading is a crucial consideration for the design and maintenance of pavements. With the help of weigh-in-motion (WIM) systems, the information about date, time, speed, lane of travel, lateral lane position, axle spacing, and wheel ...

Examination of simplified travel demand model. [Internal volume forecasting model

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

Smith, R.L. Jr.; McFarlane, W.J.

1978-01-01

A simplified travel demand model, the Internal Volume Forecasting (IVF) model, proposed by Low in 1972 is evaluated as an alternative to the conventional urban travel demand modeling process. The calibration of the IVF model for a county-level study area in Central Wisconsin results in what appears to be a reasonable model; however, analysis of the structure of the model reveals two primary mis-specifications. Correction of the mis-specifications leads to a simplified gravity model version of the conventional urban travel demand models. Application of the original IVF model to ''forecast'' 1960 traffic volumes based on the model calibrated for 1970more » produces accurate estimates. Shortcut and ad hoc models may appear to provide reasonable results in both the base and horizon years; however, as shown by the IVF mode, such models will not always provide a reliable basis for transportation planning and investment decisions.« less

Travel demand modeling for the small and medium sized MPOs in Illinois.

DOT National Transportation Integrated Search

2011-09-01

Travel demand modeling is an important tool in the transportation planning community. It helps forecast travel : characteristics into the future at various planning levels such as state, region and corridor. Using travel demand : modeling to evaluate...

Development of weekend travel demand and mode choice models : final report, June 2009.

DOT National Transportation Integrated Search

2010-06-30

Travel demand models are widely used for forecasting and analyzing policies for automobile and transit travel. However, these models are typically developed for average weekday travel when regular activities are routine. The weekday models focus prim...

TRANPLAN and GIS support for agencies in Alabama

DOT National Transportation Integrated Search

2001-08-06

Travel demand models are computerized programs intended to forecast future roadway traffic volumes for a community based on selected socioeconomic variables and travel behavior algorithms. Software to operate these travel demand models is currently a...

2000-2001 California statewide household travel survey. Final report

DOT National Transportation Integrated Search

2002-06-01

The California Department of Transportation (Caltrans) maintains a statewide database of household socioeconomic and travel information, which is used in regional and statewide travel demand forecasting. The 2000-2001 California Statewide Household T...

2011 Atlanta, Georgia, Regional Travel Survey | Transportation Secure Data

Science.gov Websites

Center | NREL Atlanta, Georgia, Regional Travel Survey 2011 Atlanta, Georgia, Regional Travel Survey To improve regional travel demand forecasts, the 2011 Regional Travel Survey collected trip data the Atlanta Regional Commission (ARC), the survey was conducted by PTV NuStats, GeoStats, and PG

Understanding Activity Engagement Across Weekdays and Weekend Days: A Multivariate Multiple Discrete-Continuous Modeling Approach

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

Garikapati, Venu; Astroza, Sebastian; Bhat, Prerna C.

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasinglymore » in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. The model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.« less

Tsunami Forecasting and Monitoring in New Zealand

NASA Astrophysics Data System (ADS)

Power, William; Gale, Nora

2011-06-01

New Zealand is exposed to tsunami threats from several sources that vary significantly in their potential impact and travel time. One route for reducing the risk from these tsunami sources is to provide advance warning based on forecasting and monitoring of events in progress. In this paper the National Tsunami Warning System framework, including the responsibilities of key organisations and the procedures that they follow in the event of a tsunami threatening New Zealand, are summarised. A method for forecasting threat-levels based on tsunami models is presented, similar in many respects to that developed for Australia by Allen and Greenslade (Nat Hazards 46:35-52, 2008), and a simple system for easy access to the threat-level forecasts using a clickable pdf file is presented. Once a tsunami enters or initiates within New Zealand waters, its progress and evolution can be monitored in real-time using a newly established network of online tsunami gauge sensors placed at strategic locations around the New Zealand coasts and offshore islands. Information from these gauges can be used to validate and revise forecasts, and assist in making the all-clear decision.

IPM Pipe

Science.gov Websites

View My Alerts Reporting Disease Outbreaks Interpreting Threats and Risks Photo Gallery Field pathways spores are likely to travel from confirmed sources. The forecasts were prepared for purposes of the forecasts for any use. This forecasting service is provided by the North Carolina State University

Review of travel data issues in VDOT's Transportation Planning Division.

DOT National Transportation Integrated Search

1991-01-01

The Transportation Planning Division (TPD) of the Virginia Department of Transportation uses an extensive amount of travel data in both its routine planning activities and its provision of data forecasts to other divisions. Travel data consist of suc...

Demand modelling of passenger air travel: An analysis and extension, volume 2

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1978-01-01

Previous intercity travel demand models in terms of their ability to predict air travel in a useful way and the need for disaggregation in the approach to demand modelling are evaluated. The viability of incorporating non-conventional factors (i.e. non-econometric, such as time and cost) in travel demand forecasting models are determined. The investigation of existing models is carried out in order to provide insight into their strong points and shortcomings. The model is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed. In addition this volume contains two appendices which should prove useful to the non-specialist in the area.

Synthesis of traveler choice research: improving modeling accuracy for better transportation decisionmaking.

DOT National Transportation Integrated Search

2013-08-01

"Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been : immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density : together wit...

A retrospective evaluation of traffic forecasting techniques.

DOT National Transportation Integrated Search

2016-08-01

Traffic forecasting techniquessuch as extrapolation of previous years traffic volumes, regional travel demand models, or : local trip generation rateshelp planners determine needed transportation improvements. Thus, knowing the accuracy of t...

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-03-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-04-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Forecasting paratransit services demand : review and recommendations.

DOT National Transportation Integrated Search

2013-06-01

Travel demand forecasting tools for Floridas paratransit services are outdated, utilizing old national trip : generation rate generalities and simple linear regression models. In its guidance for the development of : mandated Transportation Disadv...

Acoustical environment measurement at a very shallow port: Trial case in Hashirimizu Port

NASA Astrophysics Data System (ADS)

Ogasawara, Hanako; Mori, Kazuyoshi

2016-07-01

Recently, the needs for coastal environment measurement has been increasing for many purposes, such as fishing, weather forecasting, ocean noise measurement for power plants, and coastal security. Acoustical measurement is one of the solutions because it can cover a wide area with few sensors, and it is possible to monitor long term or in real time. In this study, a small-scale reciprocal sound travel experiment was carried out in Hashirimizu Port for coastal environment measurement, such as current speed and water temperature. Since the distance between the surface and the transducer becomes short according to the tidal effect, the direct signal is canceled by the surface-reflected signal under a specific condition. However, even under such a condition, mean water temperature could be estimated from the reciprocal travel time using bottom-reflected signals. The current along the travel path was a reasonable value. It is possible to obtain a special current speed with another reciprocal path, which is in a direction perpendicular to the current travel path.

The future of transportation planning : dynamic travel behavior analyses based on stochastic decision-making styles : final report.

DOT National Transportation Integrated Search

2003-08-01

Over the past half-century, the progress of travel behavior research and travel demand forecasting has been spear : headed and continuously propelled by the micro-economic theories, specifically utility maximization. There is no : denial that the tra...

Socioeconomic Forecasting : [Technical Summary

DOT National Transportation Integrated Search

2012-01-01

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

DTWT (Dispersive Tsunami Wave Tool): a new tool for computing the complete dispersion of tsunami travel time.

NASA Astrophysics Data System (ADS)

Reymond, Dominique

2017-04-01

We present a tool for computing the complete arrival times of the dispersed wave-train of a tsunami. The calculus is made using the exact formulation of the tsunami dispersion (and without approximations), at any desired periods between one hour or more (concerning the gravity waves propagation) until 10s (the highly dispersed mode). The computation of the travel times is based on the a summation of the necessary time for a tsunami to cross all the elementary blocs of a grid of bathymetry following a path between the source and receiver at a given period. In addition the source dimensions and the focal mechanism are taken into account to adjust the minimum travel time to the different possible points of emission of the source. A possible application of this tool is to forecast the arrival time of late arrivals of tsunami waves that could produce the resonnance of some bays and sites at higher frequencies than the gravity mode. The theoretical arrival times are compared to the observed ones and to the results obtained by TTT (P. Wessel, 2009) and the ones obtained by numerical simulations. References: Wessel, P. (2009). Analysis of oberved and predicted tsunami travel times for the Pacic and Indian oceans. Pure Appl. Geophys., 166:301-324.

Socioeconomic Forecasting Model for the Tri-County Regional Planning Commission

DOT National Transportation Integrated Search

1997-01-01

Socioeconomic data is a critical input to transportation planning and travel demand forecasting. Accurate estimates of existing population, incomes, employment and other socioeconomic characteristics are necessary for meaningful calibration of a trav...

Improving socioeconomic land use forecasting for medium-sized metropolitan organizations in Virginia.

DOT National Transportation Integrated Search

2008-01-01

Socioeconomic forecasts are the foundation for long range travel demand modeling, projecting variables such as population, households, employment, and vehicle ownership. In Virginia, metropolitan planning organizations (MPOs) develop socioeconomic fo...

Time series forecasting of future claims amount of SOCSO's employment injury scheme (EIS)

NASA Astrophysics Data System (ADS)

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

2012-09-01

The Employment Injury Scheme (EIS) provides protection to employees who are injured due to accidents whilst working, commuting from home to the work place or during employee takes a break during an authorized recess time or while travelling that is related with his work. The main purpose of this study is to forecast value on claims amount of EIS for the year 2011 until 2015 by using appropriate models. These models were tested on the actual EIS data from year 1972 until year 2010. Three different forecasting models are chosen for comparisons. These are the Naïve with Trend Model, Average Percent Change Model and Double Exponential Smoothing Model. The best model is selected based on the smallest value of error measures using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). From the result, the best model that best fit the forecast for the EIS is the Average Percent Change Model. Furthermore, the result also shows the claims amount of EIS for the year 2011 to year 2015 continue to trend upwards from year 2010.

Forecasting Space Weather Hazards for Astronauts in Deep Space

NASA Astrophysics Data System (ADS)

Martens, P. C.

2018-02-01

Deep Space Gateway provides a unique platform to develop, calibrate, and test a space weather forecasting system for interplanetary travel in a real life setting. We will discuss requirements and design of such a system.

1988/1989 household travel survey

DOT National Transportation Integrated Search

1989-07-01

The primary objectives of this study were to provide the data: (1) : to update the trip generation rates used in the Maricopa Association of Governments (MAG) travel demand forecasting process, and; (2) to validate the MAG trip distribution model. Th...

Florida Model Information eXchange System (MIXS).

DOT National Transportation Integrated Search

2013-08-01

Transportation planning largely relies on travel demand forecasting, which estimates the number and type of vehicles that will use a roadway at some point in the future. Forecasting estimates are made by computer models that use a wide variety of dat...

Large-Scale Traffic Microsimulation From An MPO Perspective

DOT National Transportation Integrated Search

1997-01-01

One potential advancement of the four-step travel model process is the forecasting and simulation of individual activities and travel. A common concern with such an approach is that the data and computational requirements for a large-scale, regional ...

Evaluation of origin-destination matrix estimation techniques to support aspects of traffic modeling.

DOT National Transportation Integrated Search

2014-05-01

Travel demand forecasting models are used to predict future traffic volumes to evaluate : roadway improvement alternatives. Each of the metropolitan planning organizations (MPO) in : Alabama maintains a travel demand model to support planning efforts...

Guidebook on Methods to Estimate Non-Motorized Travel : Supporting Documentation

DOT National Transportation Integrated Search

1999-07-01

This guidebook provides a means for practitioner to better understand and estimate bicycle and pedestrian travel and to address transportation planning needs. The guidebook describes and compares the various methods that can be used to forecast non-m...

Guidebook on Methods to Estimate Non-Motorized Travel : Overview of Methods

DOT National Transportation Integrated Search

1999-07-01

This guidebook provides a means for practitioner to better understand and estimate bicycle and pedestrian travel and to address transportation planning needs. The guidebook describes and compares the various methods that can be used to forecast non-m...

Representation of transit ITS in network-based travel models

DOT National Transportation Integrated Search

2005-03-01

The increased use of Intelligent Transportation Systems (ITS) technology in public transit has two major impacts on travel forecasting. First, the technology will often result in an improved volume and quality of data that may be used for planning. S...

A method for the determination of potentially profitable service patterns for commuter air carriers

NASA Technical Reports Server (NTRS)

Ransone, R. K.; Kuhlthau, A. R.; Deptula, D. A.

1975-01-01

A methodology for estimating market conception was developed as a part of the short-haul air transportation program. It is based upon an analysis of actual documents which provide a record of known travel history. Applying this methodology a forecast was made of the demand for an air feeder service between Charlottesville, Virginia and Dulles International Airport. Local business travel vouchers and local travel agent records were selected to provide the documentation. The market was determined to be profitable for an 8-passenger Cessna 402B aircraft flying a 2-hour daily service pattern designed to mesh to the best extent possible with the connecting schedules at Dulles. The Charlottesville - Dulles air feeder service market conception forecast and its methodology are documented.

An Interim Update to the 2035 Socioeconomic and Travel Demand Forecasts for Virginia

DOT National Transportation Integrated Search

2012-10-01

In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs ex...

An interim update to the 2035 socioeconomic and travel demand forecasts for Virginia.

DOT National Transportation Integrated Search

2012-09-01

"In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs e...

An interim update to the 2035 socioeconomic and travel demand forecasts for Virginia.

DOT National Transportation Integrated Search

2012-10-01

In support of the update to Virginias 2035 Statewide Multimodal Plan, this report provides an update to select : socioeconomic forecasts initially made in 2009 based on a review of data from national sources and the literature. Mobility : needs ex...

MAG traffic generator study : survey data from Arizona State University

DOT National Transportation Integrated Search

1994-12-01

The Maricopa Association of Governments (MAG) is responsible for the travel demand models used to forecast multi-modal travel behavior in the Phoenix metropolitan area. The main campus of Arizona State University (ASU), located in Tempe, is one of th...

Battery cars on superconducting magnetically levitated carriers: One commuting solution

NASA Technical Reports Server (NTRS)

Briggs, B. Mike; Oman, Henry

1992-01-01

Commuting to work in an urban-suburban metropolitan environment is becoming an unpleasant time-wasting process. We applied the technology of communication management to this commuting problem. Communication management is a system-engineering tool that produced today's efficient telephone network. The resulting best commuting option is magnetically levitated carriers of two-passenger, battery-powered, personally-owned local-travel cars. A commuter drives a car to a nearby station, selects a destination, drives on a waiting carrier, and enters an accelerating ramp. A central computer selects an optimum 100 miles-per-hour trunk route, considering existing and forecast traffic; assigns the commuter a travel slot, and subsequently orders switching-station actions. The commuter uses the expensive facilities for only a few minutes during each trip. The cost of travel could be less than 6 cents per mile.

Forecasting the demand potential for STOL air transportation

NASA Technical Reports Server (NTRS)

Fan, S.; Horonjeff, R.; Kanafani, A.; Mogharabi, A.

1973-01-01

A process for predicting the potential demand for STOL aircraft was investigated to provide a conceptual framework, and an analytical methodology for estimating the STOL air transportation market. It was found that: (1) schedule frequency has the strongest effect on the traveler's choice among available routes, (2) work related business constitutes approximately 50% of total travel volume, and (3) air travel demand follows economic trends.

Design and implementation of ticket price forecasting system

NASA Astrophysics Data System (ADS)

Li, Yuling; Li, Zhichao

2018-05-01

With the advent of the aviation travel industry, a large number of data mining technologies have been developed to increase profits for airlines in the past two decades. The implementation of the digital optimization strategy leads to price discrimination, for example, similar seats on the same flight are purchased at different prices, depending on the time of purchase, the supplier, and so on. Price fluctuations make the prediction of ticket prices have application value. In this paper, a combination of ARMA algorithm and random forest algorithm is proposed to predict the price of air ticket. The experimental results show that the model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money. Based on the proposed model, using Python language and SQL Server database, we design and implement the ticket price forecasting system.

Modelling large scale human activity in San Francisco

NASA Astrophysics Data System (ADS)

Gonzalez, Marta

2010-03-01

Diverse group of people with a wide variety of schedules, activities and travel needs compose our cities nowadays. This represents a big challenge for modeling travel behaviors in urban environments; those models are of crucial interest for a wide variety of applications such as traffic forecasting, spreading of viruses, or measuring human exposure to air pollutants. The traditional means to obtain knowledge about travel behavior is limited to surveys on travel journeys. The obtained information is based in questionnaires that are usually costly to implement and with intrinsic limitations to cover large number of individuals and some problems of reliability. Using mobile phone data, we explore the basic characteristics of a model of human travel: The distribution of agents is proportional to the population density of a given region, and each agent has a characteristic trajectory size contain information on frequency of visits to different locations. Additionally we use a complementary data set given by smart subway fare cards offering us information about the exact time of each passenger getting in or getting out of the subway station and the coordinates of it. This allows us to uncover the temporal aspects of the mobility. Since we have the actual time and place of individual's origin and destination we can understand the temporal patterns in each visited location with further details. Integrating two described data set we provide a dynamical model of human travels that incorporates different aspects observed empirically.

Clarus multi-state regional demonstrations, evaluation of use case #5 : enhanced road weather content for traveler advisories.

DOT National Transportation Integrated Search

2011-05-11

This evaluation report presents an assessment of the benefits of a new road condition forecast tool that offers road weather information to travelers. The tool has been developed by Meridian Environmental Technology, Inc. and has been demonstrated in...

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

Super Ensemble-based Aviation Turbulence Guidance (SEATG) for Air Traffic Management (ATM)

NASA Astrophysics Data System (ADS)

Kim, Jung-Hoon; Chan, William; Sridhar, Banavar; Sharman, Robert

2014-05-01

Super Ensemble (ensemble of ten turbulence metrics from time-lagged ensemble members of weather forecast data)-based Aviation Turbulence Guidance (SEATG) is developed using Weather Research and Forecasting (WRF) model and in-situ eddy dissipation rate (EDR) observations equipped on commercial aircraft over the contiguous United States. SEATG is a sequence of five procedures including weather modeling, calculating turbulence metrics, mapping EDR-scale, evaluating metrics, and producing final SEATG forecast. This uses similar methodology to the operational Graphic Turbulence Guidance (GTG) with three major improvements. First, SEATG use a higher resolution (3-km) WRF model to capture cloud-resolving scale phenomena. Second, SEATG computes turbulence metrics for multiple forecasts that are combined at the same valid time resulting in an time-lagged ensemble of multiple turbulence metrics. Third, SEATG provides both deterministic and probabilistic turbulence forecasts to take into account weather uncertainties and user demands. It is found that the SEATG forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on 7-8 Sep 2012. And, overall performance skill of deterministic SEATG against the observed EDR data during this period is superior to any single turbulence metrics. Finally, probabilistic SEATG is used as an example application of turbulence forecast for air-traffic management. In this study, a simple Wind-Optimal Route (WOR) passing through the potential areas of probabilistic SEATG and Lateral Turbulence Avoidance Route (LTAR) taking into account the SEATG are calculated at z = 35000 ft (z = 12 km) from Los Angeles to John F. Kennedy international airports. As a result, WOR takes total of 239 minutes with 16 minutes of SEATG areas for 40% of moderate turbulence potential, while LTAR takes total of 252 minutes travel time that 5% of fuel would be additionally consumed to entirely avoid the moderate SEATG regions.

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.

Final report of the operation and demonstration test of short-range weather forecasting decision support within an advanced transportation weather information system (#Safe)

DOT National Transportation Integrated Search

2006-04-01

The purpose of the Advanced Transportation Weather Information System (ATWIS) was to provide en-route weather forecasts and road condition information to the traveling public across North Dakota and South Dakota. ATWIS was the first system to develop...

Wintertime component of the THORPEX Pacific-Asian Regional Campaign (T-PARC)

NASA Astrophysics Data System (ADS)

Song, Y.; Toth, Z.; Asuma, Y.; Reynolds, C.; Lngland, R.; Szunyogh, I.; Colle, B.; Chang, E.; Doyle, C.; Kats, A.

2009-04-01

The winter component of the T-PARC is an international field project that aims at improving high impact weather event forecasts for North America. The main objective is to understand how perturbations from the tropics, Eurasia and polar fronts travel through waveguide and turn into high impact weather events. Through adaptive observations by using manned aircrafts (NOAA G-IV and US Air force C-130s) and Russian rawinsonde network over data sparse regions, it is expected that accurate initial conditions will improve the numerical weather forecasts. Non-adaptive aircraft measurements over the Pacific Rim and part of India are also deployed through E-AMDAR program, which is expected to improve the background field over Asia where perturbations are initiated. The campaign is led by NOAA and joined by agencies and universities from US, Canada, Mexico, Japan, ECWMF, and Russia. While most observational data will be assimilated by operational centers to improve real time numerical weather predictions, post field studies will focus on aspects such as: data impact on forecast and analysis, dry and moist processes that affect the formation and propagation of perturbations, meso-scale storm structure, error growth, forecast "busts" under certain atmospheric regimes, and socio-economic applications such as costs and benefits of improved forecasts and their use by the public for high impact weather events. In particular, a Winter Olympics demonstration project (February 12 - February 28) is expected to be a test bed during winter T-PARC for real user outreach and application purposes. Effectiveness of existing targeting methods as well as new targeting methods in the 3-5 day lead time range will be pursued and other aspects related to data assimilation and numerical forecasts (both deterministic and ensemble forecasts) will be investigated within this project as well.

An empirical method for estimating travel times for wet volcanic mass flows

USGS Publications Warehouse

Pierson, Thomas C.

1998-01-01

Travel times for wet volcanic mass flows (debris avalanches and lahars) can be forecast as a function of distance from source when the approximate flow rate (peak discharge near the source) can be estimated beforehand. The near-source flow rate is primarily a function of initial flow volume, which should be possible to estimate to an order of magnitude on the basis of geologic, geomorphic, and hydrologic factors at a particular volcano. Least-squares best fits to plots of flow-front travel time as a function of distance from source provide predictive second-degree polynomial equations with high coefficients of determination for four broad size classes of flow based on near-source flow rate: extremely large flows (>1 000 000 m3/s), very large flows (10 000–1 000 000 m3/s), large flows (1000–10 000 m3/s), and moderate flows (100–1000 m3/s). A strong nonlinear correlation that exists between initial total flow volume and flow rate for "instantaneously" generated debris flows can be used to estimate near-source flow rates in advance. Differences in geomorphic controlling factors among different flows in the data sets have relatively little effect on the strong nonlinear correlations between travel time and distance from source. Differences in flow type may be important, especially for extremely large flows, but this could not be evaluated here. At a given distance away from a volcano, travel times can vary by approximately an order of magnitude depending on flow rate. The method can provide emergency-management officials a means for estimating time windows for evacuation of communities located in hazard zones downstream from potentially hazardous volcanoes.

Commercializing diarrhea vaccines for travelers

PubMed Central

López-Gigosos, Rosa; Segura-Moreno, Marina; Díez-Díaz, Rosa; Plaza, Elena; Mariscal, Alberto

2014-01-01

Continued growth in international travel and forecasts for a great increase in the number of people who travel from industrialized to emerging and developing countries make it necessary to develop and improve the capacity to provide health protection to travelers. Measures available to prevent some diseases include a currently limited number of marketed vaccines which represent extremely useful tools to protect travelers. Travelers very often experience diarrheal and gastrointestinal diseases for which some vaccines are available. Use of these vaccines should be evaluated based on traveler and travel destination and characteristics. Vaccines available include those against cholera, typhoid fever, hepatitis A, hepatitis E (only available in China), and rotavirus. The aim of this review is to provide an updated summary about each of the abovementioned vaccines that may be useful for making decisions regarding their use and assessing their indications in recommendations for travelers. PMID:24496054

An Evaluation of Alternatives for Processing of Administrative Pay Vouchers: A Simulation Approach.

DTIC Science & Technology

1982-09-01

Finance Travel Voucher Q-GERT Productivity Personnel Forecasts Simulation Model 20. ABSTRACT (Continue on reverse side if necessary end Jdentfly by...Finance Office (ACF) has devised a Point System for use in determining the productivity of the ACF Travel Section (ACFTT). This Point System sets values...5 to 5+) to be assigned to incoming travel vouchers based on voucher complexity. This research had set objectives of (1) building an ACFTT model that

1DVAR Analysis of Temperature and Humidity Using GPS Radio Occultation Data

NASA Technical Reports Server (NTRS)

Poli, Paul; Joiner, Joanna; Kursinski, Robert

2000-01-01

The Global Positioning System enables positioning in 3 dimensions about our planet. It has been operational since 1994. Twenty-four satellites are used to aclile\\,e this performance. The signals sent by these satellites are electromagnetic waves travelling through our atmosphere down to the small receivers used by the civilian community and the military. Because of varying meteorological conditions (namely, temperature and humidity changes along the ray path), the rays do not travel in a straight line. They bend towards the surface. As a consequence, the ray path between two points is longer than a straight line, and the time it takes for a signal to travel this distance is longer. In 1995, a small GPS receiver was launched on a satellite (GPS/MET). It become possible to perform radio occultations around the Earth: the source - one of the 24 GPS satellites - is seen by the receiver as it rises or sets around the other side of the Earth. When the source disappears, the receiver progressively loses the signals. By measuring accurately the time delay between the emission and the reception of the signal, it is possible to infer which part of the delay is due to the atmosphere. We use GPS/MET data to retrieve temperature and humidity profiles simultaneously. A specific method is implemented: it combines information from numerical forecasts and GPS observations in an optimal way. Comparing the result with an independent source of observations (weather balloons), we demonstrate that GPS data have the potential to improve weather analyses. We also show that improved temperature and humidity profiles can be obtained using information from a forecast model. This confirms results obtained in this study using simulated data.

Delaying the international spread of pandemic influenza.

PubMed

Cooper, Ben S; Pitman, Richard J; Edmunds, W John; Gay, Nigel J

2006-06-01

The recent emergence of hypervirulent subtypes of avian influenza has underlined the potentially devastating effects of pandemic influenza. Were such a virus to acquire the ability to spread efficiently between humans, control would almost certainly be hampered by limited vaccine supplies unless global spread could be substantially delayed. Moreover, the large increases that have occurred in international air travel might be expected to lead to more rapid global dissemination than in previous pandemics. To evaluate the potential of local control measures and travel restrictions to impede global dissemination, we developed stochastic models of the international spread of influenza based on extensions of coupled epidemic transmission models. These models have been shown to be capable of accurately forecasting local and global spread of epidemic and pandemic influenza. We show that under most scenarios restrictions on air travel are likely to be of surprisingly little value in delaying epidemics, unless almost all travel ceases very soon after epidemics are detected. Interventions to reduce local transmission of influenza are likely to be more effective at reducing the rate of global spread and less vulnerable to implementation delays than air travel restrictions. Nevertheless, under the most plausible scenarios, achievable delays are small compared with the time needed to accumulate substantial vaccine stocks.

A study on the epidemiological characteristics and infectious forecast model of malaria at Guangzhou Airport among Chinese returnees from Africa.

PubMed

Wu, Hui-Ming; Fang, Zhi-Qiang; Zhao, Dang; Chen, Yan-Ling; Liu, Chuan-Ge; Liang, Xi

2017-07-04

Cross-border malaria transmission in China is a major component of Chinese imported malaria cases. Such cases mostly are travellers returning from malaria endemic countries in Africa. By investigating malaria infectious status among Chinese worker in Africa, this study analysed the malaria risk factors, in order to establish infectious forecast model. Chinese returnees data from Africa were collected at Guangzhou Baiyun International Airport, Guangzhou, China between August 2015 and March 2016 and were included in the cross-sectional and retrospective survey. A total of 1492 respondents were included in the study with the majority consisting of junior middle school educated male. Most of them are manual and technical workers hired by companies, with average of 37.04 years of age. Overall malaria incidence rate of the population was 8.98% (134/1492), and there were no significant differences regarding age, gender, occupation, or team. Forecast model was developed on the basis of malaria risk factors including working country, local ecological environment type, work duration and intensity of mosquito bite prevention. The survey suggested that malaria incidence was high among Chinese travellers who had worked in Africa countries of heavy malaria burden. Further research on the frequency and severity of clinical episodes among Chinese travellers having worked in Africa is needed.

Predicting the impacts of new technology aircraft on international air transportation demand

NASA Technical Reports Server (NTRS)

Ausrotas, R. A.

1981-01-01

International air transportation to and from the United States was analyzed. Long term and short term effects and causes of travel are described. The applicability of econometric methods to forecast passenger travel is discussed. A nomograph is developed which shows the interaction of economic growth, airline yields, and quality of service in producing international traffic.

Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

PubMed

Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

2013-01-01

When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

Optical properties of volcanic ash: improving remote sensing observations.

NASA Astrophysics Data System (ADS)

Whelley, Patrick; Colarco, Peter; Aquila, Valentina; Krotkov, Nickolay; Bleacher, Jake; Garry, Brent; Young, Kelsey; Rocha Lima, Adriana; Martins, Vanderlei; Carn, Simon

2016-04-01

Many times each year explosive volcanic eruptions loft ash into the atmosphere. Global travel and trade rely on aircraft vulnerable to encounters with airborne ash. Volcanic ash advisory centers (VAACs) rely on dispersion forecasts and satellite data to issue timely warnings. To improve ash forecasts model developers and satellite data providers need realistic information about volcanic ash microphysical and optical properties. In anticipation of future large eruptions we can study smaller events to improve our remote sensing and modeling skills so when the next Pinatubo 1991 or larger eruption occurs, ash can confidently be tracked in a quantitative way. At distances >100km from their sources, drifting ash plumes, often above meteorological clouds, are not easily detected from conventional remote sensing platforms, save deriving their quantitative characteristics, such as mass density. Quantitative interpretation of these observations depends on a priori knowledge of the spectral optical properties of the ash in UV (>0.3μm) and TIR wavelengths (>10μm). Incorrect assumptions about the optical properties result in large errors in inferred column mass loading and size distribution, which misguide operational ash forecasts. Similarly, simulating ash properties in global climate models also requires some knowledge of optical properties to improve aerosol speciation.

Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts.

PubMed

Lowe, Rachel; Barcellos, Christovam; Coelho, Caio A S; Bailey, Trevor C; Coelho, Giovanini Evelim; Graham, Richard; Jupp, Tim; Ramalho, Walter Massa; Carvalho, Marilia Sá; Stephenson, David B; Rodó, Xavier

2014-07-01

With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. European Commission's Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro. Copyright © 2014 Elsevier Ltd. All rights reserved.

Transportation Sector Model of the National Energy Modeling System. Volume 1

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. Themore » current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.« less

Travel measurement issues

DOT National Transportation Integrated Search

1984-01-01

The proceedings include the following research papers: "Life-Cycle Concept: A Practical Application to Transportation Planning;" "Automobile Occupancy, Vehicle Trips, and Trip Purpose: Some Forecasting Problems;" "Successful Administration of a Maile...

Delaying the International Spread of Pandemic Influenza

PubMed Central

Cooper, Ben S; Pitman, Richard J; Edmunds, W. John; Gay, Nigel J

2006-01-01

Background The recent emergence of hypervirulent subtypes of avian influenza has underlined the potentially devastating effects of pandemic influenza. Were such a virus to acquire the ability to spread efficiently between humans, control would almost certainly be hampered by limited vaccine supplies unless global spread could be substantially delayed. Moreover, the large increases that have occurred in international air travel might be expected to lead to more rapid global dissemination than in previous pandemics. Methods and Findings To evaluate the potential of local control measures and travel restrictions to impede global dissemination, we developed stochastic models of the international spread of influenza based on extensions of coupled epidemic transmission models. These models have been shown to be capable of accurately forecasting local and global spread of epidemic and pandemic influenza. We show that under most scenarios restrictions on air travel are likely to be of surprisingly little value in delaying epidemics, unless almost all travel ceases very soon after epidemics are detected. Conclusions Interventions to reduce local transmission of influenza are likely to be more effective at reducing the rate of global spread and less vulnerable to implementation delays than air travel restrictions. Nevertheless, under the most plausible scenarios, achievable delays are small compared with the time needed to accumulate substantial vaccine stocks. PMID:16640458

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

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica

2017-12-01

Traditionally, navigation-related forecasts in central Europe cover short- to medium-range lead times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead times of several weeks up to several months currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of the Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known ensemble streamflow prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators (correlation coefficient; mean absolute error, skill score; mean squared error, skill score; and continuous ranked probability, skill score) and an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) as former studies for other regions of central Europe indicate, the accuracy and/or skill of the meteorological forcing used has a larger effect than the quality of initial hydrological conditions for relevant stations along the German waterways. (2) Despite the predictive limitations on longer lead times in central Europe, this study reveals the existence of a valuable predictability of streamflow on monthly up to seasonal timescales along the Rhine, upper Danube and Elbe waterways, and the Elbe achieves the highest skill and economic value. (3) The more physically based and the statistical approach are able to improve the predictive skills and economic value compared to climatology and the ESP approach. The specific forecast skill highly depends on the forecast location, the lead time and the season. (4) Currently, the statistical approach seems to be most skilful for the three waterways investigated. The lagged relationship between the monthly and/or seasonal streamflow and the climatic and/or oceanic variables vary between 1 month (e.g. local precipitation, temperature and soil moisture) up to 6 months (e.g. sea surface temperature). Besides focusing on improving the forecast methodology, especially by combining the individual approaches, the focus is on developing useful forecast products on monthly to seasonal timescales for waterway transport and to operationalize the related forecasting service.

Road Weather and Connected Vehicles

NASA Astrophysics Data System (ADS)

Pisano, P.; Boyce, B. C.

2015-12-01

On average, there are over 5.8 M vehicle crashes each year of which 23% are weather-related. Weather-related crashes are defined as those crashes that occur in adverse weather or on slick pavement. The vast majority of weather-related crashes happen on wet pavement (74%) and during rainfall (46%). Connected vehicle technologies hold the promise to transform road-weather management by providing improved road weather data in real time with greater temporal and geographic accuracy. This will dramatically expand the amount of data that can be used to assess, forecast, and address the impacts that weather has on roads, vehicles, and travelers. The use of vehicle-based measurements of the road and surrounding atmosphere with other, more traditional weather data sources, and create road and atmospheric hazard products for a variety of users. The broad availability of road weather data from mobile sources will vastly improve the ability to detect and forecast weather and road conditions, and will provide the capability to manage road-weather response on specific roadway links. The RWMP is currently demonstrating how weather, road conditions, and related vehicle data can be used for decision making through an innovative Integrated Mobile Observations project. FHWA is partnering with 3 DOTs (MN, MI, & NV) to pilot these applications. One is a mobile alerts application called the Motorists Advisories and Warnings (MAW) and a maintenance decision support application. These applications blend traditional weather information (e.g., radar, surface stations) with mobile vehicle data (e.g., temperature, brake status, wiper status) to determine current weather conditions. These weather conditions, and other road-travel-relevant information, are provided to users via web and phone applications. The MAW provides nowcasts and short-term forecasts out to 24 hours while the EMDSS application can provide forecasts up to 72 hours in advance. The three DOTs have placed readers and external road weather sensors on their maintenance fleet vehicles to collect vehicular and meteorological data. Data from all three states is sent to a processing system called the Pikalert® Vehicle Data Translator (VDT) that quality checks and uses the data to infer current and forecasted weather conditions.

Forecasting Winter Storms in the Sierra: A Social Science Perspective in Keeping the Public Safe without Negatively Impacting the Local Tourism Industry

NASA Astrophysics Data System (ADS)

Milne, R.; Wallmann, J.; Myrick, D. T.

2010-12-01

The National Weather Service Office in Reno is responsible for issuing Blizzard Warnings, Winter Storm Warnings, and Winter Weather Advisories for the Sierra, including the Lake Tahoe Basin and heavily traveled routes such as Interstate 80, Highway 395 and Highway 50. These forecast products prepare motorists for harsh travel conditions as well as those venturing into the backcountry, which are essential to the NWS mission of saving lives and property. During the winter season, millions of people from around the world visit the numerous world class ski resorts in the Sierra and the Lake Tahoe Basin, which is vital to the local economy. This situation creates a challenging decision for the forecasters to provide appropriate wording in winter statements to keep the public safe, without significantly impacting the local tourism-based economy. Numerous text and graphical products, including online weather briefings, are utilized by NWS Reno to highlight hazards in ensuring the public, businesses, and other government agencies are prepared for winter storms and take appropriate safety measures. The effectiveness of these product types will be explored, with past snowstorms used as examples to show how forecasters determine which type of text or graphical product is most appropriate to convey the hazardous weather threats.

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

NASA Technical Reports Server (NTRS)

Viken, Jeff; Dollyhigh, Samuel; Smith, Jeremy; Trani, Antonio; Baik, Hojong; Hinze, Nicholas; Ashiabor, Senanu

2006-01-01

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Analyzing the evolutionary mechanisms of the Air Transportation System-of-Systems using network theory and machine learning algorithms

NASA Astrophysics Data System (ADS)

Kotegawa, Tatsuya

Complexity in the Air Transportation System (ATS) arises from the intermingling of many independent physical resources, operational paradigms, and stakeholder interests, as well as the dynamic variation of these interactions over time. Currently, trade-offs and cost benefit analyses of new ATS concepts are carried out on system-wide evaluation simulations driven by air traffic forecasts that assume fixed airline routes. However, this does not well reflect reality as airlines regularly add and remove routes. A airline service route network evolution model that projects route addition and removal was created and combined with state-of-the-art air traffic forecast methods to better reflect the dynamic properties of the ATS in system-wide simulations. Guided by a system-of-systems framework, network theory metrics and machine learning algorithms were applied to develop the route network evolution models based on patterns extracted from historical data. Constructing the route addition section of the model posed the greatest challenge due to the large pool of new link candidates compared to the actual number of routes historically added to the network. Of the models explored, algorithms based on logistic regression, random forests, and support vector machines showed best route addition and removal forecast accuracies at approximately 20% and 40%, respectively, when validated with historical data. The combination of network evolution models and a system-wide evaluation tool quantified the impact of airline route network evolution on air traffic delay. The expected delay minutes when considering network evolution increased approximately 5% for a forecasted schedule on 3/19/2020. Performance trade-off studies between several airline route network topologies from the perspectives of passenger travel efficiency, fuel burn, and robustness were also conducted to provide bounds that could serve as targets for ATS transformation efforts. The series of analysis revealed that high robustness is achievable only in exchange of lower passenger travel and fuel burn efficiency. However, increase in the network density can mitigate this trade-off.

Calibration of a rainfall-runoff hydrological model and flood simulation using data assimilation

NASA Astrophysics Data System (ADS)

Piacentini, A.; Ricci, S. M.; Thual, O.; Coustau, M.; Marchandise, A.

2010-12-01

Rainfall-runoff models are crucial tools for long-term assessment of flash floods or real-time forecasting. This work focuses on the calibration of a distributed parsimonious event-based rainfall-runoff model using data assimilation. The model combines a SCS-derived runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The SCS-derived runoff model is parametrized by the initial water deficit, the discharge coefficient for the soil reservoir and a lagged discharge coefficient. The Lag and Route routing model is parametrized by the velocity of travel and the lag parameter. These parameters are assumed to be constant for a given catchment except for the initial water deficit and the velocity travel that are event-dependent (landuse, soil type and moisture initial conditions). In the present work, a BLUE filtering technique was used to calibrate the initial water deficit and the velocity travel for each flood event assimilating the first available discharge measurements at the catchment outlet. The advantages of the BLUE algorithm are its low computational cost and its convenient implementation, especially in the context of the calibration of a reduced number of parameters. The assimilation algorithm was applied on two Mediterranean catchment areas of different size and dynamics: Gardon d'Anduze and Lez. The Lez catchment, of 114 km2 drainage area, is located upstream Montpellier. It is a karstic catchment mainly affected by floods in autumn during intense rainstorms with short Lag-times and high discharge peaks (up to 480 m3.s-1 in September 2005). The Gardon d'Anduze catchment, mostly granite and schistose, of 545 km2 drainage area, lies over the departements of Lozère and Gard. It is often affected by flash and devasting floods (up to 3000 m3.s-1 in September 2002). The discharge observations at the beginning of the flood event are assimilated so that the BLUE algorithm provides optimal values for the initial water deficit and the velocity travel before the flood peak. These optimal values are used for a new simulation of the event in forecast mode (under the assumption of perfect rain-fall). On both catchments, it was shown over a significant number of flood events, that the data assimilation procedure improves the flood peak forecast. The improvement is globally more important for the Gardon d'Anduze catchment where the flood events are stronger. The peak can be forecasted up to 36 hours head of time assimilating very few observations (up to 4) during the rise of the water level. For multiple peaks events, the assimilation of the observations from the first peak leads to a significant improvement of the second peak simulation. It was also shown that the flood rise is often faster in reality than it is represented by the model. In this case and when the flood peak is under estimated in the simulation, the use of the first observations can be misleading for the data assimilation algorithm. The careful estimation of the observation and background error variances enabled the satisfying use of the data assimilation in these complex cases even though it does not allow the model error correction.

Simulating the Interactions Among Land Use, Transportation ...

EPA Pesticide Factsheets

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic and non-linear interactions among transportation, land use, and socioeconomic systems. System dynamics (SD) provides a common framework for modeling the complex interactions among transportation and other related systems. This study uses a SD model to simulate the cascading impacts of a proposed light rail transit (LRT) system in central North Carolina, USA. The Durham-Orange Light Rail Project (D-O LRP) SD model incorporates relationships among the land use, transportation, and economy sectors to simulate the complex feedbacks that give rise to the travel behavior changes forecasted by the region’s transportation model. This paper demonstrates the sensitivity of changes in travel behavior to the proposed LRT system and the assumptions that went into the transportation modeling, and compares those results to the impacts of an alternative fare-free transit system. SD models such as the D-O LRP SD model can complement transportation studies by providing valuable insight into the interdependent community systems that collectively contribute to travel behavior changes. Presented at the 35th International Conference of the System Dynamics Society in Cambridge, MA, July 18th, 2017

Model Information Exchange System (MIXS).

DOT National Transportation Integrated Search

2013-08-01

Many travel demand forecast models operate at state, regional, and local levels. While they share the same physical network in overlapping geographic areas, they use different and uncoordinated modeling networks. This creates difficulties for models ...

Speed and Delay Prediction Models for Planning Applications

DOT National Transportation Integrated Search

1999-01-01

Estimation of vehicle speed and delay is fundamental to many forms of : transportation planning analyses including air quality, long-range travel : forecasting, major investment studies, and congestion management systems. : However, existing planning...

The impact of underwater glider observations in the forecast of Hurricane Gonzalo (2014)

NASA Astrophysics Data System (ADS)

Goni, G. J.; Domingues, R. M.; Kim, H. S.; Domingues, R. M.; Halliwell, G. R., Jr.; Bringas, F.; Morell, J. M.; Pomales, L.; Baltes, R.

2017-12-01

The tropical Atlantic basin is one of seven global regions where tropical cyclones (TC) are commonly observed to originate and intensify from June to November. On average, approximately 12 TCs travel through the region every year, frequently affecting coastal, and highly populated areas. In an average year, 2 to 3 of them are categorized as intense hurricanes. Given the appropriate atmospheric conditions, TC intensification has been linked to ocean conditions, such as increased ocean heat content and enhanced salinity stratification near the surface. While errors in hurricane track forecasts have been reduced during the last years, errors in intensity forecasts remain mostly unchanged. Several studies have indicated that the use of in situ observations has the potential to improve the representation of the ocean to correctly initialize coupled hurricane intensity forecast models. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface thermal and salinity fields in support of TC intensity studies and forecasts has yet to be implemented. Autonomous technologies offer new and cost-effective opportunities to accomplish this objective. We highlight here a partnership effort that utilize underwater gliders to better understand air-sea processes during high wind events, and are particularly geared towards improving hurricane intensity forecasts. Results are presented for Hurricane Gonzalo (2014), where glider observations obtained in the tropical Atlantic: Helped to provide an accurate description of the upper ocean conditions, that included the presence of a low salinity barrier layer; Allowed a detailed analysis of the upper ocean response to hurricane force winds of Gonzalo; Improved the initialization of the ocean in a coupled ocean-atmosphere numerical model; and together with observations from other ocean observing platforms, substantially reduced the error in intensity forecast using the HYCOM-HWRF model. Data collected by this project are transmitted in real-time to the Global Telecommunication System, distributed through the institutional web pages, by the IOOS Glider Data Assembly Center, and by NCEI, and assimilated in real-time numerical weather forecast models.

Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

Physics-based coastal current tomographic tracking using a Kalman filter.

PubMed

Wang, Tongchen; Zhang, Ying; Yang, T C; Chen, Huifang; Xu, Wen

2018-05-01

Ocean acoustic tomography can be used based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area to invert/map the ocean current inside the area. Data at different times can be related using a Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different times are treated independently. Real data are also examined.

Better hurricane forecasts

NASA Astrophysics Data System (ADS)

Friebele, Elaine

People living in coastal areas can rely on better hurricane predictions because forecasters now have nearly instant access to global wind data. Measurements of wind speed and direction over the world's oceans are available within 3 hours of measurement from the Japanese satellite ADEOS (Advanced Earth Observing Satellite).Wind parameters at 25-km resolution are being measured by NASA's scatterometer traveling on the Japanese satellite ADEOS (Advanced Earth Observing Satellite). “The high accuracy and spatial resolution of the data were quickly recognized by our forecasters, who have been starved for data over significant expanses of the world's oceans,” said Jim Hoke, director of NOAA's Marine Prediction Center.

Air Traffic Forecasting at the Port Authority of New York and New Jersey

NASA Technical Reports Server (NTRS)

Augustine, J. G.

1972-01-01

Procedures for conducting air traffic forecasts with specific application to the Port Authority of New York and New Jersey are discussed. The procedure relates air travel growth to detailed socio-economic and demographic characteristics of the U.S. population rather than to aggregate economic data such as Gross National Product, personal income, and industrial production. Charts are presented to show the relationship between various selected characteristics and the use of air transportation facilities.

The Integrated Airport Competition Model, 1998

NASA Technical Reports Server (NTRS)

Veldhuis, J.; Essers, I.; Bakker, D.; Cohn, N.; Kroes, E.

1999-01-01

This paper addresses recent model development by the Directorate General of Civil Aviation (DGCA) and Hague Consulting Group (HCG) concerning long-distance travel. Long-distance travel demand is growing very quickly and raising a great deal of economic and policy issues. There is increasing competition among the main Western European airports, and smaller, regional airports are fighting for market share. New modes of transport, such as high speed rail, are also coming into the picture and affect the mode split for medium distance transport within Europe. Developments such as these are demanding the attention of policy makers and a tool is required for their analysis. For DGCA, Hague Consulting Group has developed a model system to provide answers to the policy questions posed by these expected trends, and to identify areas where policy makers can influence the traveller choices. The development of this model system, the Integrated Airport Competition Model/integraal Luchthaven Competitie Model (ILCM), began in 1992. Since that time the sub-models, input data and user interface have been expanded, updated and improved. HCG and DGCA have transformed the ILCM from a prototype into an operational forecasting tool.

The Integrated Airport Competition Model, 1998

NASA Technical Reports Server (NTRS)

Veldhuis, J.; Essers, I.; Bakker, D.; Cohn, N.; Kroes, E.

1999-01-01

This paper addresses recent model development by the Directorate General of Civil Aviation (DGCA) and Hague Consulting Group (HCG) concerning long-distance travel, Long-distance travel demand is growing very quickly and raising a great deal of economic and policy issues. There is increasing competition among the main Western European airports, and smaller, regional airports are fighting for market share. New modes of transport, such as high speed rail, arc also coming into the picture and affect the mode split for medium distance transport within Europe. Developments such as these are demanding the attention of policy makers and a tool is required for their analysis. For DGCA, Hague Consulting Group has developed a model system to provide answers to the policy questions posed by these expected trends, and to identify areas where policy makers can influence the traveller choices. The development of this model system, the Integrated Airport Competition Model/Integral Luchthaven Competitive Model (ILCM), began in 1992. Since that time the sub-models, input data and user interface have been expanded, updated and improved. HCG and DGCA have transformed the ILCM from a prototype into an operational forecasting tool.

Census mapbook for transportation planning.

DOT National Transportation Integrated Search

1994-12-01

Geographic display of Census data in transportation planning and policy decisions are compiled in a report of 49 maps, depicting use of the data in applications such as travel demand model development and model validation, population forecasting, cor...

Forecasting bicycle and pedestrian usage and research data collection equipment.

DOT National Transportation Integrated Search

2010-01-01

During recent years, community leaders and transportation professionals in the Austin area have increased their interest in pedestrian and bicycle travel. Advocacy groups, task forces, bicycling clubs, and volunteer organizations encourage government...

Enhanced road weather content for travel advisories : Clarus regional demonstrations.

DOT National Transportation Integrated Search

2011-01-01

Statewide transportation information systems need road weather and road condition forecasts to improve safety and mobility for transportation users. Under the Clarus Initiatives regional demonstrations enhanced road weather content was developed f...

Computationally-Efficient Minimum-Time Aircraft Routes in the Presence of Winds

NASA Technical Reports Server (NTRS)

Jardin, Matthew R.

2004-01-01

A computationally efficient algorithm for minimizing the flight time of an aircraft in a variable wind field has been invented. The algorithm, referred to as Neighboring Optimal Wind Routing (NOWR), is based upon neighboring-optimal-control (NOC) concepts and achieves minimum-time paths by adjusting aircraft heading according to wind conditions at an arbitrary number of wind measurement points along the flight route. The NOWR algorithm may either be used in a fast-time mode to compute minimum- time routes prior to flight, or may be used in a feedback mode to adjust aircraft heading in real-time. By traveling minimum-time routes instead of direct great-circle (direct) routes, flights across the United States can save an average of about 7 minutes, and as much as one hour of flight time during periods of strong jet-stream winds. The neighboring optimal routes computed via the NOWR technique have been shown to be within 1.5 percent of the absolute minimum-time routes for flights across the continental United States. On a typical 450-MHz Sun Ultra workstation, the NOWR algorithm produces complete minimum-time routes in less than 40 milliseconds. This corresponds to a rate of 25 optimal routes per second. The closest comparable optimization technique runs approximately 10 times slower. Airlines currently use various trial-and-error search techniques to determine which of a set of commonly traveled routes will minimize flight time. These algorithms are too computationally expensive for use in real-time systems, or in systems where many optimal routes need to be computed in a short amount of time. Instead of operating in real-time, airlines will typically plan a trajectory several hours in advance using wind forecasts. If winds change significantly from forecasts, the resulting flights will no longer be minimum-time. The need for a computationally efficient wind-optimal routing algorithm is even greater in the case of new air-traffic-control automation concepts. For air-traffic-control automation, thousands of wind-optimal routes may need to be computed and checked for conflicts in just a few minutes. These factors motivated the need for a more efficient wind-optimal routing algorithm.

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

Implications of the 26 December 2004 Sumatra-Andaman earthquake on tsunami forecast and assessment models for great subduction-zone earthquakes

USGS Publications Warehouse

Geist, Eric L.; Titov, Vasily V.; Arcas, Diego; Pollitz, Fred F.; Bilek, Susan L.

2007-01-01

Results from different tsunami forecasting and hazard assessment models are compared with observed tsunami wave heights from the 26 December 2004 Indian Ocean tsunami. Forecast models are based on initial earthquake information and are used to estimate tsunami wave heights during propagation. An empirical forecast relationship based only on seismic moment provides a close estimate to the observed mean regional and maximum local tsunami runup heights for the 2004 Indian Ocean tsunami but underestimates mean regional tsunami heights at azimuths in line with the tsunami beaming pattern (e.g., Sri Lanka, Thailand). Standard forecast models developed from subfault discretization of earthquake rupture, in which deep- ocean sea level observations are used to constrain slip, are also tested. Forecast models of this type use tsunami time-series measurements at points in the deep ocean. As a proxy for the 2004 Indian Ocean tsunami, a transect of deep-ocean tsunami amplitudes recorded by satellite altimetry is used to constrain slip along four subfaults of the M >9 Sumatra–Andaman earthquake. This proxy model performs well in comparison to observed tsunami wave heights, travel times, and inundation patterns at Banda Aceh. Hypothetical tsunami hazard assessments models based on end- member estimates for average slip and rupture length (Mw 9.0–9.3) are compared with tsunami observations. Using average slip (low end member) and rupture length (high end member) (Mw 9.14) consistent with many seismic, geodetic, and tsunami inversions adequately estimates tsunami runup in most regions, except the extreme runup in the western Aceh province. The high slip that occurred in the southern part of the rupture zone linked to runup in this location is a larger fluctuation than expected from standard stochastic slip models. In addition, excess moment release (∼9%) deduced from geodetic studies in comparison to seismic moment estimates may generate additional tsunami energy, if the exponential time constant of slip is less than approximately 1 hr. Overall, there is significant variation in assessed runup heights caused by quantifiable uncertainty in both first-order source parameters (e.g., rupture length, slip-length scaling) and spatiotemporal complexity of earthquake rupture.

A Transportation Modeling Primer

DOT National Transportation Integrated Search

2006-06-01

This primer is intended to explain the urban transportation modeling process works, the assumptions made and the steps used to forecast travel demand for urban transportation planning. This is done in order to help to understand the process and its i...

Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa

NASA Astrophysics Data System (ADS)

Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter

2013-04-01

Despite being widely recognised as an efficient tool in the operational management of water resources, rainfall and streamflow forecasts are currently not utilised in water management practice in the Incomati Basin in Southern Africa. Although, there have been initiatives for forecasting streamflow in the Sabie and Crocodile sub-basins, the outputs of these have found little use because of scepticism on the accuracy and reliability of the information, or the relevance of the information provided to the needs of the water managers. The process of improving these forecasts is underway, but as yet the actual needs of the forecasts are unclear and scope of the ongoing initiatives remains very limited. In this study questionnaires and focused group interviews were used to establish the need, potential use, benefit and required accuracy of rainfall and streamflow forecasts in the Incomati Basin. Thirty five interviews were conducted with professionals engaged in water sector and detailed discussions were held with water institutions, including the Inkomati Catchment Management Agency (ICMA), Komati Basin Water Authority (KOBWA), South African Weather Service (SAWS), water managers, dam operators, water experts, farmers and other water users in the Basin. Survey results show that about 97% of the respondents receive weather forecasts. In contrast to expectations, only 5% have access to the streamflow forecast. In the weather forecast, the most important variables were considered to be rainfall and temperature at daily and weekly time scales. Moreover, forecasts of global climatic indices such as El Niño or La Niña were neither received nor demanded. There was limited demand and/or awareness of flood and drought forecasts including the information on their linkages with global climatic indices. While the majority of respondents indicate the need and indeed use the weather forecast, the provision, communication and interpretation were in general found to be with too little detail and clarity. In some cases this was attributed to the short time and space allotted in media such as television and newspapers respectively. Major uses of the weather forecast were made in personal planning i.e., travelling (29%) and dressing (23%). The usefulness in water sector was reported for water allocation (23%), farming (11%) and flood monitoring (9%), but was considered as a factor having minor influence on the actual decision making in operational water management mainly due to uncertainty of the weather forecast, difference in the time scale and institutional arrangements. In the incidences where streamflow forecasts were received (5% of the cases), its application in decision making was not carried out due to high uncertainty. Moreover, dam operators indicated weekly streamflow forecast as very important in releasing water for agriculture but this was not the format in which forecasts were available to them. Generally, users affirmed the accuracy and benefits of weather forecasts and had no major concerns on the impacts of wrong forecasts. However, respondents indicated the need to improve the accuracy and accessibility of the forecast. Likewise, water managers expressed the need for both rainfall and flow forecasts but indicated that they face hindrances due to financial and human resource constraints. This shows that there is a need to strengthen water related forecasts and the consequent uses in the basin. This can be done through collaboration among forecasting and water organisations such as the SAWS, Research Institutions and users like ICMA, KOBWA and farmers. Collaboration between the meteorology and water resources sectors is important to establish consistent forecast information. The forecasts themselves should be detailed and user specific to ensure these are indeed used and can answer to the needs of the users.

A model to forecast peak spreading.

DOT National Transportation Integrated Search

2012-04-01

As traffic congestion increases, the K-factor, defined as the proportion of the 24-hour traffic volume that occurs during the peak hour, may decrease. This behavioral response is known as peak spreading: as congestion grows during the peak travel tim...

Integrated Land - Use , Transportation and Environmental Modeling : Validation Case Studies

DOT National Transportation Integrated Search

2010-08-01

For decades the transportation-planning research community has acknowledged the interactions between the evolution of our transportation systems and our land-use, and the need to unify the practices of land-use forecasting and travel-demand modeling ...

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

Workers monitor NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic and secured onto a portable work stand, as it travels between the airlock of Building 2 to the high bay of Building 1 at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

Seismic Forecasting of Solar Activity

NASA Technical Reports Server (NTRS)

Braun, Douglas; Lindsey, Charles

2001-01-01

We have developed and improved helioseismic imaging techniques of the far-side of the Sun as part of a synoptic monitor of solar activity. In collaboration with the MIDI team at Stanford University we are routinely applying our analysis to images within 24 hours of their acquisition by SOHO. For the first time, real-time seismic maps of large active regions on the Sun's far surface are publicly available. The synoptic images show examples of active regions persisting for one or more solar rotations, as well as those initially detected forming on the solar far side. Until recently, imaging the far surface of the Sun has been essentially blind to active regions more than about 50 degrees from the antipode of disk center. In a paper recently accepted for publication, we have demonstrated how acoustic travel-time perturbations may be mapped over the entire hemisphere of the Sun facing away from the Earth, including the polar regions. In addition to offering significant improvements to ongoing space weather forecasting efforts, the procedure offers the possibility of local seismic monitoring of both the temporal and spatial variations in the acoustic properties of the Sun over the entire far surface.

Joint NOAA/NWS/USGS prototype debris flow warning system for recently burned areas in Southern California

USGS Publications Warehouse

Restrepo, P.; Jorgensen, D.P.; Cannon, S.H.; Costa, J.; Laber, J.; Major, J.; Martner, B.; Purpura, J.; Werner, K.

2008-01-01

Debris flows, also known as mudslides, are composed gravity-driven mixtures of sediment and water that travel through steep channels, over open hillslopes, and the like. Addressing this issue, US Geological Survey (USGS) and NOAA have established a debris-flow warning system that has the ability to monitor and forecast precipitation and issue timely weather hazard warning. In 2005, this joint NOAA-USGS prototype debris-flow warning system was issued in Southern California and as a result, it has provided valuable information to emergency managers in affected communities.

Future Weather Forecasting in the Year 2020-Investing in Technology Today: Improving Weather and Environmental Predictions

NASA Technical Reports Server (NTRS)

Anthes, Richard; Schoeberl, Mark

2000-01-01

Fast-forward twenty years to the nightly simultaneous TV/webcast. Accurate 8-14 day regional forecasts will be available as will be a whole host of linked products including economic impact, travel, energy usage, etc. On-demand, personalized street-level forecasts will be downloaded into your PDA. Your home system will automatically update the products of interest to you (e.g. severe storm forecasts, hurricane predictions, etc). Short and long range climate forecasts will be used by your "Quicken 2020" to make suggest changes in your "futures" investment portfolio. Through a lively and informative multi-media presentation, leading Space-Earth Science Researchers and Technologists will share their vision for the year 2020, offering a possible futuristic forecast enabled through the application of new technologies under development today. Copies of the 'broadcast' will be available on Beta Tape for your own future use. If sufficient interest exists, the program may also be made available for broadcasters wishing to do stand-ups with roll-ins from the San Francisco meeting for their viewers back home.

Bringing freight components into statewide and regional travel demand forecasting: part 1 : final report.

DOT National Transportation Integrated Search

2016-01-01

Transportation decision makers have the difficult task of investment decision making having limited resources while : maximizing benefit to the transportation system. Given the growth in freight transport and its importance to national, : state, and ...

Use of Pavement Temperature Measurements for Winter Maintenance Decisions

DOT National Transportation Integrated Search

1988-01-01

Formation of ice and frost on roadways and bridges presents a significant potential impediment to safe winter travel in Iowa. Roadway surface temperatures are not measured routinely by the National Weather Service and are not part of public forecasts...

Use of pavement temperature measurements for winter maintenance decisions

DOT National Transportation Integrated Search

1998-01-01

Formation of ice and frost on roadways and bridges presents a significant potential impediment to safe winter travel in Iowa. Roadway surface temperatures are not measured routinely by the National Weather Service and are not part of public forecasts...

Short-Term Energy Outlook Model Documentation: Motor Gasoline Consumption Model

EIA Publications

2011-01-01

The motor gasoline consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of total U.S. consumption of motor gasolien based on estimates of vehicle miles traveled and average vehicle fuel economy.

Modeling Trip Duration for Mobile Source Emissions Forecasting

DOT National Transportation Integrated Search

2000-08-01

The distribution of the duration of trips in a metropolitan area is an important input to estimating area-wide running loss emissions, operating mode fractions and vehicle miles of travel (VMT) accumulated on local roads in the region. In the current...

Use of Pavement Temperature Measurements for Winter Maintenance Decisions

DOT National Transportation Integrated Search

1998-01-01

Formation of ice and frost on roadways and bridges presents a significant potential impediment to safe winter travel in Iowa. Roadway surface temperatures are not measured routinely by the National Weather Service and are not part of public forecasts...

Aviation and the environment : airport operations and future growth present environmental challenges

DOT National Transportation Integrated Search

2000-08-01

Many of the nation's commercial service airports are operating at or near capacity and are under increasing pressure to expand their operations to accommodate the growing demand for domestic air travel-forecast by the Federal Aviation Administration ...

The scaling law of human travel - A message from George

NASA Astrophysics Data System (ADS)

Brockmann, Dirk; Hufnagel, Lars

The dispersal of individuals of a species is the key driving force of various spatiotemporal phenomena which occur on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools.1-3 The geographic spread of human infectious diseases such as influenza, measles and the recent severe acute respiratory syndrome (SARS) is essentially promoted by human travel which occurs on many length scales and is sustained by a variety of means of trans-portation4-8. In the light of increasing international trade, intensified human traffic, and an imminent influenza A pandemic the knowledge of dynamical and statistical properties of human dispersal is of fundamental importance and acute. 7,9,10 A quantitative statistical theory for human travel and concomitant reliable forecasts would substantially improve and extend existing prevention strategies. Despite its crucial role, a quantitative assessment of human dispersal remains elusive and the opinion that humans disperse diffusively still prevails in many models. 11 In this chapter we will report on a recently developed technique which permits a solid and quantitative assessment of human dispersal on geographical scales.12 The key idea is to infer the statistical properties of human travel by analysing the geographic circulation of individual bank notes for which comprehensive datasets are collected at online bill-tracking websites. The analysis shows that the distribution of traveling distances decays as a power law, indicating that the movement of bank notes is reminiscent of superdiffusive, scale free random walks known as Lévy flights.13 Secondly, the probability of remaining in a small, spatially confined region for a time T is dominated by heavy tails which attenuate superdiffusive dispersal. We will show that the dispersal of bank notes can be described on many spatiotemporal scales by a two parameter continuous time random walk (CTRW) model to a surprising accuracy. We will provide a brief introduction to continuous time random walk theory14 and will show that human disperal is an ambivalent, effectively superdiffusive process.

Inferring atmospheric weather conditions in volcanic environments using infrasound

NASA Astrophysics Data System (ADS)

Ortiz, H. D.; Johnson, J. B.; Ruiz, M. C.

2015-12-01

We use infrasound produced by Tungurahua Volcano (Ecuador) to infer local time-varying atmospheric conditions, which can be used to improve gas flux measurements and tephra dispersal modeling. Physical properties of the atmosphere, including wind and temperature (which controls adiabatic sound speed), can be quantified by studying the travel times of acoustic waves produced during volcanic activity. The travel times between Tungurahua's vent and five infrasound stations located in a network configuration over an area of 90 km2 were used in this study. We are able to quantify the arrival time differences of acoustic waves for ten unique station pairs and use this information to model the average speed of sound between source and receiver. To identify what parameters best fit the observed arrival times, we perform a grid search for a homogeneous two-dimensional wind velocity as well as for air temperature. Due to travel time dependence on the specific path taken by waves, we account for topography using a 5 meter resolution digital elevation model of Tungurahua. To investigate the time-varying atmospheric structure we use data recorded at Tungurahua volcano, during a strombolian eruptive phase in August 2012, however the methodology can be applied to continuous network infrasound data collected since July 2006 as part of the Japanese-Ecuadorian Cooperation Project: "Enhancement of the Volcano Monitoring Capacity in Ecuador". We propose that the computation of wind velocities will help to improve gas flux measurements that are based on remote sensing techniques like Differential Optical Absorption Spectroscopy (DOAS), resulting in better estimates of sulfur fluxes that can then be related to magma fluxing into the volcanic system. Further, wind field quantification close to the volcano can improve numerical models that are used to forecast tephra deposits, thereby helping to mitigate their effect on inhabitants, infrastructure, livestock, and crops.

VMT Mix Modeling for Mobile Source Emissions Forecasting: Formulation and Empirical Application

DOT National Transportation Integrated Search

2000-05-01

The purpose of the current report is to propose and implement a methodology for obtaining improved link-specific vehicle miles of travel (VMT) mix values compared to those obtained from existent methods. Specifically, the research is developing a fra...

The Transshipment Problem in Travel Forecasting: Preliminary Analysis of the Ontario Commercial Vehicle Survey

DOT National Transportation Integrated Search

2009-05-15

Transshipment has large implications for the provision of public infrastructure, and most of the available data on commodity shipments within a single country do not identity any transshipment points along their way. The Ontario Commercial Vehicle Su...

Urban commodity flow data collection and analysis using global positioning systems

DOT National Transportation Integrated Search

2006-05-01

The Urban Commodity Flow Data Collection and Analysis project is a joint effort between : Oregon State University (OSU) and the City of Portland, Oregon Travel Forecasting Group (Port : of Portland and Portland Metro). The broad objective of this pro...

TCRP H-37 Characteristics of Premium Transit Services That Affect Mode Choice: Summary of Phase 1

DOT National Transportation Integrated Search

2010-11-15

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models...

Estimated and forecasted trends in domain specific time-use and energy expenditure among adults in Russia

PubMed Central

2014-01-01

Background Examination of historical trends and projections in estimated energy expenditure in Russia is important given the country’s economic downturns and growth. Methods Nationally representative data from the Russia Longitudinal Monitoring Survey (RLMS) from 1995–2011 was used to determine the metabolic equivalents of task (MET)-hours per week from occupational, domestic, travel, and active leisure physical activity (PA) domains, as well as sedentary leisure time (hours per week) among adults 18–60 years. Additionally, we projected what these values would be like in 2020 and 2030 if observed trends continue. Results Among male adults, the largest contributor to total PA was occupational PA followed by travel PA. In contrast, domestic PA followed by occupational PA contributed most to total PA among female adults. Total PA was 282.9 MET-hours per week in 1995 and declined to 231.7 in 2011. Total PA is projected to decrease to 216.5 MET-hours per week in 2020 and to 193.0 MET-hours per week in 2030. The greatest relative declines are occurring in travel PA. Female adults are also exhibiting significant declines in domestic PA. Changes in occupational and active leisure PA are less distinct. Conclusions Policies and initiatives are needed to counteract the long-term decline of overall physical activity linked with a modernizing lifestyle and economy among Russian adults. PMID:24475868

Estimated and forecasted trends in domain specific time-use and energy expenditure among adults in Russia.

PubMed

Dearth-Wesley, Tracy; Popkin, Barry M; Ng, Shu Wen

2014-01-30

Examination of historical trends and projections in estimated energy expenditure in Russia is important given the country's economic downturns and growth. Nationally representative data from the Russia Longitudinal Monitoring Survey (RLMS) from 1995-2011 was used to determine the metabolic equivalents of task (MET)-hours per week from occupational, domestic, travel, and active leisure physical activity (PA) domains, as well as sedentary leisure time (hours per week) among adults 18-60 years. Additionally, we projected what these values would be like in 2020 and 2030 if observed trends continue. Among male adults, the largest contributor to total PA was occupational PA followed by travel PA. In contrast, domestic PA followed by occupational PA contributed most to total PA among female adults. Total PA was 282.9 MET-hours per week in 1995 and declined to 231.7 in 2011. Total PA is projected to decrease to 216.5 MET-hours per week in 2020 and to 193.0 MET-hours per week in 2030. The greatest relative declines are occurring in travel PA. Female adults are also exhibiting significant declines in domestic PA. Changes in occupational and active leisure PA are less distinct. Policies and initiatives are needed to counteract the long-term decline of overall physical activity linked with a modernizing lifestyle and economy among Russian adults.

Ozone distributions over southern Lake Michigan: comparisons between ferry-based observations, shoreline-based DOAS observations and model forecasts

NASA Astrophysics Data System (ADS)

Cleary, P. A.; Fuhrman, N.; Schulz, L.; Schafer, J.; Fillingham, J.; Bootsma, H.; McQueen, J.; Tang, Y.; Langel, T.; McKeen, S.; Williams, E. J.; Brown, S. S.

2015-05-01

Air quality forecast models typically predict large summertime ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline differential optical absorption spectroscopy (DOAS) observations in southeastern Wisconsin and as predicted by the Community Multiscale Air Quality (CMAQ) model. From 2008 to 2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008 to 2010 measurements of ambient ozone were conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI, and Muskegon, MI, up to six times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha, with little dependence on position of the ferry or temperature and with greatest differences during evening and night. Concurrent 1-48 h forecasts from the CMAQ model in the upper Midwestern region surrounding Lake Michigan were compared to ferry ozone measurements, shoreline DOAS measurements and Environmental Protection Agency (EPA) station measurements. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. Trends in the bias with respect to location and time of day were explored showing non-uniformity in model bias over the lake. Model ozone bias was consistently high over the lake in comparison to land-based measurements, with highest biases for 25-48 h after initialization.

TBEST model enhancements : parcel level demographic data capabilities and exploration of enhanced trip attraction capabilities.

DOT National Transportation Integrated Search

2011-09-01

"FDOT, in pursuit of its role to assist in providing public transportation services in Florida, has made a substantial : research investment in a travel demand forecasting tool for public transportation known as Transit Boardings : Estimation and Sim...

Simplified 4-Step Transportation Planning Process For Any Sized Area

DOT National Transportation Integrated Search

1999-01-01

This paper presents a streamlined version of the Washington, D.C. region's : 4-step travel demand forecasting model. The purpose for streamlining the : model was to have a model that could: replicate the regional model, and be run : in a new s...

Proceedings of the Workshop on Transportation/Urban Form Interactions held at Cambridge, MA. on August 14-15, 1978

DOT National Transportation Integrated Search

1979-06-01

Contents: A form of utility function for the UMOT model; An analysis of transportation/land use interactions; Toward a methodology to shape urban structure; Approaches for improving urban travel forecasts; Quasi-dynamic urban location models with end...

Real-time determination of the worst tsunami scenario based on Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Furuya, Takashi; Koshimura, Shunichi; Hino, Ryota; Ohta, Yusaku; Inoue, Takuya

2016-04-01

In recent years, real-time tsunami inundation forecasting has been developed with the advances of dense seismic monitoring, GPS Earth observation, offshore tsunami observation networks, and high-performance computing infrastructure (Koshimura et al., 2014). Several uncertainties are involved in tsunami inundation modeling and it is believed that tsunami generation model is one of the great uncertain sources. Uncertain tsunami source model has risk to underestimate tsunami height, extent of inundation zone, and damage. Tsunami source inversion using observed seismic, geodetic and tsunami data is the most effective to avoid underestimation of tsunami, but needs to expect more time to acquire the observed data and this limitation makes difficult to terminate real-time tsunami inundation forecasting within sufficient time. Not waiting for the precise tsunami observation information, but from disaster management point of view, we aim to determine the worst tsunami source scenario, for the use of real-time tsunami inundation forecasting and mapping, using the seismic information of Earthquake Early Warning (EEW) that can be obtained immediately after the event triggered. After an earthquake occurs, JMA's EEW estimates magnitude and hypocenter. With the constraints of earthquake magnitude, hypocenter and scaling law, we determine possible multi tsunami source scenarios and start searching the worst one by the superposition of pre-computed tsunami Green's functions, i.e. time series of tsunami height at offshore points corresponding to 2-dimensional Gaussian unit source, e.g. Tsushima et al., 2014. Scenario analysis of our method consists of following 2 steps. (1) Searching the worst scenario range by calculating 90 scenarios with various strike and fault-position. From maximum tsunami height of 90 scenarios, we determine a narrower strike range which causes high tsunami height in the area of concern. (2) Calculating 900 scenarios that have different strike, dip, length, width, depth and fault-position. Note that strike is limited with the range obtained from 90 scenarios calculation. From 900 scenarios, we determine the worst tsunami scenarios from disaster management point of view, such as the one with shortest travel time and the highest water level. The method was applied to a hypothetical-earthquake, and verified if it can effectively search the worst tsunami source scenario in real-time, to be used as an input of real-time tsunami inundation forecasting.

Modeling solar energetic particle events using ENLIL heliosphere simulations

NASA Astrophysics Data System (ADS)

Luhmann, J. G.; Mays, M. L.; Odstrcil, D.; Li, Yan; Bain, H.; Lee, C. O.; Galvin, A. B.; Mewaldt, R. A.; Cohen, C. M. S.; Leske, R. A.; Larson, D.; Futaana, Y.

2017-07-01

Solar energetic particle (SEP) event modeling has gained renewed attention in part because of the availability of a decade of multipoint measurements from STEREO and L1 spacecraft at 1 AU. These observations are coupled with improving simulations of the geometry and strength of heliospheric shocks obtained by using coronagraph images to send erupted material into realistic solar wind backgrounds. The STEREO and ACE measurements in particular have highlighted the sometimes surprisingly widespread nature of SEP events. It is thus an opportune time for testing SEP models, which typically focus on protons 1-100 MeV, toward both physical insight to these observations and potentially useful space radiation environment forecasting tools. Some approaches emphasize the concept of particle acceleration and propagation from close to the Sun, while others emphasize the local field line connection to a traveling, evolving shock source. Among the latter is the previously introduced SEPMOD treatment, based on the widely accessible and well-exercised WSA-ENLIL-cone model. SEPMOD produces SEP proton time profiles at any location within the ENLIL domain. Here we demonstrate a SEPMOD version that accommodates multiple, concurrent shock sources occurring over periods of several weeks. The results illustrate the importance of considering longer-duration time periods and multiple CME contributions in analyzing, modeling, and forecasting SEP events.

Tsunami Forecasting in the Atlantic Basin

NASA Astrophysics Data System (ADS)

Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

2012-12-01

The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre-computation - starting with those sources that carry the highest risk. Model computation zones are confined to regions at risk to save computation time. For example, Atlantic sources have been shown to not propagate into the Gulf of Mexico. Therefore, fine grid computations are not performed in the Gulf for Atlantic sources. Outputs from the Atlantic model include forecast marigrams at selected sites, maximum amplitudes, drawdowns, and currents for all coastal points. The maximum amplitude maps will be supplemented with contoured energy flux maps which show more clearly the effects of bathymetric features on tsunami wave propagation. During an event, forecast marigrams will be compared to observations to adjust the model results. The modified forecasts will then be used to set alert levels between coastal breakpoints, and provided to emergency management.

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions (proceedings)

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Flood forecasting with DDD-application of a parsimonious hydrological model in operational flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Haddeland, Ingjerd

2014-05-01

A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.

A robust method to forecast volcanic ash clouds

USGS Publications Warehouse

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

2012-01-01

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

KSC-2014-4553

NASA Image and Video Library

2014-11-20

CAPE CANAVERAL, Fla. – Workers monitor NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic and secured onto a portable work stand, as it travels between the airlock of Building 2 to the high bay of Building 1 at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

Maximum Entropy Principle for Transportation

NASA Astrophysics Data System (ADS)

Bilich, F.; DaSilva, R.

2008-11-01

In this work we deal with modeling of the transportation phenomenon for use in the transportation planning process and policy-impact studies. The model developed is based on the dependence concept, i.e., the notion that the probability of a trip starting at origin i is dependent on the probability of a trip ending at destination j given that the factors (such as travel time, cost, etc.) which affect travel between origin i and destination j assume some specific values. The derivation of the solution of the model employs the maximum entropy principle combining a priori multinomial distribution with a trip utility concept. This model is utilized to forecast trip distributions under a variety of policy changes and scenarios. The dependence coefficients are obtained from a regression equation where the functional form is derived based on conditional probability and perception of factors from experimental psychology. The dependence coefficients encode all the information that was previously encoded in the form of constraints. In addition, the dependence coefficients encode information that cannot be expressed in the form of constraints for practical reasons, namely, computational tractability. The equivalence between the standard formulation (i.e., objective function with constraints) and the dependence formulation (i.e., without constraints) is demonstrated. The parameters of the dependence-based trip-distribution model are estimated, and the model is also validated using commercial air travel data in the U.S. In addition, policy impact analyses (such as allowance of supersonic flights inside the U.S. and user surcharge at noise-impacted airports) on air travel are performed.

Emerging Climate-data Needs in the Air Transport Sector

NASA Astrophysics Data System (ADS)

Thompson, T. R.

2014-12-01

This paper addresses the nature of climate information needed within the air-transport sector. Air transport is not a single economic sector with uniform needs for climate data: airport, airline, and air-navigation services are the principal sub-sectors, each with their own particular climate-related decision contexts. For example, airports function as fixed infrastructure that is primarily affected by probabilities of extreme events that could hamper runway/taxiway operations, interfere with worker availability, or impede travel to and from the airport by passengers. Airlines, in contrast, are more concerned with changes in atmospheric conditions (upper-air turbulence, convective weather events, etc.) that might require consideration in long-term decisions related to flight-planning processes and aircraft equipage. Air-navigation service providers have needs that are primarily concerned with assurance of safe spatial separation of aircraft via sensor data and communications links. In addition to present-day commercial air transport, we discuss what climate data may be needed for new types of air transport that may emerge in the next couple of decades. These include, for example, small aircraft provided on-demand to non-pilot travelers, high-altitude supersonic business and commercial jets, and very large numbers of un-manned aircraft. Finally, we give examples relating to key technical challenges in providing decision-relevant climate data to the air-transport sector. These include: (1) identifying what types of climate data are most relevant the different decisions facing the several segments of this industry; (2) determining decision-appropriate time horizons for forecasts of this data; and (3) coupling the uncertainties inherent in these forecasts to the decision process.

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

PubMed Central

Tercek, Michael; Rodman, Ann

2016-01-01

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

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

ERIC Educational Resources Information Center

Clode, Linda

1992-01-01

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

Severe Weather Field Experience: An Undergraduate Field Course on Career Enhancement and Severe Convective Storms

ERIC Educational Resources Information Center

Godfrey, Christopher M.; Barrett, Bradford S.; Godfrey, Elaine S.

2011-01-01

Undergraduate students acquire a deeper understanding of scientific principles through first-hand experience. To enhance the learning environment for atmospheric science majors, the University of North Carolina at Asheville has developed the severe weather field experience. Participants travel to Tornado Alley in the Great Plains to forecast and…

Evaluation of the operational and demonstration test of short-range weather forecasting decision support within an advanced rural traveler information system

DOT National Transportation Integrated Search

1996-11-01

THIS IS THE TECHNICAL SUMMARY OF THE RESEARCH REPORT, COMMERCIAL MOTOR VEHICLE DRIVER FATIOUE AND ALERTNESS STUDY BY WYLIE ET AL., THE LARGEST AND MOST COMPREHENSIVE OVER-THE-ROAD STUDY ON THIS SUBJECT EVER CONDUCTED IN NORTH AMERICA. THE DATA COLLEC...

Tomorrow's Forecast: Oceans and Weather.

ERIC Educational Resources Information Center

Smigielski, Alan

1995-01-01

This issue of "Art to Zoo" focuses on weather and climate and is tied to the traveling exhibition Ocean Planet from the Smithsonian's National Museum of Natural History. The lessons encourage students to think about the profound influence the oceans have on planetary climate and life on earth. Sections of the lesson plan include: (1)…

78 FR 28012 - Tier One Environmental Impact Statement for the Rochester, Minnesota to Twin Cities, Minnesota...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-13

... detail. Project Background and Study Area: Based upon travel demand and growth between the two regional... corridors in the region had been established. The number of jobs currently supported by Rochester employers... transportation alternative that will meet forecasted population and economic growth mobility demands in the...

Landslide modeling and forecasting—recent progress by the u.s. geological survey

USGS Publications Warehouse

Baum, Rex L.; Kean, Jason W.

2015-01-01

Landslide studies by the U.S. Geological Survey (USGS) are focused on two main objectives: scientific understanding and forecasting. The first objective is to gain better understanding of the physical processes involved in landslide initiation and movement. This objective is largely in support of the second objective, to develop predictive capabilities to answer the main hazard questions. Answers to the following six questions are needed to characterize the hazard from landslides: (1) Where will landslides occur? (2) What kind(s) of landslides will occur? (3) When will landslides occur? (4) How big will the landslides be? (5) How fast will the landslides travel? (6) How far will the landslides go? Although these questions are sometimes recast in different terms, such as frequency or recurrence rather than timing (when), the questions or their variants address the spatial, physical, and temporal aspects of landslide hazards. Efforts to develop modeling and forecasting capabilities by the USGS are primarily focused on specific landslide types that pose a high degree of hazard and show relatively high potential for predictability.

Examining Hurricane Track Length and Stage Duration Since 1980

NASA Astrophysics Data System (ADS)

Fandrich, K. M.; Pennington, D.

2017-12-01

Each year, tropical systems impact thousands of people worldwide. Current research shows a correlation between the intensity and frequency of hurricanes and the changing climate. However, little is known about other prominent hurricane features. This includes information about hurricane track length (the total distance traveled from tropical depression through a hurricane's final category assignment) and how this distance may have changed with time. Also unknown is the typical duration of a hurricane stage, such as tropical storm to category one, and if the time spent in each stage has changed in recent decades. This research aims to examine changes in hurricane stage duration and track lengths for the 319 storms in NOAA's National Ocean Service Hurricane Reanalysis dataset that reached Category 2 - 5 from 1980 - 2015. Based on evident ocean warming, it is hypothesized that a general increase in track length with time will be detected, thus modern hurricanes are traveling a longer distance than past hurricanes. It is also expected that stage durations are decreasing with time so that hurricanes mature faster than in past decades. For each storm, coordinates are acquired at 4-times daily intervals throughout its duration and track lengths are computed for each 6-hour period. Total track lengths are then computed and storms are analyzed graphically and statistically by category for temporal track length changes. The stage durations of each storm are calculated as the time difference between two consecutive stages. Results indicate that average track lengths for Cat 2 and 3 hurricanes are increasing through time. These findings show that these hurricanes are traveling a longer distance than earlier Cat 2 and 3 hurricanes. In contrast, average track lengths for Cat 4 and 5 hurricanes are decreasing through time, showing less distance traveled than earlier decades. Stage durations for all Cat 2, 4 and 5 storms decrease through the decades but Cat 3 storms show a positive increase though time. This compliments the results of the track length analysis indicating that as storms intensify faster, they are doing so over a shorter distance. It is expected that this research could be used to improve hurricane track forecasting and provide information about the effects of climate change on tropical systems and the tropical environment.

REAL-TIME high-resolution urban surface water flood mapping to support flood emergency management

NASA Astrophysics Data System (ADS)

Guan, M.; Yu, D.; Wilby, R.

2016-12-01

Strong evidence has shown that urban flood risks will substantially increase because of urbanisation, economic growth, and more frequent weather extremes. To effectively manage these risks require not only traditional grey engineering solutions, but also a green management solution. Surface water flood risk maps based on return period are useful for planning purposes, but are limited for application in flood emergencies, because of the spatiotemporal heterogeneity of rainfall and complex urban topography. Therefore, a REAL-TIME urban surface water mapping system is highly beneficial to increasing urban resilience to surface water flooding. This study integrated numerical weather forecast and high-resolution urban surface water modelling into a real-time multi-level surface water mapping system for Leicester City in the UK. For rainfall forecast, the 1km composite rain radar from the Met Office was used, and we used the advanced rainfall-runoff model - FloodMap to predict urban surface water at both city-level (10m-20m) and street-level (2m-5m). The system is capable of projecting 3-hour urban surface water flood, driven by rainfall derived from UK Met Office radar. Moreover, this system includes real-time accessibility mapping to assist the decision-making of emergency responders. This will allow accessibility (e.g. time to travel) from individual emergency service stations (e.g. Fire & Rescue; Ambulance) to vulnerable places to be evaluated. The mapping results will support contingency planning by emergency responders ahead of potential flood events.

Snow mass and river flows modelled using GRACE total water storage observations

NASA Astrophysics Data System (ADS)

Wang, S.

2017-12-01

Snow mass and river flow measurements are difficult and less accurate in cold regions due to the hash environment. Floods in cold regions are commonly a result of snowmelt during the spring break-up. Flooding is projected to increase with climate change in many parts of the world. Forecasting floods from snowmelt remains a challenge due to scarce and quality issues in basin-scale snow observations and lack of knowledge for cold region hydrological processes. This study developed a model for estimating basin-level snow mass (snow water equivalent SWE) and river flows using the total water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The SWE estimation is based on mass balance approach which is independent of in situ snow gauge observations, thus largely eliminates the limitations and uncertainties with traditional in situ or remote sensing snow estimates. The model forecasts river flows by simulating surface runoff from snowmelt and the corresponding baseflow from groundwater discharge. Snowmelt is predicted using a temperature index model. Baseflow is predicted using a modified linear reservoir model. The model also quantifies the hysteresis between the snowmelt and the streamflow rates, or the lump time for water travel in the basin. The model was applied to the Red River Basin, the Mackenzie River Basin, and the Hudson Bay Lowland Basins in Canada. The predicted river flows were compared with the observed values at downstream hydrometric stations. The results were also compared to that for the Lower Fraser River obtained in a separate study to help better understand the roles of environmental factors in determining flood and their variations with different hydroclimatic conditions. This study advances the applications of space-based time-variable gravity measurements in cold region snow mass estimation, river flow and flood forecasting. It demonstrates a relatively simple method that only needs GRACE TWS and temperature data for river flow or flood forecasting. The model can be particularly useful for regions with spare observation networks, and can be used in combination with other available methods to help improve the accuracy in river flow and flood forecasting over cold regions.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

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

Garikapati, Venu M.; You, Daehyun; Zhang, Wenwen

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is foundmore » to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.« less

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.

Effects of new motorway infrastructure on active travel in the local population: a retrospective repeat cross-sectional study in Glasgow, Scotland.

PubMed

Olsen, Jonathan R; Mitchell, Richard; Ogilvie, David

2016-07-07

Promoting active travel is an important part of increasing population physical activity, which has both physical and mental health benefits. A key benefit described by the then Scottish Government of the five-mile M74 motorway extension, which opened during June 2011 in the south of Glasgow, was that the forecast reduction in motor traffic on local streets would make these streets safer for walking and cycling, thus increasing active travel by the local population. The aim of the study was to evaluate the impact of new motorway infrastructure on the proportion of journey stages made actively (cycling or on foot) by individuals travelling in and out of the local area. Data for the periods 2009-10 and 2012-13 were extracted from the Scottish Household Survey (SHS) travel diaries, which record each journey stage made during the previous day by a representative sample of the Scottish population aged 16 and over. Each individual journey stage was assigned to one of the following study areas surrounding existing and new transport infrastructure: (1) an area surrounding the new M74 motorway extension (n = 435 (2009-10), 543 (2012-13)), (2) a comparator area surrounding an existing motorway (n = 477 (2009-10), 560 (2012-13)), and (3) a control area containing no comparable motorway infrastructure (n = 541 (2009-10), 593 (2012-13)). Multivariable, multi-level regression analysis was performed to determine any between-area differences in change in active travel over time, which might indicate an intervention effect. Reference populations were defined using two alternative definitions, (1) Glasgow City and (2) Glasgow and surrounding local authorities. The results showed an increase in the proportion of journey stages using active travel in all study areas compared to both reference populations. However, there were no significant between-area differences to suggest an effect attributable the M74 motorway extension. There was no clear evidence that the M74 motorway extension either increased or decreased active travel in the local area. The anticipation by policy makers that reduced motorised traffic on local streets might increase journeys walked or cycled appears to have been unfounded.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

Travel Demand Modeling

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

Southworth, Frank; Garrow, Dr. Laurie

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming,more » and agent-based microsimulation.« less

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).

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

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.

Maximum entropy principal for transportation

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

Bilich, F.; Da Silva, R.

In this work we deal with modeling of the transportation phenomenon for use in the transportation planning process and policy-impact studies. The model developed is based on the dependence concept, i.e., the notion that the probability of a trip starting at origin i is dependent on the probability of a trip ending at destination j given that the factors (such as travel time, cost, etc.) which affect travel between origin i and destination j assume some specific values. The derivation of the solution of the model employs the maximum entropy principle combining a priori multinomial distribution with a trip utilitymore » concept. This model is utilized to forecast trip distributions under a variety of policy changes and scenarios. The dependence coefficients are obtained from a regression equation where the functional form is derived based on conditional probability and perception of factors from experimental psychology. The dependence coefficients encode all the information that was previously encoded in the form of constraints. In addition, the dependence coefficients encode information that cannot be expressed in the form of constraints for practical reasons, namely, computational tractability. The equivalence between the standard formulation (i.e., objective function with constraints) and the dependence formulation (i.e., without constraints) is demonstrated. The parameters of the dependence-based trip-distribution model are estimated, and the model is also validated using commercial air travel data in the U.S. In addition, policy impact analyses (such as allowance of supersonic flights inside the U.S. and user surcharge at noise-impacted airports) on air travel are performed.« less

The US aviation system to the year 2000

NASA Technical Reports Server (NTRS)

Austrotas, R. A.

1982-01-01

The aviation system of the U.S. is described. Growth of the system over the past twenty years is analyzed. Long term and short term causes of air travel are discussed. The interaction of economic growth, airline yields, and quality of service in producing domestic traffic is shown. Forecasts are made for airline and general aviation growth. Potential airline scenarios are presented.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Collaborative Aviation Weather Statement - An Impact-based Decision Support Tool

NASA Astrophysics Data System (ADS)

Blondin, Debra

2016-04-01

Historically, convection causes the highest number of air traffic constraints on the United States National Air Space (NAS). Increased NAS predictability allows traffic flow managers to more effectively initiate, amend or terminate planned or active traffic management initiatives, resulting in more efficient use of available airspace. A Collaborative Aviation Weather Statement (CAWS) is an impact-based decision support tool used for the timely delivery of high-confidence, high-relevance aviation convective weather forecasts to air traffic managers. The CAWS is a graphical and textual forecast produced by a collaborative team of meteorologists from the Aviation Weather Center (AWC), Center Weather Service Units, and airlines to bring attention to high impact areas of thunderstorms. The CAWS addresses thunderstorm initiation or movement into the airports having the highest volume of traffic or into traffic sensitive jet routes. These statements are assessed by planners at the Federal Aviation Administration's (FAA) Air Route Traffic Control Centers and are used for planning traffic management initiatives to balance air traffic flow across the United States. The FAA and the airline industry use the CAWS to plan, manage, and execute operations in the NAS, thereby improving the system efficiency and safety and also saving dollars for industry and the traveling public.

A combined road weather forecast system to prevent road ice formation in the Adige Valley (Italy)

NASA Astrophysics Data System (ADS)

Di Napoli, Claudia; Piazza, Andrea; Antonacci, Gianluca; Todeschini, Ilaria; Apolloni, Roberto; Pretto, Ilaria

2016-04-01

Road ice is a dangerous meteorological hazard to a nation's transportation system and economy. By reducing the pavement friction with vehicle tyres, ice formation on pavements increases accident risk and delays travelling times thus posing a serious threat to road users' safety and the running of economic activities. Keeping roads clear and open is therefore essential, especially in mountainous areas where ice is likely to form during the winter period. Winter road maintenance helps to restore road efficiency and security, and its benefits are up to 8 times the costs sustained for anti-icing strategies [1]. However, the optimization of maintenance costs and the reduction of the environmental damage from over-salting demand further improvements. These can be achieved by reliable road weather forecasts, and in particular by the prediction of road surface temperatures (RSTs). RST is one of the most important parameters in determining road surface conditions. It is well known from literature that ice forms on pavements in high-humidity conditions when RSTs are below 0°C. We have therefore implemented an automatic forecast system to predict critical RSTs on a test route along the Adige Valley complex terrain, in the Italian Alps. The system considers two physical models, each computing heat and energy fluxes between the road and the atmosphere. One is Reuter's radiative cooling model, which predicts RSTs at sunrise as a function of surface temperatures at sunset and the time passed since then [2]. One is METRo (Model of the Environment and Temperature of Roads), a road weather forecast software which also considers heat conduction through road material [3]. We have applied the forecast system to a network of road weather stations (road weather information system, RWIS) installed on the test route [4]. Road and atmospheric observations from RWIS have been used as initial conditions for both METRo and Reuter's model. In METRo observations have also been coupled to meteorological forecasts from ECMWF numerical prediction model. Overnight RST minima have then been estimated automatically in nowcast mode. In this presentation we show and discuss results and performances for the 2014-2015 and 2015-2016 winter seasons. Using evaluation indexes we demonstrate that combining METRo and Reuter's models into one single forecast system improves bias and accuracy by about 0.5°C. This study is supported by the LIFE11 ENV/IT/000002 CLEAN-ROADS project. The project aims to assess the environmental impact of salt de-icers in Trentino mountain region by supporting winter road management operations with meteorological information. [1] Thornes J.E. and Stephenson D.B., Meteorological Applications, 8:307 (2001) [2] Reuter H., Tellus, 3:141 (1951) [3] Crevier L.P. and Delage Y., Journal of applied meteorology, 40:2026 (2001) [4] Pretto I. et al., SIRWEC 2014 conference proceedings, ID:0019 (2014)

The quasi 2 day wave response in TIME-GCM nudged with NOGAPS-ALPHA

NASA Astrophysics Data System (ADS)

Wang, Jack C.; Chang, Loren C.; Yue, Jia; Wang, Wenbin; Siskind, D. E.

2017-05-01

The quasi 2 day wave (QTDW) is a traveling planetary wave that can be enhanced rapidly to large amplitudes in the mesosphere and lower thermosphere (MLT) region during the northern winter postsolstice period. In this study, we present five case studies of QTDW events during January and February 2005, 2006 and 2008-2010 by using the Thermosphere-Ionosphere-Mesosphere Electrodynamics-General Circulation Model (TIME-GCM) nudged with the Navy Operational Global Atmospheric Prediction System-Advanced Level Physics High Altitude (NOGAPS-ALPHA) Weather Forecast Model. With NOGAPS-ALPHA introducing more realistic lower atmospheric forcing in TIME-GCM, the QTDW events have successfully been reproduced in the TIME-GCM. The nudged TIME-GCM simulations show good agreement in zonal mean state with the NOGAPS-ALPHA 6 h reanalysis data and the horizontal wind model below the mesopause; however, it has large discrepancies in the tropics above the mesopause. The zonal mean zonal wind in the mesosphere has sharp vertical gradients in the nudged TIME-GCM. The results suggest that the parameterized gravity wave forcing may need to be retuned in the assimilative TIME-GCM.

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

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

2017-04-01

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

Anomalous diffusion and the structure of human transportation networks

NASA Astrophysics Data System (ADS)

Brockmann, D.

2008-04-01

The dispersal of individuals of a species is the key driving force of various spatiotemporal phenomena which occur on geographical scales. It can synchronise populations of interacting species, stabilise them, and diversify gene pools [1-3]. The geographic spread of human infectious diseases such as influenza, measles and the recent severe acute respiratory syndrome (SARS) is essentially promoted by human travel which occurs on many length scales and is sustained by a variety of means of transportation [4-8]. In the light of increasing international trade, intensified human traffic, and an imminent influenza A pandemic the knowledge of dynamical and statistical properties of human dispersal is of fundamental importance and acute [7,9,10]. A quantitative statistical theory for human travel and concomitant reliable forecasts would substantially improve and extend existing prevention strategies. Despite its crucial role, a quantitative assessment of human dispersal remains elusive and the opinion that humans disperse diffusively still prevails in many models [11]. In this chapter I will report on a recently developed technique which permits a solid and quantitative assessment of human dispersal on geographical scales [11]. The key idea is to infer the statistical properties of human travel by analysing the geographic circulation of individual bank notes for which comprehensive datasets are collected at the online bill-tracking website www.wheresgeorge.com. The analysis shows that the distribution of travelling distances decays as a power law, indicating that the movement of bank notes is reminiscent of superdiffusive, scale free random walks known as Lèvy flights [13]. Secondly, the probability of remaining in a small, spatially confined region for a time T is dominated by heavy tails which attenuate superdiffusive dispersal. I will show that the dispersal of bank notes can be described on many spatiotemporal scales by a two parameter continuous time random walk (CTRW) model to a surprising accuracy. To this end, I will provide a brief introduction to continuous time random walk theory [14] and will show that human dispersal is an ambivalent, effectively superdiffusive process.

Optimization of the Costs and the Safety of Maritime Transport by Routing: The use of Currents Forecast in the Routing of Racing Sail Boats as a Prototype of Rout Optimization for Trading Ships

NASA Astrophysics Data System (ADS)

Theunynck, Denis; Peze, Thierry; Toumazou, Vincent; Zunquin, Gauthier; Cohen, Olivier; Monges, Arnaud

2005-03-01

It is interesting to see whether the model of routing designed for races and great Navy operations could be transferred to commercial navigation and if so, within which framework.Sail boat routing conquered its letters of nobility during great races like the « Route du Rhum » or the transatlantic race « Jacques Vabre ». It is the ultimate stage of the step begun by the Navy at the time of great operations, like D-day (Overlord )June 6, 1944, in Normandy1.Routing is, from the beginning, mainly based on statistical knowledge and weather forecast, but with the recent availability of reliable currents forecast, sail boats routers and/or skippers now have to learn how to use both winds and currents to obtain the best performance, that is to travel between two points in the shortest time possible in acceptable security conditions.Are the currents forecast only useful to racing sail boat ? Of course not, they are a great help to fisherman for whom the knowledge of currents is also the knowledge of sea temperature who indicates the probability of fish presence. They are also used in offshore work to predict the hardness of the sea during operation.A less developed field of application is the route optimization of trading ships. The idea is to optimize the use of currents to increase the relative speed of ships with no augmentation of fuel expense. This new field will require that currents forecasters learn about the specific needs of another type of clients. There is also a need of teaching because the future customers will have to learn how to use the information they will get.At this point, the introduction of the use of currents forecast in racing sail boats routing is only the first step. It is of great interest because it can rely on a high knowledge in routing.The main difference is of course that the wind direction and its force are of greater importance to a sail boat that they are for a trading ship for whom the point of interest will be the fuel consumption and the ETA respect.Despite that, sail boat routing could be use as a prototype to determine the needs, both in term of information and formations of ship routers and skippers2.

Fast Kalman Filter for Random Walk Forecast model

NASA Astrophysics Data System (ADS)

Saibaba, A.; Kitanidis, P. K.

2013-12-01

Kalman filtering is a fundamental tool in statistical time series analysis to understand the dynamics of large systems for which limited, noisy observations are available. However, standard implementations of the Kalman filter are prohibitive because they require O(N^2) in memory and O(N^3) in computational cost, where N is the dimension of the state variable. In this work, we focus our attention on the Random walk forecast model which assumes the state transition matrix to be the identity matrix. This model is frequently adopted when the data is acquired at a timescale that is faster than the dynamics of the state variables and there is considerable uncertainty as to the physics governing the state evolution. We derive an efficient representation for the a priori and a posteriori estimate covariance matrices as a weighted sum of two contributions - the process noise covariance matrix and a low rank term which contains eigenvectors from a generalized eigenvalue problem, which combines information from the noise covariance matrix and the data. We describe an efficient algorithm to update the weights of the above terms and the computation of eigenmodes of the generalized eigenvalue problem (GEP). The resulting algorithm for the Kalman filter with Random walk forecast model scales as O(N) or O(N log N), both in memory and computational cost. This opens up the possibility of real-time adaptive experimental design and optimal control in systems of much larger dimension than was previously feasible. For a small number of measurements (~ 300 - 400), this procedure can be made numerically exact. However, as the number of measurements increase, for several choices of measurement operators and noise covariance matrices, the spectrum of the (GEP) decays rapidly and we are justified in only retaining the dominant eigenmodes. We discuss tradeoffs between accuracy and computational cost. The resulting algorithms are applied to an example application from ray-based travel time tomography.

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

User Observed Estimates of Cloud Fraction for Modifying a Cloud-free UV Index for Use in an Educational Smart-phone Application on Erythema

NASA Astrophysics Data System (ADS)

Lantz, K. O.; Long, C. S.; Buller, D.; Berwick, M.; Buller, M.; Kane, I.; Shane, J.

2012-12-01

The UV Index (UVI) is a measure of the skin-damaging UV radiation levels at the Earth's surface. Clouds, haze, air pollution, total ozone, surface elevation, and ground reflectivity affect the levels of UV radiation reaching the ground. The global UV Index was developed as a simple tool to educate the public for taking precautions when exposed to UV radiation to avoid sun-burning, which has been linked to the development of skin cancer. The purpose of this study was to validate an algorithm to modify a cloud-free UV Index forecast for cloud conditions as observed by adults in real-time. The cloud attenuation algorithm is used in a smart-phone application to modify a clear-sky UV Index forecast. In the United States, the Climate Prediction Center of the National Oceanic and Atmospheric Administration's (NOAA) issues a daily UV Index Forecast. The NOAA UV Index is an hourly forecast for a 0.5 x 0.5 degree area and thus has a degree of uncertainty. Cloud cover varies temporally and spatially over short times and distances as weather conditions change and can have a large impact on the UV radiation. The smart-phone application uses the cloud-based UV Index forecast as the default but allows the user to modify a cloud-free UV Index forecast when the predicted sky conditions do not match observed conditions. Eighty four (n=84) adults were recruited to participate in the study through advertisements posted online and in a university e-newsletter. Adults were screened for eligibility (i.e., 18 or older, capable to traveling to test site, had a smart phone with a data plan to access online observation form). A sky observation measure was created to assess cloud fraction. The adult volunteers selected from among four photographs the image that best matched the cloud conditions they observed. Images depicted no clouds (clear sky), thin high clouds, partly cloudy sky, and thick clouds (sky completely overcast). When thin high clouds or partly cloudy images were selected, adults estimated the percentage of the sky covered by clouds. Cloud fraction was calculated by assigning 0% if the clear-sky image was selected, 100% if the overcast thick cloud image was selected, and 10% to 90% as indicated by adults, if high thin clouds or partly cloudy images were selected. The observed cloud fraction from the adult volunteers was compared to the cloud fraction determined by a Total Sky Imager. A cloud modification factor based on the observed cloud fraction was applied to the cloud-free UV Index forecast. This result was compared to the NOAA cloudy sky UV Index forecast and to the concurrent UV Index measurements from three broadband UV radiometers and a Brewer spectrophotometer calibrated using NIST traceable standards.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Path planning in uncertain flow fields using ensemble method

NASA Astrophysics Data System (ADS)

Wang, Tong; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.

2016-10-01

An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

Travel time and concurrent-schedule choice: retrospective versus prospective control.

PubMed

Davison, M; Elliffe, D

2000-01-01

Six pigeons were trained on concurrent variable-interval schedules in which two different travel times between alternatives, 4.5 and 0.5 s, were randomly arranged. In Part 1, the next travel time was signaled while the subjects were responding on each alternative. Generalized matching analyses of performance in the presence of the two travel-time signals showed significantly higher response and time sensitivity when the longer travel time was signaled compared to when the shorter time was signaled. When the data were analyzed as a function of the previous travel time, there were no differences in sensitivity. Dwell times on the alternatives were consistently longer in the presence of the stimulus that signaled the longer travel time than they were in the presence of the stimulus that signaled the shorter travel time. These results are in accord with a recent quantitative account of the effects of travel time. In Part 2, no signals indicating the next travel time were given. When these data were analyzed as a function of the previous travel time, time-allocation sensitivity after the 4.5-s travel time was significantly greater than that after the 0.5-s travel time, but no such difference was found for response allocation. Dwell times were also longer when the previous travel time had been longer.

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

NASA Astrophysics Data System (ADS)

Liu, P.

2013-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China

NASA Astrophysics Data System (ADS)

Zhao, Tongtiegang; Liu, Pan; Zhang, Yongyong; Ruan, Chengqing

2017-09-01

Global climate model (GCM) forecasts are an integral part of long-range hydroclimatic forecasting. We propose to use clustering to explore anomaly correlation, which indicates the performance of raw GCM forecasts, in the three-dimensional space of latitude, longitude, and initialization time. Focusing on a certain period of the year, correlations for forecasts initialized at different preceding periods form a vector. The vectors of anomaly correlation across different GCM grid cells are clustered to reveal how GCM forecasts perform as time progresses. Through the case study of Climate Forecast System Version 2 (CFSv2) forecasts of summer precipitation in China, we observe that the correlation at a certain cell oscillates with lead time and can become negative. The use of clustering reveals two meaningful patterns that characterize the relationship between anomaly correlation and lead time. For some grid cells in Central and Southwest China, CFSv2 forecasts exhibit positive correlations with observations and they tend to improve as time progresses. This result suggests that CFSv2 forecasts tend to capture the summer precipitation induced by the East Asian monsoon and the South Asian monsoon. It also indicates that CFSv2 forecasts can potentially be applied to improving hydrological forecasts in these regions. For some other cells, the correlations are generally close to zero at different lead times. This outcome implies that CFSv2 forecasts still have plenty of room for further improvement. The robustness of the patterns has been tested using both hierarchical clustering and k-means clustering and examined with the Silhouette score.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

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

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA

USGS Publications Warehouse

Reid, Mark E.; Coe, Jeffrey A.; Brien, Dianne

2016-01-01

Many debris flows increase in volume as they travel downstream, enhancing their mobility and hazard. Volumetric growth can result from diverse physical processes, such as channel sediment entrainment, stream bank collapse, adjacent landsliding, hillslope erosion and rilling, and coalescence of multiple debris flows; incorporating these varied phenomena into physics-based debris-flow models is challenging. As an alternative, we embedded effects of debris-flow growth into an empirical/statistical approach to forecast potential inundation areas within digital landscapes in a GIS framework. Our approach used an empirical debris-growth function to account for the effects of growth phenomena. We applied this methodology to a debris-flow-prone area in the Oregon Coast Range, USA, where detailed mapping revealed areas of erosion and deposition along paths of debris flows that occurred during a large storm in 1996. Erosion was predominant in stream channels with slopes > 5°. Using pre- and post-event aerial photography, we derived upslope contributing area and channel-length growth factors. Our method reproduced the observed inundation patterns produced by individual debris flows; it also generated reproducible, objective potential inundation maps for entire drainage networks. These maps better matched observations than those using previous methods that focus on proximal or distal regions of a drainage network.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.

PubMed

Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey

2017-11-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2017-01-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. PMID:29107987

The time machine in our mind.

PubMed

Stocker, Kurt

2012-04-01

This article provides the first comprehensive conceptual account for the imagistic mental machinery that allows us to travel through time--for the time machine in our mind. It is argued that language reveals this imagistic machine and how we use it. Findings from a range of cognitive fields are theoretically unified and a recent proposal about spatialized mental time travel is elaborated on. The following novel distinctions are offered: external versus internal viewing of time; ''watching" time versus projective ''travel" through time; optional versus obligatory mental time travel; mental time travel into anteriority or posteriority versus mental time travel into the past or future; single mental time travel versus nested dual mental time travel; mental time travel in episodic memory versus mental time travel in semantic memory; and ''seeing" versus ''sensing" mental imagery. Theoretical, empirical, and applied implications are discussed. Copyright © 2012 Cognitive Science Society, Inc.

Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

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

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.

Using Time-Series Regression to Predict Academic Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

PubMed Central

Lam, William H. K.; Li, Qingquan

2017-01-01

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. PMID:29210978

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

PubMed

Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

2017-12-06

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

5 CFR 550.1404 - Creditable travel time.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 5 Administrative Personnel 1 2012-01-01 2012-01-01 false Creditable travel time. 550.1404 Section... ADMINISTRATION (GENERAL) Compensatory Time Off for Travel § 550.1404 Creditable travel time. (a) General. Subject... off for time in a travel status if— (1) The employee is required to travel away from the official duty...

5 CFR 550.1404 - Creditable travel time.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 5 Administrative Personnel 1 2014-01-01 2014-01-01 false Creditable travel time. 550.1404 Section... ADMINISTRATION (GENERAL) Compensatory Time Off for Travel § 550.1404 Creditable travel time. (a) General. Subject... off for time in a travel status if— (1) The employee is required to travel away from the official duty...

5 CFR 550.1404 - Creditable travel time.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 5 Administrative Personnel 1 2013-01-01 2013-01-01 false Creditable travel time. 550.1404 Section... ADMINISTRATION (GENERAL) Compensatory Time Off for Travel § 550.1404 Creditable travel time. (a) General. Subject... off for time in a travel status if— (1) The employee is required to travel away from the official duty...

5 CFR 550.1404 - Creditable travel time.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 5 Administrative Personnel 1 2011-01-01 2011-01-01 false Creditable travel time. 550.1404 Section... ADMINISTRATION (GENERAL) Compensatory Time Off for Travel § 550.1404 Creditable travel time. (a) General. Subject... off for time in a travel status if— (1) The employee is required to travel away from the official duty...

5 CFR 550.1404 - Creditable travel time.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Creditable travel time. 550.1404 Section... ADMINISTRATION (GENERAL) Compensatory Time Off for Travel § 550.1404 Creditable travel time. (a) General. Subject... off for time in a travel status if— (1) The employee is required to travel away from the official duty...

Near-Real Time Monitoring of TEC Over Japan at NICT (RWC Tokyo OF ISES)

NASA Astrophysics Data System (ADS)

Miyake, W.; Jin, H.

2010-05-01

The world wide use of global navigation satellite systems such as GPS offers unique opportunities for a permanent monitoring of the total electron content (TEC) of the ionosphere. We have developed a system of the rapid derivation of TEC from GEONET (a dense GPS receiver network in Japan). In addition to a previous plot of TEC temporal variation over Japan, we have recently developed a near-real-time two-dimensional TEC map and have used it for the daily operation of Space Weather Forecast Center at NICT (Regional Warning Center Tokyo of International Space Environment Service). The TEC map can be used to continuously monitor the ionospheric disturbances over Japan, including spatial and temporal development of ionospheric storms, large-amplitude traveling ionospheric disturbances, and plasma bubbles intruding over Japan, with high time resolution. The development of the real-time monitoring system of TEC enables us to monitor large ionospheric disturbances, ranging from global- to small-scale disturbances, expected in the next solar maximum. The plot and maps are open to the public and are available on http://wdc.nict.go.jp/IONO/index_E.html.

New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreido, Vsevolod

2017-04-01

Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.

Some Aspects of Forecasting Severe Thunderstorms during Cool-Season Return-Flow Episodes.

NASA Astrophysics Data System (ADS)

Weiss, Steven J.

1992-08-01

Historically, the Gulf of Mexico has been considered a primary source of water vapor that influences the weather for much of the United States east of the Rocky Mountains. Although severe thunderstorms and tornadoes occur most frequently during the spring and summer months, the periodic transport of Gulf moisture inland ahead of traveling baroclinic waves can result in significant severe-weather episodes during the cool season.To gain insight into the short-range skill in forecasting surface synoptic patterns associated with moisture return from the Gulf, operational numerical weather prediction models from the National Meteorological Center were examined. Sea level pressure fields from the Limited-Area Fine-Mesh Model (LFM), Nested Grid Model (NGM), and the aviation (AVN) run of the Global Spectral Model, valid 48 h after initial data time, were evaluated for three cool-season cases that preceded severe local storm outbreaks. The NGM and AVN provided useful guidance in forecasting the onset of return flow along the Gulf coast. There was a slight tendency for these models to be slightly slow in the development of return flow. In contrast the LFM typically overforecasts the occurrence of return flow and tends to `open the Gulf' from west to east too quickly.Although the low-level synoptic pattern may be forecast correctly, the overall prediction process is hampered by a data void over the Gulf. It is hypothesized that when the return-flow moisture is located over the Gulf, model forecasts of stability and the resultant operational severe local storm forecasts are less skillful compared to situations when the moisture has spread inland already. This hypothesis is tested by examining the performance of the initial second-day (day 2) severe thunderstorm outlook issued by the National Severe Storms Forecast Center during the Gulf of Mexico Experiment (GUFMEX) in early 1988.It has been found that characteristically different air masses were present along the Gulf coast prior to the issuance of outlooks that accurately predicted the occurrence of severe thunderstorms versus outlooks that did not verify well. Unstable air masses with ample low-level moisture were in place along the coast prior to the issuance of the `good' day 2 outlooks, whereas relatively dry, stable air masses were present before the issuance of `false-alarm' outlooks. In the latter cases, large errors in the NGM 48-h lifted-index predictions were located north of the Gulf coast.

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

2017-03-01

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Solar Energetic Particles -- A Radiation Hazard to Humans and Hardware in Space

NASA Astrophysics Data System (ADS)

Mewaldt, R. A.

2006-10-01

During large solar energetic particle (SEP) events the intensity of >30 MeV protons in nearby interplanetary space can increase by a million times over the steady intensity of galactic cosmic rays, creating a radiation hazard to both humans and hardware in space. With NASA now committed to sending astronauts to the Moon and possibly on to Mars, outside the protective cover of the Earth's magnetosphere, interest in understanding and forecasting large SEP events has taken on a new sense of urgency. The past solar maximum included four of the top ten SEP events of the space era. Fortunately, the array of spacecraft now in interplanetary space has provided greatly improved measurements of the composition and energy spectra of accelerated ions, leading to fresh insights into the nature of these events. The largest SEP events are accelerated by coronal and interplanetary shocks driven by coronal mass ejections (CMEs) traveling at >2000 km/sec. Although shock acceleration is ubiquitous in nature, its efficiency is highly variable, making it difficult to forecast the onset and evolution of large SEP events. This talk will describe the radiation hazards associated with the largest SEP events, discuss their frequency of occurrence, consider a worst-case SEP event, and describe how the radiation risks can be mitigated.

Using temporal detrending to observe the spatial correlation of traffic.

PubMed

Ermagun, Alireza; Chatterjee, Snigdhansu; Levinson, David

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis-St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

Using temporal detrending to observe the spatial correlation of traffic

PubMed Central

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models. PMID:28472093

Analysis of user cost and service trade-offs in transit and paratransit services

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

Louviere, J.; Kocur, G.

1979-08-01

The Xenia Model Transit Service served as a test of several alternative transit services operated in a small city setting. Research was designed to test a new method for assessing user tradeoffs in costs and service based on attitudinal methods. Termed direct response assessment, the methods were developed in psychology and have been extended to application in utility assessment. A tradeoff survey was administered as part of a home interview survey. Data from the tradeoff survey were used to develop separate equations for each sample respondent to explain and describe their tradeoffs over transit fare, travel time, walk distance, typemore » of service, and headway. An aggregate equation was also developed, assuming that all respondents shared common tradeoffs. These equations were employed to retrospectively predict changes in transit system patronage since system inception in 1974. Both sets of models performed well, producing forecasts that were in the same direction and range of experience, although magnitudes were somewhat different. Coefficients of the individual tradeoff equations were then analyzed to see if they could be predicted on the basis of interpersonal characteristics of the respondents. Results indicated that differences in coefficients could be attributed to some differences in individuals such as income and auto ownership. Overall results were promising for policy evaluation and forecasting.« less

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Real-time emergency forecasting technique for situation management systems

NASA Astrophysics Data System (ADS)

Kopytov, V. V.; Kharechkin, P. V.; Naumenko, V. V.; Tretyak, R. S.; Tebueva, F. B.

2018-05-01

The article describes the real-time emergency forecasting technique that allows increasing accuracy and reliability of forecasting results of any emergency computational model applied for decision making in situation management systems. Computational models are improved by the Improved Brown’s method applying fractal dimension to forecast short time series data being received from sensors and control systems. Reliability of emergency forecasting results is ensured by the invalid sensed data filtering according to the methods of correlation analysis.

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

Sources and dynamics of turbulence in the upper troposphere and lower stratosphere: A review

NASA Astrophysics Data System (ADS)

Sharman, R. D.; Trier, S. B.; Lane, T. P.; Doyle, J. D.

2012-06-01

Turbulence is a well-known hazard to aviation that is responsible for numerous injuries each year, with occasional fatalities, and is the underlying cause of many people's fear of air travel. Not only are turbulence encounters a safety issue, they also result in millions of dollars of operational costs to airlines, leading to increased costs passed on to the consumer. For these reasons, pilots, dispatchers, and air traffic controllers attempt to avoid turbulence wherever possible. Accurate forecasting of aviation-scale turbulence has been hampered in part by a lack of understanding of the underlying dynamical processes. However, more precise observations of turbulence encounters together with recent research into turbulence generation processes is helping to elucidate the detailed dynamical processes involved and is laying the foundation for improved turbulence forecasting and avoidance. In this paper we briefly review some of the more important recent observational, theoretical, and modeling results related to turbulence at cruise altitudes for commercial aircraft (i.e., the upper troposphere and lower stratosphere), and their implications for aviation turbulence forecasting.

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

Tsunamis generated by eruptions from mount st. Augustine volcano, alaska.

PubMed

Kienle, J; Kowalik, Z; Murty, T S

1987-06-12

During an eruption of the Alaskan volcano Mount St. Augustine in the spring of 1986, there was concern about the possibility that a tsunami might be generated by the collapse of a portion of the volcano into the shallow water of Cook Inlet. A similar edifice collapse of the volcano and ensuing sea wave occurred during an eruption in 1883. Other sea waves resulting in great loss of life and property have been generated by the eruption of coastal volcanos around the world. Although Mount St. Augustine remained intact during this eruptive cycle, a possible recurrence of the 1883 events spurred a numerical simulation of the 1883 sea wave. This simulation, which yielded a forecast of potential wave heights and travel times, was based on a method that could be applied generally to other coastal volcanos.

Detection of emerging sunspot regions in the solar interior.

PubMed

Ilonidis, Stathis; Zhao, Junwei; Kosovichev, Alexander

2011-08-19

Sunspots are regions where strong magnetic fields emerge from the solar interior and where major eruptive events occur. These energetic events can cause power outages, interrupt telecommunication and navigation services, and pose hazards to astronauts. We detected subsurface signatures of emerging sunspot regions before they appeared on the solar disc. Strong acoustic travel-time anomalies of an order of 12 to 16 seconds were detected as deep as 65,000 kilometers. These anomalies were associated with magnetic structures that emerged with an average speed of 0.3 to 0.6 kilometer per second and caused high peaks in the photospheric magnetic flux rate 1 to 2 days after the detection of the anomalies. Thus, synoptic imaging of subsurface magnetic activity may allow anticipation of large sunspot regions before they become visible, improving space weather forecast.

Validation of travel times to hospital estimated by GIS.

PubMed

Haynes, Robin; Jones, Andrew P; Sauerzapf, Violet; Zhao, Hongxin

2006-09-19

An increasing number of studies use GIS estimates of car travel times to health services, without presenting any evidence that the estimates are representative of real travel times. This investigation compared GIS estimates of travel times with the actual times reported by a sample of 475 cancer patients who had travelled by car to attend clinics at eight hospitals in the North of England. Car travel times were estimated by GIS using the shortest road route between home address and hospital and average speed assumptions. These estimates were compared with reported journey times and straight line distances using graphical, correlation and regression techniques. There was a moderately strong association between reported times and estimated travel times (r = 0.856). Reported travel times were similarly related to straight line distances. Altogether, 50% of travel time estimates were within five minutes of the time reported by respondents, 77% were within ten minutes and 90% were within fifteen minutes. The distribution of over- and under-estimates was symmetrical, but estimated times tended to be longer than reported times with increasing distance from hospital. Almost all respondents rounded their travel time to the nearest five or ten minutes. The reason for many cases of reported journey times exceeding the estimated times was confirmed by respondents' comments as traffic congestion. GIS estimates of car travel times were moderately close approximations to reported times. GIS travel time estimates may be superior to reported travel times for modelling purposes because reported times contain errors and can reflect unusual circumstances. Comparison with reported times did not suggest that estimated times were a more sensitive measure than straight line distance.

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.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

50 CFR 260.79 - Travel and other expenses.

Code of Federal Regulations, 2013 CFR

2013-10-01

... based on an hourly rate, an additional hourly charge may be made for travel time including time spent waiting for transportation as well as time spent traveling, but not to exceed 8 hours of travel time for... charge may be made for travel time outside the employee's official work hours. ...

50 CFR 260.79 - Travel and other expenses.

Code of Federal Regulations, 2014 CFR

2014-10-01

... based on an hourly rate, an additional hourly charge may be made for travel time including time spent waiting for transportation as well as time spent traveling, but not to exceed 8 hours of travel time for... charge may be made for travel time outside the employee's official work hours. ...

50 CFR 260.79 - Travel and other expenses.

Code of Federal Regulations, 2011 CFR

2011-10-01

... based on an hourly rate, an additional hourly charge may be made for travel time including time spent waiting for transportation as well as time spent traveling, but not to exceed 8 hours of travel time for... charge may be made for travel time outside the employee's official work hours. ...

50 CFR 260.79 - Travel and other expenses.

Code of Federal Regulations, 2012 CFR

2012-10-01

... based on an hourly rate, an additional hourly charge may be made for travel time including time spent waiting for transportation as well as time spent traveling, but not to exceed 8 hours of travel time for... charge may be made for travel time outside the employee's official work hours. ...

50 CFR 260.79 - Travel and other expenses.

Code of Federal Regulations, 2010 CFR

2010-10-01

... based on an hourly rate, an additional hourly charge may be made for travel time including time spent waiting for transportation as well as time spent traveling, but not to exceed 8 hours of travel time for... charge may be made for travel time outside the employee's official work hours. ...

Using CATS Near-Real-time Lidar Observations to Monitor and Constrain Volcanic Sulfur Dioxide (SO2) Forecasts

NASA Technical Reports Server (NTRS)

Hughes, E. J.; Yorks, J.; Krotkov, N. A.; da Silva, A. M.; Mcgill, M.

2016-01-01

An eruption of Italian volcano Mount Etna on 3 December 2015 produced fast-moving sulfur dioxide (SO2) and sulfate aerosol clouds that traveled across Asia and the Pacific Ocean, reaching North America in just 5 days. The Ozone Profiler and Mapping Suite's Nadir Mapping UV spectrometer aboard the U.S. National Polar-orbiting Partnership satellite observed the horizontal transport of the SO2 cloud. Vertical profiles of the colocated volcanic sulfate aerosols were observed between 11.5 and 13.5 km by the new Cloud Aerosol Transport System (CATS) space-based lidar aboard the International Space Station. Backward trajectory analysis estimates the SO2 cloud altitude at 7-12 km. Eulerian model simulations of the SO2 cloud constrained by CATS measurements produced more accurate dispersion patterns compared to those initialized with the back trajectory height estimate. The near-real-time data processing capabilities of CATS are unique, and this work demonstrates the use of these observations to monitor and model volcanic clouds.

Using CATS Near-Real-Time Lidar Observations to Monitor and Constrain Volcanic Sulfur Dioxide (SO2) Forecasts

NASA Technical Reports Server (NTRS)

Hughes, E. J.; Yorks, J.; Krotkov, N. A.; Da Silva, A. M.; McGill, M.

2016-01-01

An eruption of Italian volcano Mount Etna on 3 December 2015 produced fast-moving sulfur dioxide (SO2) and sulfate aerosol clouds that traveled across Asia and the Pacific Ocean, reaching North America in just 5days. The Ozone Profiler and Mapping Suite's Nadir Mapping UV spectrometer aboard the U.S. National Polar-orbiting Partnership satellite observed the horizontal transport of the SO2 cloud. Vertical profiles of the colocated volcanic sulfate aerosols were observed between 11.5 and 13.5 km by the new Cloud Aerosol Transport System (CATS) space-based lidar aboard the International Space Station. Backward trajectory analysis estimates the SO2 cloud altitude at 7-12 km. Eulerian model simulations of the SO2 cloud constrained by CATS measurements produced more accurate dispersion patterns compared to those initialized with the back trajectory height estimate. The near-real-time data processing capabilities of CATS are unique, and this work demonstrates the use of these observations to monitor and model volcanic clouds.

Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

NASA Astrophysics Data System (ADS)

Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.

2015-04-01

Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.

An evaluation of the impact of biomass burning smoke aerosol particles on near surface temperature forecasts

NASA Astrophysics Data System (ADS)

Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.; Marquis, J. W.

2016-12-01

Currently, with the improvements in aerosol forecast accuracies through aerosol data assimilation, the community is unavoidably facing a scientific question: is it worth the computational time to insert real-time aerosol analyses into numerical models for weather forecasts? In this study, by analyzing a significant biomass burning aerosol event that occurred in 2015 over the Northern part of the Central US, the impact of aerosol particles on near-surface temperature forecasts is evaluated. The aerosol direct surface cooling efficiency, which links surface temperature changes to aerosol loading, is derived from observational-based data for the first time. The potential of including real-time aerosol analyses into weather forecasting models for near surface temperature forecasts is also investigated.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Factors that Influence the Use of Climate Forecasts: Evidence from the 1997/98 El Niño Event in Peru.

NASA Astrophysics Data System (ADS)

Orlove, Benjamin S.; Broad, Kenneth; Petty, Aaron M.

2004-11-01

This article analyzes the use of climate forecasts among members of the Peruvian fishing sector during the 1997/98 El Niño event. It focuses on the effect of the time of hearing a forecast on the socioeconomic responses to the forecast. Findings are based on data collected from a survey of 596 persons in five ports spanning the length of the Peruvian coast. Respondents include commercial and artisanal fishers, plant workers, managers, and firm owners.These data fill an important gap in the literature on the use of forecasts. Though modelers have discussed the effects of the timing of the dissemination and reception of forecasts, along with other factors, on acting on a forecast once it has been heard, few researchers have gathered empirical evidence on these topics.The 1997/98 El Niño event was covered extensively by the media throughout Peru, affording the opportunity to study the effect of hearing forecasts on actions taken by members of a population directly impacted by ENSO events. Findings of this study examine the relationships among 1) socioeconomic variables, including geographic factors, age, education, income level, organizational ties, and media access; 2) time of hearing the forecast; and 3) actions taken in response to the forecast. Socioeconomic variables have a strong effect on the time of hearing the forecast and the actions taken in response to the forecast; however, time of hearing does not have an independent effect on taking action. The article discusses the implications of these findings for the application of forecasts.A supplement to this article is available online (dx.doi.org/10.1175/BAMS-85-11-Orlove)

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

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

Monitoring and seasonal forecasting of meteorological droughts

NASA Astrophysics Data System (ADS)

Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian

2015-04-01

Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. Unfortunately, a major constraint in current drought outlooks is the lack of reliable monitoring capability for observed precipitation globally in near-real time. Furthermore, drought monitoring systems requires a long record of past observations to provide mean climatological conditions. We address these constraints by developing a novel drought monitoring approach in which monthly mean precipitation is derived from short-range using ECMWF probabilistic forecasts and then merged with the long term precipitation climatology of the Global Precipitation Climatology Centre (GPCC) dataset. Merging the two makes available a real-time global precipitation product out of which the Standardized Precipitation Index (SPI) can be estimated and used for global or regional drought monitoring work. This approach provides stability in that by-passes problems of latency (lags) in having local rain-gauge measurements available in real time or lags in satellite precipitation products. Seasonal drought forecasts can also be prepared using the common methodology and based upon two data sources used to provide initial conditions (GPCC and the ECMWF ERA-Interim reanalysis (ERAI) combined with either the current ECMWF seasonal forecast or a climatology based upon ensemble forecasts. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar to or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. However, results presented regionally and globally pinpoint several regions in the world where drought onset forecasting is feasible and skilful.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

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

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Flight Departure Delay and Rerouting Under Uncertainty in En Route Convective Weather

NASA Technical Reports Server (NTRS)

Mukherjee, Avijit; Grabbe, Shon; Sridhar, Banavar

2011-01-01

Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, updated weather forecasts and flight schedules are used. At each stage, weather forecasts up to a certain look-ahead time are treated as deterministic and flight scheduling is done to mitigate the impact of weather on four-dimensional flight trajectories. Uncertainty in weather forecasts during departure scheduling results in tactical airborne holding of flights. The amount of airborne holding depends on the accuracy of forecasts as well as the look-ahead time included in the departure scheduling. The weather forecast look-ahead time is varied systematically within the experiments performed in this paper to analyze its effect on flight delays. Based on the results, longer look-ahead times cause higher departure delays and additional flying time due to reroutes. However, the amount of airborne holding necessary to prevent weather incursions reduces when the forecast look-ahead times are higher. For the chosen day of traffic and weather, setting the look-ahead time to 90 minutes yields the lowest total delay cost.

41 CFR 301-52.14 - What must I do with any travel advance outstanding at the time I submit my travel claim?

Code of Federal Regulations, 2013 CFR

2013-07-01

... travel advance outstanding at the time I submit my travel claim? 301-52.14 Section 301-52.14 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES... § 301-52.14 What must I do with any travel advance outstanding at the time I submit my travel claim? You...

41 CFR 301-52.14 - What must I do with any travel advance outstanding at the time I submit my travel claim?

Code of Federal Regulations, 2010 CFR

2010-07-01

... travel advance outstanding at the time I submit my travel claim? 301-52.14 Section 301-52.14 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES... § 301-52.14 What must I do with any travel advance outstanding at the time I submit my travel claim? You...

41 CFR 301-52.14 - What must I do with any travel advance outstanding at the time I submit my travel claim?

Code of Federal Regulations, 2012 CFR

2012-07-01

... travel advance outstanding at the time I submit my travel claim? 301-52.14 Section 301-52.14 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES... § 301-52.14 What must I do with any travel advance outstanding at the time I submit my travel claim? You...

41 CFR 301-52.14 - What must I do with any travel advance outstanding at the time I submit my travel claim?

Code of Federal Regulations, 2014 CFR

2014-07-01

... travel advance outstanding at the time I submit my travel claim? 301-52.14 Section 301-52.14 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES... § 301-52.14 What must I do with any travel advance outstanding at the time I submit my travel claim? You...

41 CFR 301-52.14 - What must I do with any travel advance outstanding at the time I submit my travel claim?

Code of Federal Regulations, 2011 CFR

2011-07-01

... travel advance outstanding at the time I submit my travel claim? 301-52.14 Section 301-52.14 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES... § 301-52.14 What must I do with any travel advance outstanding at the time I submit my travel claim? You...

Skip the trip: air travelers' behavioral responses to pandemic influenza.

PubMed

Fenichel, Eli P; Kuminoff, Nicolai V; Chowell, Gerardo

2013-01-01

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over "swine flu," as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

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.

5 CFR 551.422 - Time spent traveling.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Time spent traveling. 551.422 Section 551... Activities § 551.422 Time spent traveling. (a) Time spent traveling shall be considered hours of work if: (1... who is permitted to use an alternative mode of transportation, or an employee who travels at a time...

5 CFR 551.422 - Time spent traveling.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 5 Administrative Personnel 1 2013-01-01 2013-01-01 false Time spent traveling. 551.422 Section 551... Activities § 551.422 Time spent traveling. (a) Time spent traveling shall be considered hours of work if: (1... who is permitted to use an alternative mode of transportation, or an employee who travels at a time...

5 CFR 551.422 - Time spent traveling.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 5 Administrative Personnel 1 2011-01-01 2011-01-01 false Time spent traveling. 551.422 Section 551... Activities § 551.422 Time spent traveling. (a) Time spent traveling shall be considered hours of work if: (1... who is permitted to use an alternative mode of transportation, or an employee who travels at a time...

5 CFR 551.422 - Time spent traveling.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 5 Administrative Personnel 1 2012-01-01 2012-01-01 false Time spent traveling. 551.422 Section 551... Activities § 551.422 Time spent traveling. (a) Time spent traveling shall be considered hours of work if: (1... who is permitted to use an alternative mode of transportation, or an employee who travels at a time...

5 CFR 551.422 - Time spent traveling.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 5 Administrative Personnel 1 2014-01-01 2014-01-01 false Time spent traveling. 551.422 Section 551... Activities § 551.422 Time spent traveling. (a) Time spent traveling shall be considered hours of work if: (1... who is permitted to use an alternative mode of transportation, or an employee who travels at a time...

Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

NASA Astrophysics Data System (ADS)

Xu, Tao; Li, Xiang; Claramunt, Christophe

2018-06-01

Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

USGS Publications Warehouse

Clark, M.R.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R.

2004-01-01

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow. ?? 2004 American Meteorological Society.

Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

NASA Astrophysics Data System (ADS)

Rasim; Junaeti, E.; Wirantika, R.

2018-01-01

Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

Regional early flood warning system: design and implementation

NASA Astrophysics Data System (ADS)

Chang, L. C.; Yang, S. N.; Kuo, C. L.; Wang, Y. F.

2017-12-01

This study proposes a prototype of the regional early flood inundation warning system in Tainan City, Taiwan. The AI technology is used to forecast multi-step-ahead regional flood inundation maps during storm events. The computing time is only few seconds that leads to real-time regional flood inundation forecasting. A database is built to organize data and information for building real-time forecasting models, maintaining the relations of forecasted points, and displaying forecasted results, while real-time data acquisition is another key task where the model requires immediately accessing rain gauge information to provide forecast services. All programs related database are constructed in Microsoft SQL Server by using Visual C# to extracting real-time hydrological data, managing data, storing the forecasted data and providing the information to the visual map-based display. The regional early flood inundation warning system use the up-to-date Web technologies driven by the database and real-time data acquisition to display the on-line forecasting flood inundation depths in the study area. The friendly interface includes on-line sequentially showing inundation area by Google Map, maximum inundation depth and its location, and providing KMZ file download of the results which can be watched on Google Earth. The developed system can provide all the relevant information and on-line forecast results that helps city authorities to make decisions during typhoon events and make actions to mitigate the losses.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Using Heliospheric Imaging for Storm Forecasting - SMEI CME Observations as a Tool for Operational Forecasting at AFWA

NASA Astrophysics Data System (ADS)

Webb, D. F.; Johnston, J. C.; Fry, C. D.; Kuchar, T. A.

2008-12-01

Observations of coronal mass ejections (CMEs) from heliospheric imagers such as the Solar Mass Ejection Imager (SMEI) can lead to significant improvements in operational space weather forecasting. We are working with the Air Force Weather Agency (AFWA) to ingest SMEI all-sky imagery with appropriate tools to help forecasters improve their operational space weather forecasts. We describe two approaches: 1) Near- real time analysis of propagating CMEs from SMEI images alone combined with near-Sun observations of CME onsets and, 2) Using these calculations of speed as a mid-course correction to the HAFv2 solar wind model forecasts. HAFv2 became operational at AFWA in late 2006. The objective is to determine a set of practical procedures that the duty forecaster can use to update or correct a solar wind forecast using heliospheric imager data. SMEI observations can be used inclusively to make storm forecasts, as recently discussed in Webb et al. (Space Weather, in press, 2008). We have developed a point-and-click analysis tool for use with SMEI images and are working with AFWA to ensure that timely SMEI images are available for analyses. When a frontside solar eruption occurs, especially if within about 45 deg. of Sun center, a forecaster checks for an associated CME observed by a coronagraph within an appropriate time window. If found, especially if the CME is a halo type, the forecaster checks SMEI observations about a day later, depending on the apparent initial CME speed, for possibly associated CMEs. If one is found, then the leading edge is measured over several successive frames and an elongation-time plot constructed. A minimum of three data points, i.e., over 3-4 orbits or about 6 hours, are necessary for such a plot. Using the solar source location and onset time of the CME from, e.g., SOHO observations, and assuming radial propagation, a distance-time relation is calculated and extrapolated to the 1 AU distance. As shown by Webb et al., the storm onset time is then expected to be about 3 hours after this 1 AU arrival time (AT). The prediction program is updated as more SMEI data become available. Currently when an appropriate solar event occurs, AFWA routinely runs the HAFv2 model to make a forecast of the shock and ejecta arrival times at Earth. SMEI data can be used to improve this prediction. The HAFv2 model can produce synthetic sky maps of predicted CME brightness for comparison with SMEI images. The forecaster uses SMEI imagery to observe and track the CME. The forecaster then measures the CME location and speed using the SMEI imagery and the HAFv2 synthetic sky maps. After comparing the SMEI and HAFv2 results, the forecaster can adjust a key input to HAFv2, such as the initial speed of the disturbance at the Sun or the mid-course speed. The forecaster then iteratively runs HAFv2 until the observed and forecast sky maps match. The final HAFv2 solution becomes the new forecast. When the CME/shock arrives at (or does not reach) Earth, the forecaster verifies the forecast and updates the forecast skill statistics. Eventually, we plan to develop a more automated version of this procedure.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

A hybrid group method of data handling with discrete wavelet transform for GDP forecasting

NASA Astrophysics Data System (ADS)

Isa, Nadira Mohamed; Shabri, Ani

2013-09-01

This study is proposed the application of hybridization model using Group Method of Data Handling (GMDH) and Discrete Wavelet Transform (DWT) in time series forecasting. The objective of this paper is to examine the flexibility of the hybridization GMDH in time series forecasting by using Gross Domestic Product (GDP). A time series data set is used in this study to demonstrate the effectiveness of the forecasting model. This data are utilized to forecast through an application aimed to handle real life time series. This experiment compares the performances of a hybrid model and a single model of Wavelet-Linear Regression (WR), Artificial Neural Network (ANN), and conventional GMDH. It is shown that the proposed model can provide a promising alternative technique in GDP forecasting.

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, 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

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

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.

2017-12-01

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

How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

Measuring workplace travel behaviour: validity and reliability of survey questions.

PubMed

Petrunoff, Nicholas A; Xu, Huilan; Rissel, Chris; Wen, Li Ming; van der Ploeg, Hidde P

2013-01-01

The purpose of this study was to assess the (previously untested) reliability and validity of survey questions commonly used to assess travel mode and travel time. Sixty-five respondents from a staff survey of travel behaviour conducted in a south-western Sydney hospital agreed to complete a travel diary for a week, wear an accelerometer over the same period, and twice complete an online travel survey an average of 21 days apart. The agreement in travel modes between the self-reported online survey and travel diary was examined with the kappa statistic. Spearman's correlation coefficient was used to examine agreement of travel time from home to workplace measured between the self-reported online survey and four-day travel diary. Moderate-to-vigorous physical activity (MVPA) time of active and nonactive travellers was compared by t-test. There was substantial agreement between travel modes (K = 0.62, P < 0.0001) and a moderate correlation for travel time (ρ = 0.75, P < 0.0001) reported in the travel diary and online survey. There was a high level of agreement for travel mode (K = 0.82, P < 0.0001) and travel time (ρ = 0.83, P < 0.0001) between the two travel surveys. Accelerometer data indicated that for active travellers, 16% of the journey-to-work time is MVPA, compared with 6% for car drivers. Active travellers were significantly more active across the whole workday. The survey question "How did you travel to work this week? If you used more than one transport mode specify the one you used for the longest (distance) portion of your journey" is reliable over 21 days and agrees well with a travel diary.

Measuring Workplace Travel Behaviour: Validity and Reliability of Survey Questions

PubMed Central

Petrunoff, Nicholas A.; Xu, Huilan; van der Ploeg, Hidde P.

2013-01-01

Background. The purpose of this study was to assess the (previously untested) reliability and validity of survey questions commonly used to assess travel mode and travel time. Methods. Sixty-five respondents from a staff survey of travel behaviour conducted in a south-western Sydney hospital agreed to complete a travel diary for a week, wear an accelerometer over the same period, and twice complete an online travel survey an average of 21 days apart. The agreement in travel modes between the self-reported online survey and travel diary was examined with the kappa statistic. Spearman's correlation coefficient was used to examine agreement of travel time from home to workplace measured between the self-reported online survey and four-day travel diary. Moderate-to-vigorous physical activity (MVPA) time of active and nonactive travellers was compared by t-test. Results. There was substantial agreement between travel modes (K = 0.62, P < 0.0001) and a moderate correlation for travel time (ρ = 0.75, P < 0.0001) reported in the travel diary and online survey. There was a high level of agreement for travel mode (K = 0.82, P < 0.0001) and travel time (ρ = 0.83, P < 0.0001) between the two travel surveys. Accelerometer data indicated that for active travellers, 16% of the journey-to-work time is MVPA, compared with 6% for car drivers. Active travellers were significantly more active across the whole workday. Conclusions. The survey question “How did you travel to work this week? If you used more than one transport mode specify the one you used for the longest (distance) portion of your journey” is reliable over 21 days and agrees well with a travel diary. PMID:23956757

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

Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

PubMed

Xu, Wei; Peng, Yong

2015-01-01

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2015-08-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

Self-motion perception compresses time experienced in return travel.

PubMed

Seno, Takeharu; Ito, Hiroyuki; Shoji, Sunaga

2011-01-01

It is often anecdotally reported that time experienced in return travel (back to the start point) seems shorter than time spent in outward travel (travel to a new destination). Here, we report the first experimental results showing that return travel time is experienced as shorter than the actual time. This discrepancy is induced by the existence of self-motion perception.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data.

PubMed

Liu, Kai; Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

PubMed Central

Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods. PMID:27362654

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

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

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Forecasting Enrollments with Fuzzy Time Series.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.

PubMed

Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger

2017-01-01

We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.

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.

Innovative methods for calculation of freeway travel time using limited data : executive summary report.

DOT National Transportation Integrated Search

2008-08-01

ODOTs policy for Dynamic Message Sign : utilization requires travel time(s) to be displayed as : a default message. The current method of : calculating travel time involves a workstation : operator estimating the travel time based upon : observati...

Fellow travellers: Working memory and mental time travel in rodents.

PubMed

Dere, Ekrem; Dere, Dorothea; de Souza Silva, Maria Angelica; Huston, Joseph P; Zlomuzica, Armin

2017-03-19

The impairment of mental time travel is a severe cognitive symptom in patients with brain lesions and a number of neuropsychiatric disorders. Whether animals are also able to mentally travel in time both forward and backward is still a matter of debate. In this regard, we have proposed a continuum of mental time travel abilities across different animal species, with humans being the species with the ability to perform most sophisticated forms of mental time travel. In this review and perspective article, we delineate a novel approach to understand the evolution, characteristics and function of human and animal mental time travel. Furthermore, we propose a novel approach to measure mental time travel in rodents in a comprehensive manner using a test battery composed of well-validated and easy applicable tests. Copyright © 2017. Published by Elsevier B.V.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009)

PubMed Central

2011-01-01

Background 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. Methods 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. Results 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. Conclusions 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. PMID:21324153

Evaluation of a real-time travel time prediction system in a freeway construction work zone : executive summary.

DOT National Transportation Integrated Search

2001-03-01

A real-time travel time prediction system (TIPS) was evaluated in a construction work : zone. TIPS includes changeable message signs (CMSs) displaying the travel time and : distance to the end of the work zone to motorists. The travel times displayed...

The Role of Perspective in Mental Time Travel.

PubMed

Ansuini, Caterina; Cavallo, Andrea; Pia, Lorenzo; Becchio, Cristina

2016-01-01

Recent years have seen accumulating evidence for the proposition that people process time by mapping it onto a linear spatial representation and automatically "project" themselves on an imagined mental time line. Here, we ask whether people can adopt the temporal perspective of another person when travelling through time. To elucidate similarities and differences between time travelling from one's own perspective or from the perspective of another person, we asked participants to mentally project themselves or someone else (i.e., a coexperimenter) to different time points. Three basic properties of mental time travel were manipulated: temporal location (i.e., where in time the travel originates: past, present, and future), motion direction (either backwards or forwards), and temporal duration (i.e., the distance to travel: one, three, or five years). We found that time travels originating in the present lasted longer in the self- than in the other-perspective. Moreover, for self-perspective, but not for other-perspective, time was differently scaled depending on where in time the travel originated. In contrast, when considering the direction and the duration of time travelling, no dissimilarities between the self- and the other-perspective emerged. These results suggest that self- and other-projection, despite some differences, share important similarities in structure.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared observations at Roque de los Muchachos Observatory (ORM). For the model validation, we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 h (hourly forecasts), from 2016 January 11 to March 04. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-h horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and 0.904 for the 24- and 48-h forecast time series, respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and root-mean-square error of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Effectiveness of different approaches to disseminating traveler information on travel time reliability. [supporting datasets

DOT National Transportation Integrated Search

2013-11-30

Travel time reliability information includes static data about traffic speeds or trip times that capture historic variations from day to day, and it can help individuals understand the level of variation in traffic. Unlike real-time travel time infor...

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

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

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

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

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

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

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

Sea level forecasts for Pacific Islands based on Satellite Altimetry

NASA Astrophysics Data System (ADS)

Yoon, H.; Merrifield, M. A.; Thompson, P. R.; Widlansky, M. J.; Marra, J. J.

2017-12-01

Coastal flooding at tropical Pacific Islands often occurs when positive sea level anomalies coincide with high tides. To help mitigate this risk, a forecast tool for daily-averaged sea level anomalies is developed that can be added to predicted tides at tropical Pacific Island sites. The forecast takes advantage of the observed westward propagation that sea level anomalies exhibit over a range of time scales. The daily near-real time altimetry gridded data from Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) is used to specify upstream sea level at each site, with lead times computed based on mode-one baroclinic Rossby wave speeds. To validate the forecast, hindcasts are compared to tide gauge and nearby AVISO gridded time series. The forecast skills exceed persistence at most stations out to a month or more lead time. The skill is highest at stations where eddy variability is relatively weak. The impacts on the forecasts due to varying propagation speed, decay time, and smoothing of the AVISO data are examined. In addition, the inclusion of forecast winds in a forced wave equation is compared to the freely propagating results. Case studies are presented for seasonally high tide events throughout the Pacific Island region.

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

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

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

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Short-term data forecasting based on wavelet transformation and chaos theory

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Cunbin; Zhang, Liang

2017-09-01

A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

A channel dynamics model for real-time flood forecasting

USGS Publications Warehouse

Hoos, Anne B.; Koussis, Antonis D.; Beale, Guy O.

1989-01-01

A new channel dynamics scheme (alternative system predictor in real time (ASPIRE)), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio.

U.S. Geological Survey Volcano Hazards Program—Assess, forecast, prepare, engage

USGS Publications Warehouse

Stovall, Wendy K.; Wilkins, Aleeza M.; Mandeville, Charles W.; Driedger, Carolyn L.

2016-07-13

At least 170 volcanoes in 12 States and 2 territories have erupted in the past 12,000 years and have the potential to erupt again. Consequences of eruptions from U.S. volcanoes can extend far beyond the volcano’s immediate area. Many aspects of our daily life are vulnerable to volcano hazards, including air travel, regional power generation and transmission infrastructure, interstate transportation, port facilities, communications infrastructure, and public health. The U.S. Geological Survey has the Federal responsibility to issue timely warnings of potential volcanic activity to the affected populace and civil authorities. The Volcano Hazards Program (VHP) is funded to carry out that mission and does so through a combination of volcano monitoring, short-term warnings, research on how volcanoes work, and community education and outreach.

Future Air Traffic Growth and Schedule Model, Supplement

NASA Technical Reports Server (NTRS)

Kimmel, William M. (Technical Monitor); Smith, Jeremy C.; Dollyhigh, Samuel M.

2004-01-01

The Future Air Traffic Growth and Schedule Model was developed as an implementation of the Fratar algorithm to project future traffic flow between airports in a system and of then scheduling the additional flights to reflect current passenger time-of-travel preferences. The methodology produces an unconstrained future schedule from a current (or baseline) schedule and the airport operations growth rates. As an example of the use of the model, future schedules are projected for 2010 and 2022 for all flights arriving at, departing from, or flying between all continental United States airports that had commercial scheduled service for May 17, 2002. Inter-continental US traffic and airports are included and the traffic is also grown with the Fratar methodology to account for their arrivals and departures to the continental US airports. Input data sets derived from the Official Airline Guide (OAG) data and FAA Terminal Area Forecast (TAF) are included in the examples of the computer code execution.

X-class Flare Erupts from Sun on April 24

NASA Image and Video Library

2017-12-08

The sun emitted a significant solar flare, peaking at 8:27 p.m. EDT on April 24, 2014. Images of the flare were captured by NASA's Solar Dynamics Observatory. Solar flares are powerful bursts of radiation. Harmful radiation from a flare cannot pass through Earth's atmosphere to physically affect humans on the ground, however -- when intense enough -- they can disturb the atmosphere in the layer where GPS and communications signals travel. To see how this event may impact Earth, please visit NOAA's Space Weather Prediction Center at spaceweather.gov, the U.S. government's official source for space weather forecasts, alerts, watches and warnings. This flare is classified as an X1.4 flare. X-class denotes the most intense flares, while the number provides more information about its strength. An X2 is twice as intense as an X1, an X3 is three times as intense, etc. Credit: NASA/Goddard/SDO Credit: NASA/SDO

The Economic Consequences of an Invasion of Poland by the Soviet Union.

DTIC Science & Technology

1981-09-01

worker’s salary. Basic food supplies are erratic. Travelers report that at Gorsky, for example, there is no butter, meat, fruit and flour , and that...Lithuanians, Ukranians, Estonians, Georgians, Armenians, Uzbeks and many of the Muslim peoples. One author noted, "With the glue of belief and trust...Forecast," Wall Street Journal , May 18, 1981, p. 31. 56. Daniel Park, "Oil and Gas in Comecon Countries," (London: Kogan Page, 1979) table 24a, p. 41

U.S. Tsunami Warning System: Advancements since the 2004 Indian Ocean Tsunami (Invited)

NASA Astrophysics Data System (ADS)

Whitmore, P.

2009-12-01

The U.S. government embarked on a strengthening program for the U.S. Tsunami Warning System (TWS) in the aftermath of the disastrous 2004 Indian Ocean tsunami. The program was designed to improve several facets of the U.S. TWS, including: upgrade of the coastal sea level network - 16 new stations plus higher transmission rates; expansion of the deep ocean tsunameter network - 7 sites increased to 39; upgrade of seismic networks - both USGS and Tsunami Warning Center (TWC); increase of TWC staff to allow 24x7 coverage at two centers; development of an improved tsunami forecast system; increased preparedness in coastal communities; expansion of the Pacific Tsunami Warning Center facility; and improvement of the tsunami data archive effort at the National Geophysical Data Center. The strengthening program has been completed and has contributed to the many improvements attained in the U.S. TWS since 2004. Some of the more significant enhancements to the program are: the number of sea level and seismic sites worldwide available to the TWCs has more than doubled; the TWC areas-of-responsibility expanded to include the U.S./Canadian Atlantic coasts, Indian Ocean, Caribbean Sea, Gulf of Mexico, and U.S. Arctic coast; event response time decreased by approximately one-half; product accuracy has improved; a tsunami forecast system developed by NOAA capable of forecasting inundation during an event has been delivered to the TWCs; warning areas are now defined by pre-computed or forecasted threat versus distance or travel time, significantly reducing the amount of coast put in a warning; new warning dissemination techniques have been implemented to reach a broader audience in less time; tsunami product content better reflects the expected impact level; the number of TsunamiReady communities has quadrupled; and the historical data archive has increased in quantity and accuracy. In addition to the strengthening program, the U.S. National Tsunami Hazard Mitigation Program (NTHMP) has expanded its efforts since 2004 and improved tsunami preparedness throughout U.S. coastal communities. The NTHMP is a partnership of federal agencies and state tsunami response agencies whose efforts include: development of inundation and evacuation maps for most highly threatened communities; tsunami evacuation and educational signage for coastal communities; support for tsunami educational, awareness and planning seminars; increased number of local tsunami warning dissemination devices such as sirens; and support for regional tsunami exercises. These activities are major factors that have contributed to the increase of TsunamiReady communities throughout the country.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

PubMed

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Network structure and travel time perception.

PubMed

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

NASA Astrophysics Data System (ADS)

Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

2013-04-01

A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro-meteorological stages -read manually once or twice per day - that, despite not ideal in the context of real-time system, improve model performance significantly, and therefore are entered into the system by manual input. At its current configuration, the SPHEB performance objectives are fulfilled for 90% of the forecasts with lead times up to +2 days and +15 hours (using the predictability criteria of the Russian Hydrometeorological Center S/?Δ) and the average accuracy is in the range 70-99% ( r2 criteria). However, longer lead times are at present not satisfactory in terms of forecasts accuracy.

Travel time data collection handbook

DOT National Transportation Integrated Search

1998-03-01

This Travel Time Data Collection Handbook provides guidance to transportation : professionals and practitioners for the collection, reduction, and presentation : of travel time data. The handbook should be a useful reference for designing : travel ti...

Patterns of health behaviour associated with active travel: a compositional data analysis.

PubMed

Foley, Louise; Dumuid, Dorothea; Atkin, Andrew J; Olds, Timothy; Ogilvie, David

2018-03-21

Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel.

Statistical and Hydrological evaluation of precipitation forecasts from IMD MME and ECMWF numerical weather forecasts for Indian River basins

NASA Astrophysics Data System (ADS)

Mohite, A. R.; Beria, H.; Behera, A. K.; Chatterjee, C.; Singh, R.

2016-12-01

Flood forecasting using hydrological models is an important and cost-effective non-structural flood management measure. For forecasting at short lead times, empirical models using real-time precipitation estimates have proven to be reliable. However, their skill depreciates with increasing lead time. Coupling a hydrologic model with real-time rainfall forecasts issued from numerical weather prediction (NWP) systems could increase the lead time substantially. In this study, we compared 1-5 days precipitation forecasts from India Meteorological Department (IMD) Multi-Model Ensemble (MME) with European Center for Medium Weather forecast (ECMWF) NWP forecasts for over 86 major river basins in India. We then evaluated the hydrologic utility of these forecasts over Basantpur catchment (approx. 59,000 km2) of the Mahanadi River basin. Coupled MIKE 11 RR (NAM) and MIKE 11 hydrodynamic (HD) models were used for the development of flood forecast system (FFS). RR model was calibrated using IMD station rainfall data. Cross-sections extracted from SRTM 30 were used as input to the MIKE 11 HD model. IMD started issuing operational MME forecasts from the year 2008, and hence, both the statistical and hydrologic evaluation were carried out from 2008-2014. The performance of FFS was evaluated using both the NWP datasets separately for the year 2011, which was a large flood year in Mahanadi River basin. We will present figures and metrics for statistical (threshold based statistics, skill in terms of correlation and bias) and hydrologic (Nash Sutcliffe efficiency, mean and peak error statistics) evaluation. The statistical evaluation will be at pan-India scale for all the major river basins and the hydrologic evaluation will be for the Basantpur catchment of the Mahanadi River basin.

Monitoring the impact of decentralised chronic care services on patient travel time in rural Africa--methods and results in Northern Malawi.

PubMed

Houben, Rein M G J; Van Boeckel, Thomas P; Mwinuka, Venance; Mzumara, Peter; Branson, Keith; Linard, Catherine; Chimbwandira, Frank; French, Neil; Glynn, Judith R; Crampin, Amelia C

2012-11-15

Decentralised health services form a key part of chronic care strategies in resource-limited settings by reducing the distance between patient and clinic and thereby the time and costs involved in travelling. However, few tools exist to evaluate the impact of decentralisation on patient travel time or what proportion of patients attend their nearest clinic. Here we develop methods to monitor changes in travel time, using data from the antiretroviral therapy (ART) roll-out in a rural district in North Malawi. Clinic position was combined with GPS information on the home village of patients accessing ART services in Karonga District (North Malawi) between July 2005 and July 2009. Potential travel time was estimated as the travel time for an individual attending their nearest clinic, and estimated actual travel time as the time to the clinic attended. This allowed us to calculate changes in potential and actual travel time as new clinics opened and track the proportion and origin of patients not accessing their nearest clinic. The model showed how the opening of further ART clinics in Karonga District reduced median potential travel time from 83 to 43 minutes, and median actual travel time fell from 83 to 47 minutes. The proportion of patients not attending their nearest clinic increased from 6% when two clinics were open, to 12% with four open. Integrating GPS information with patient data shows the impact of decentralisation on travel time and clinic choice to inform policy and research questions. In our case study, travel time decreased, accompanied by an increased uptake of services. However, the model also identified an increasing proportion of ART patients did not attend their nearest clinic.

Forecasting Jakarta composite index (IHSG) based on chen fuzzy time series and firefly clustering algorithm

NASA Astrophysics Data System (ADS)

Ningrum, R. W.; Surarso, B.; Farikhin; Safarudin, Y. M.

2018-03-01

This paper proposes the combination of Firefly Algorithm (FA) and Chen Fuzzy Time Series Forecasting. Most of the existing fuzzy forecasting methods based on fuzzy time series use the static length of intervals. Therefore, we apply an artificial intelligence, i.e., Firefly Algorithm (FA) to set non-stationary length of intervals for each cluster on Chen Method. The method is evaluated by applying on the Jakarta Composite Index (IHSG) and compare with classical Chen Fuzzy Time Series Forecasting. Its performance verified through simulation using Matlab.

Evaluation of a real-time travel time prediction system in a freeway construction work zone : final report, March 2001.

DOT National Transportation Integrated Search

2001-03-01

A real-time travel time prediction system (TIPS) was evaluated in a construction work zone. TIPS includes changeable message signs (CMSs) displaying the travel time and distance to the end of the work zone to motorists. The travel times displayed by ...

48 CFR 752.7002 - Travel and transportation.

Code of Federal Regulations, 2010 CFR

2010-10-01

... referred to as the Standardized Regulations—as from time to time amended, for not more than the travel time...” clause of this contract, time spent away from post resulting from educational travel will be counted as... time amended, for not more than the travel time required by scheduled commercial air carrier using the...

Seasonal forecasting of discharge for the Raccoon River, Iowa

NASA Astrophysics Data System (ADS)

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

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast weighting procedures based on the computed potential skill (historical forecast accuracy) of the different GCMs. We find that the models describe the year-to-year variability in streamflow accurately, as well as the overall tendency towards increasing (and more variable) discharge over time. Surprisingly, forecast skill does not decrease markedly with lead time, and high flows tend to be well predicted, suggesting that these forecasts may have considerable practical applications. Further, the seasonal flow forecast accuracy is substantially improved by weighting the contribution of individual GCMs to the forecasts, and also by the inclusion of antecedent precipitation. Our results can provide critical information for adaptation strategies aiming to mitigate the costs and disruptions arising from flood and drought conditions, and allow us to determine how far in advance skillful forecasts can be issued. The availability of these discharge forecasts would have major societal and economic benefits for hydrology and water resources management, agriculture, disaster forecasts and prevention, energy, finance and insurance, food security, policy-making and public authorities, and transportation.

Research on Nonlinear Time Series Forecasting of Time-Delay NN Embedded with Bayesian Regularization

NASA Astrophysics Data System (ADS)

Jiang, Weijin; Xu, Yusheng; Xu, Yuhui; Wang, Jianmin

Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the imp&exp trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecasting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably 'catch' the dynamic characteristic of the nonlinear system which produced the origin serial.

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

NASA Astrophysics Data System (ADS)

Rogelis, María Carolina; Werner, Micha

2018-02-01

Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

5 CFR 630.207 - Travel time.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 5 Administrative Personnel 1 2011-01-01 2011-01-01 false Travel time. 630.207 Section 630.207... and General Provisions for Annual and Sick Leave § 630.207 Travel time. The travel time granted an employee under section 6303(d) of title 5, United States Code, is inclusive of the time necessarily...

5 CFR 630.207 - Travel time.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Travel time. 630.207 Section 630.207... and General Provisions for Annual and Sick Leave § 630.207 Travel time. The travel time granted an employee under section 6303(d) of title 5, United States Code, is inclusive of the time necessarily...

5 CFR 630.207 - Travel time.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 5 Administrative Personnel 1 2014-01-01 2014-01-01 false Travel time. 630.207 Section 630.207... and General Provisions for Annual and Sick Leave § 630.207 Travel time. The travel time granted an employee under section 6303(d) of title 5, United States Code, is inclusive of the time necessarily...

5 CFR 630.207 - Travel time.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 5 Administrative Personnel 1 2012-01-01 2012-01-01 false Travel time. 630.207 Section 630.207... and General Provisions for Annual and Sick Leave § 630.207 Travel time. The travel time granted an employee under section 6303(d) of title 5, United States Code, is inclusive of the time necessarily...

5 CFR 630.207 - Travel time.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 5 Administrative Personnel 1 2013-01-01 2013-01-01 false Travel time. 630.207 Section 630.207... and General Provisions for Annual and Sick Leave § 630.207 Travel time. The travel time granted an employee under section 6303(d) of title 5, United States Code, is inclusive of the time necessarily...

Network Structure and Travel Time Perception

PubMed Central

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time. PMID:24204932

Final Report Phase I Study to Characterize the Market Potential for Non-Motorized Travel

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

Hwang, Ho-Ling; Reuscher, Tim; Wilson, Daniel W

The idea of livable communities suggests that people should have the option to utilize non-motorized travel (NMT), specifically walking and bicycling, to conduct their daily tasks. Forecasting personal travel by walk and bike is necessary as part of regional transportation planning, and requires fine detail not only about individual travel, but also on transportation and neighborhood infrastructure. In an attempt to characterize the 'market' potential for NMT, the Office of Planning, Federal Highway Administration (FHWA) funded the Center for Transportation Analysis (CTA) of the Oak Ridge National Laboratory (ORNL) to conduct a study. The objectives of this effort were tomore » identify factors that influence communities to walk and bike and to examine why, or why not, travelers walk and bike in their communities. This study relied on information collected under the 2009 National Household Travel Survey (NHTS) as the major source of data, and was supplemented with data from the American Community Survey (ACS), educational survey, health, employment, and others. Initial statistical screening methods were applied to sort through over 400 potential predictor variables, and examined with various measures (e.g., walk trip per person, walk mileage per person, bike trip per person, bike mileage per person) as the dependent variables. The best geographic level of detail used in the modeling for this study was determined to be the Census block group level for walking and Census tract level for biking. The need for additional supplemental private data (i.e., Walk Scores and Nielsen employment data), and geospatial information that reflects land use and physical environments, became evident after an examination of findings from the initial screening models. To be feasible, in terms of costs and time, the geographic scale of the study region was scaled down to nine selected NHTS add-on regions. These regions were chosen based on various criteria including transit availability, population size, and a mix of geographic locations across the nation. Given the similarities in modeling results from walk trips and walk mileages, additional modeling efforts conducted under the later part of this study were focused on walk trips per person. Bike models were limited only with the stepwise logistic models using Census tracts in the selected regions. Due to NHTS sampling limitations, only about 12% of these tracts have bike trips recorded from NHTS sampled households. The modeling with NHTS bike data proved to be more challenging and time consuming than what was anticipated. Along with the late arrival of Nielsen employment data, the project team had to limit the modeling effort to focus on walking. Therefore, the final modeling and discriminant analysis was conducted only for walking trips.« less

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

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

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

7 CFR 52.50 - Travel and other expenses.

Code of Federal Regulations, 2014 CFR

2014-01-01

... travel time incurred in connection with the performance of any inspection service, including appeal... time spent traveling, but not to exceed eight hours of travel time for any one person for any one day... 7 Agriculture 2 2014-01-01 2014-01-01 false Travel and other expenses. 52.50 Section 52.50...

7 CFR 52.50 - Travel and other expenses.

Code of Federal Regulations, 2013 CFR

2013-01-01

... travel time incurred in connection with the performance of any inspection service, including appeal... time spent traveling, but not to exceed eight hours of travel time for any one person for any one day... 7 Agriculture 2 2013-01-01 2013-01-01 false Travel and other expenses. 52.50 Section 52.50...

The impact of the characteristics of volcanic ash on forecasting.

NASA Astrophysics Data System (ADS)

Beckett, Frances; Hort, Matthew; Millington, Sarah; Stevenson, John; Witham, Claire

2013-04-01

The eruption of Eyjafjallajökull during April - May 2010 and Grímsvötn in May 2011, Iceland, caused the widespread dispersion of volcanic ash across the NE Atlantic, and ultimately into UK and European airspace. This resulted in thousands of flights to and from affected countries across Europe to be cancelled. The Met Office, UK, is the home of the London VAAC, a Volcanic Ash Advisory Centre, and as such is responsible for providing reports and forecasts for the movement of volcanic ash clouds covering the UK, Iceland and the north-eastern part of the North Atlantic ocean. To forecast the dispersion of volcanic ash requires that the sedimentation of ash particles through the atmosphere is effectively modelled. The settling velocity of an ash particle is a function of its size, shape and density, plus the density and viscosity of the air through which it is falling. We consider the importance of characterising the physical properties of ash when modelling the long range dispersion of ash particles through the atmosphere. Using the Reynolds number dependent scheme employed by NAME, the Lagrangian particle model used operationally by the Met Office, we calculate the settling velocity and thus the maximum travel distance of an ash particle through an idealised atmosphere as a function of its size, shape and density. The results are compared to measured particle sizes from deposits across Europe following the eruption of Eyjafjallajökull in 2010. Further, the particle size distribution (PSD) of ash in a volcanic cloud with time is modelled using NAME: the particle density distribution and particle shape factor are varied and the modelled PSD compared to the PSD measured in the ash cloud during the eruption of Eyjafjallajökull in 2010 by the FAAM research aircraft. The influence of the weather on PSD is also considered by comparing model output using an idealised atmosphere to output using NWP driven meteorological fields. We discuss the sensitivity of forecasts of the dispersion of volcanic ash to the representation of particle characteristics in NAME, the importance of representing the weather in ash fall models, and the implications of these results for the operational forecasting of volcanic ash dispersion at the London VAAC.

Wireless data collection system for travel time estimation and traffic performance evaluation.

DOT National Transportation Integrated Search

2010-09-01

Having accurate and continually updated travel time and other performance data for the road and highway system has many benefits. From the perspective of the road users, having real-time updates on travel times will permit better travel and route pla...

Validation and augmentation of Inrix arterial travel time data using independent sources : [research summary].

DOT National Transportation Integrated Search

2015-02-01

Although the freeway travel time data has been validated extensively in recent : years, the quality of arterial travel time data is not well known. This project : presents a comprehensive validation scheme for arterial travel time data based : on GPS...

The value of forecasting key-decision variables for rain-fed farming

NASA Astrophysics Data System (ADS)

Winsemius, Hessel; Werner, Micha

2013-04-01

Rain-fed farmers are highly vulnerable to variability in rainfall. Timely knowledge of the onset of the rainy season, the expected amount of rainfall and the occurrence of dry spells can help rain-fed farmers to plan the cropping season. Seasonal probabilistic weather forecasts may provide such information to farmers, but need to provide reliable forecasts of key variables with which farmers can make decisions. In this contribution, we present a new method to evaluate the value of meteorological forecasts in predicting these key variables. The proposed method measures skill by assessing whether a forecast was useful to this decision. This is done by taking into account the required accuracy of timing of the event to make the decision useful. The method progresses the estimate of forecast skill to forecast value by taking into account the required accuracy that is needed to make the decision valuable, based on the cost/loss ratio of possible decisions. The method is applied over the Limpopo region in Southern Africa. We demonstrate the method using the example of temporary water harvesting techniques. Such techniques require time to construct and must be ready long enough before the occurrence of a dry spell to be effective. The value of the forecasts to the decision used as an example is shown to be highly sensitive to the accuracy in the timing of forecasted dry spells, and the tolerance in the decision to timing error. The skill with which dry spells can be predicted is shown to be higher in some parts of the basin, indicating that these forecasts have higher value for the decision in those parts than in others. Through assessing the skill of forecasting key decision variables to the farmers we show that it is easier to understand if the forecasts have value in reducing risk, or if other adaptation strategies should be implemented.

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

NASA Astrophysics Data System (ADS)

Singhofen, P.

2017-12-01

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

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.

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

PubMed

Wang, Wen-chuan; Chau, Kwok-wing; Qiu, Lin; Chen, Yang-bo

2015-05-01

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting. Copyright © 2015 Elsevier Inc. All rights reserved.

Using a numerical model to understand the connection between the ocean and acoustic travel-time measurements.

PubMed

Powell, Brian S; Kerry, Colette G; Cornuelle, Bruce D

2013-10-01

Measurements of acoustic ray travel-times in the ocean provide synoptic integrals of the ocean state between source and receiver. It is known that the ray travel-time is sensitive to variations in the ocean at the transmission time, but the sensitivity of the travel-time to spatial variations in the ocean prior to the acoustic transmission have not been quantified. This study examines the sensitivity of ray travel-time to the temporally and spatially evolving ocean state in the Philippine Sea using the adjoint of a numerical model. A one year series of five day backward integrations of the adjoint model quantify the sensitivity of travel-times to varying dynamics that can alter the travel-time of a 611 km ray by 200 ms. The early evolution of the sensitivities reveals high-mode internal waves that dissipate quickly, leaving the lowest three modes, providing a connection to variations in the internal tide generation prior to the sample time. They are also strongly sensitive to advective effects that alter density along the ray path. These sensitivities reveal how travel-time measurements are affected by both nearby and distant waters. Temporal nonlinearity of the sensitivities suggests that prior knowledge of the ocean state is necessary to exploit the travel-time observations.

Metrics for the Evaluation the Utility of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Sumo, T. M.; Stockwell, W. R.

2013-12-01

Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.

Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2014-10-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

Uses and Applications of Climate Forecasts for Power Utilities.

NASA Astrophysics Data System (ADS)

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

1995-05-01

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

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

10 CFR 719.43 - What is the treatment of travel costs?

Code of Federal Regulations, 2014 CFR

2014-01-01

...-46, as appropriate, to be reimbursable. (b) Travel time may be allowed at a full hourly rate for the... remaining travel time shall be reimbursed at 50 percent of the full hourly rate, except that in no event will travel time spent working for other clients be allowable. Also, for long distance travel that...

Assessment of seasonal soil moisture forecasts over Southern South America with emphasis on dry and wet events

NASA Astrophysics Data System (ADS)

Spennemann, Pablo; Rivera, Juan Antonio; Osman, Marisol; Saulo, Celeste; Penalba, Olga

2017-04-01

The importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socio-economic losses, mainly in regions of large populations and where agriculture is a key value for the economies, like Southern South America (SSA). Therefore, to improve the understanding of the performance and uncertainties of seasonal soil moisture and precipitation forecasts over SSA, this study aims to: 1) perform a general assessment of the Climate Forecast System version-2 (CFSv2) soil moisture and precipitation forecasts; and 2) evaluate the CFSv2 ability to represent an extreme drought event merging observations with forecasted Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Anomalies (SSMA) based on GLDAS-2.0 simulations. Results show that both SPI and SSMA forecast skill are regionally and seasonally dependent. In general a fast degradation of the forecasts skill is observed as the lead time increases with no significant metrics for forecast lead times longer than 2 months. Based on the assessment of the 2008-2009 extreme drought event it is evident that the CFSv2 forecasts have limitations regarding the identification of drought onset, duration, severity and demise, considering both meteorological (SPI) and agricultural (SSMA) drought conditions. These results have some implications upon the use of seasonal forecasts to assist agricultural practices in SSA, given that forecast skill is still too low to be useful for lead times longer than 2 months.

23 CFR 511.309 - Provisions for traffic and travel conditions reporting.

Code of Federal Regulations, 2012 CFR

2012-04-01

... requirements for traffic and travel conditions made available by real-time information programs are: (1... or less from the time the hazardous conditions, blockage, or closure is observed. (4) Travel time information. The timeliness for the availability of travel time information along limited access roadway...

Performance of the 'material Failure Forecast Method' in real-time situations: A Bayesian approach applied on effusive and explosive eruptions

NASA Astrophysics Data System (ADS)

Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.; Arámbula-Mendoza, R.; Budi-Santoso, A.

2016-11-01

Most attempts of deterministic eruption forecasting are based on the material Failure Forecast Method (FFM). This method assumes that a precursory observable, such as the rate of seismic activity, can be described by a simple power law which presents a singularity at a time close to the eruption onset. Until now, this method has been applied only in a small number of cases, generally for forecasts in hindsight. In this paper, a rigorous Bayesian approach of the FFM designed for real-time applications is applied. Using an automatic recognition system, seismo-volcanic events are detected and classified according to their physical mechanism and time series of probability distributions of the rates of events are calculated. At each time of observation, a Bayesian inversion provides estimations of the exponent of the power law and of the time of eruption, together with their probability density functions. Two criteria are defined in order to evaluate the quality and reliability of the forecasts. Our automated procedure has allowed the analysis of long, continuous seismic time series: 13 years from Volcán de Colima, Mexico, 10 years from Piton de la Fournaise, Reunion Island, France, and several months from Merapi volcano, Java, Indonesia. The new forecasting approach has been applied to 64 pre-eruptive sequences which present various types of dominant seismic activity (volcano-tectonic or long-period events) and patterns of seismicity with different level of complexity. This has allowed us to test the FFM assumptions, to determine in which conditions the method can be applied, and to quantify the success rate of the forecasts. 62% of the precursory sequences analysed are suitable for the application of FFM and half of the total number of eruptions are successfully forecast in hindsight. In real-time, the method allows for the successful forecast of 36% of all the eruptions considered. Nevertheless, real-time forecasts are successful for 83% of the cases that fulfil the reliability criteria. Therefore, good confidence on the method is obtained when the reliability criteria are met.

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

Improvement of forecast skill for severe weather by merging radar-based extrapolation and storm-scale NWP corrected forecast

NASA Astrophysics Data System (ADS)

Wang, Gaili; Wong, Wai-Kin; Hong, Yang; Liu, Liping; Dong, Jili; Xue, Ming

2015-03-01

The primary objective of this study is to improve the performance of deterministic high resolution rainfall forecasts caused by severe storms by merging an extrapolation radar-based scheme with a storm-scale Numerical Weather Prediction (NWP) model. Effectiveness of Multi-scale Tracking and Forecasting Radar Echoes (MTaRE) model was compared with that of a storm-scale NWP model named Advanced Regional Prediction System (ARPS) for forecasting a violent tornado event that developed over parts of western and much of central Oklahoma on May 24, 2011. Then the bias corrections were performed to improve the forecast accuracy of ARPS forecasts. Finally, the corrected ARPS forecast and radar-based extrapolation were optimally merged by using a hyperbolic tangent weight scheme. The comparison of forecast skill between MTaRE and ARPS in high spatial resolution of 0.01° × 0.01° and high temporal resolution of 5 min showed that MTaRE outperformed ARPS in terms of index of agreement and mean absolute error (MAE). MTaRE had a better Critical Success Index (CSI) for less than 20-min lead times and was comparable to ARPS for 20- to 50-min lead times, while ARPS had a better CSI for more than 50-min lead times. Bias correction significantly improved ARPS forecasts in terms of MAE and index of agreement, although the CSI of corrected ARPS forecasts was similar to that of the uncorrected ARPS forecasts. Moreover, optimally merging results using hyperbolic tangent weight scheme further improved the forecast accuracy and became more stable.

Network Performance Evaluation Model for assessing the impacts of high-occupancy vehicle facilities

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

Janson, B.N.; Zozaya-Gorostiza, C.; Southworth, F.

1986-09-01

A model to assess the impacts of major high-occupancy vehicle (HOV) facilities on regional levels of energy consumption and vehicle air pollution emissions in urban aeas is developed and applied. This model can be used to forecast and compare the impacts of alternative HOV facility design and operation plans on traffic patterns, travel costs, model choice, travel demand, energy consumption and vehicle emissions. The model is designed to show differences in the overall impacts of alternative HOV facility types, locations and operation plans rather than to serve as a tool for detailed engineering design and traffic planning studies. The Networkmore » Performance Evaluation Model (NETPEM) combines several urban transportation planning models within a multi-modal network equilibrium framework including modules with which to define the type, location and use policy of the HOV facility to be tested, and to assess the impacts of this facility.« less

Volcanic ash hazards and aviation risk: Chapter 4

USGS Publications Warehouse

Guffanti, Marianne C.; Tupper, Andrew C.

2015-01-01

The risks to safe and efficient air travel from volcanic-ash hazards are well documented and widely recognized. Under the aegis of the International Civil Aviation Organization, globally coordinated mitigation procedures are in place to report explosive eruptions, detect airborne ash clouds and forecast their expected movement, and issue specialized messages to warn aircraft away from hazardous airspace. This mitigation framework is based on the integration of scientific and technical capabilities worldwide in volcanology, meteorology, and atmospheric physics and chemistry. The 2010 eruption of Eyjafjallajökull volcano in Iceland, which led to a nearly week-long shutdown of air travel into and out of Europe, has prompted the aviation industry, regulators, and scientists to work more closely together to improve how hazardous airspace is defined and communicated. Volcanic ash will continue to threaten aviation and scientific research will continue to influence the risk-mitigation framework.

Portals for Real-Time Earthquake Data and Forecasting: Challenge and Promise (Invited)

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Feltstykket, R.; Donnellan, A.; Glasscoe, M. T.

2013-12-01

Earthquake forecasts have been computed by a variety of countries world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. However, recent events clearly demonstrate that mitigating personal risk is becoming the responsibility of individual members of the public. Open access to a variety of web-based forecasts, tools, utilities and information is therefore required. Portals for data and forecasts present particular challenges, and require the development of both apps and the client/server architecture to deliver the basic information in real time. The basic forecast model we consider is the Natural Time Weibull (NTW) method (JBR et al., Phys. Rev. E, 86, 021106, 2012). This model uses small earthquakes (';seismicity-based models') to forecast the occurrence of large earthquakes, via data-mining algorithms combined with the ANSS earthquake catalog. This method computes large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Localizing these forecasts in space so that global forecasts can be computed in real time presents special algorithmic challenges, which we describe in this talk. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we compute real-time global forecasts at a grid scale of 0.1o. We analyze and monitor the performance of these models using the standard tests, which include the Reliability/Attributes and Receiver Operating Characteristic (ROC) tests. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges of serving up these datasets over the web on web-based platforms such as those at www.quakesim.org , www.e-decider.org , and www.openhazards.com.

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.

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

During hurricane Harvey the greater east Texas area was affected by extensive flash flooding. Their localised nature meant they were too small for conventional large scale flood forecasting systems to capture. We are testing the use of two real time forecast products from the European Centre for Medium-range Weather Forecasts (ECMWF) in combination with local vulnerability information to provide flash flood forecasting tools at the medium range (up to 7 days ahead). Meteorological forecasts are the total precipitation extreme forecast index (EFI), a measure of how the ensemble forecast probability distribution differs from the model-climate distribution for the chosen location, time of year and forecast lead time; and the shift of tails (SOT) which complements the EFI by quantifying how extreme an event could potentially be. Both products give the likelihood of flash flood generating precipitation. For hurricane Harvey, 3-day EFI and SOT products for the period 26th - 29th August 2017 were used, generated from the twice daily, 18 km, 51 ensemble member ECMWF Integrated Forecast System. After regridding to 1 km resolution the forecasts were combined with vulnerable area data to produce a flash flood hazard risk area. The vulnerability data were floodplains (EU Joint Research Centre), road networks (Texas Department of Transport) and urban areas (Census Bureau geographic database), together reflecting the susceptibility to flash floods from the landscape. The flash flood hazard risk area forecasts were verified using a traditional approach against observed National Weather Service flash flood reports, a total of 153 reported flash floods have been detected in that period. Forecasts performed best for SOT = 5 (hit ratio = 65%, false alarm ratio = 44%) and EFI = 0.7 (hit ratio = 74%, false alarm ratio = 45%) at 72 h lead time. By including the vulnerable areas data, our verification results improved by 5-15%, demonstrating the value of vulnerability information within natural hazard forecasts. This research shows that flash flooding from hurricane Harvey was predictable up to 4 days ahead and that filtering the forecasts to vulnerable areas provides a more focused guidance to civil protection agencies planning their emergency response.

On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

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

Forecasting daily patient volumes in the emergency department.

PubMed

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

2008-02-01

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

7 CFR 52.50 - Travel and other expenses.

Code of Federal Regulations, 2010 CFR

2010-01-01

... and other expenses. Charges may be made to cover the cost of travel time incurred in connection with... hour. This includes time spent waiting for transportation as well as time spent traveling, but not to exceed eight hours of travel time for any one person for any one day: And provided further, that if...

7 CFR 52.50 - Travel and other expenses.

Code of Federal Regulations, 2011 CFR

2011-01-01

... and other expenses. Charges may be made to cover the cost of travel time incurred in connection with... hour. This includes time spent waiting for transportation as well as time spent traveling, but not to exceed eight hours of travel time for any one person for any one day: And provided further, that if...

77 FR 45415 - Preparation of an Environmental Impact Statement for the Redlands; Passenger Rail Project in the...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-31

..., travel times, and corridor safety. DATES: The date, time, and location for the public scoping meetings... mobility, travel times, and corridor safety while minimizing adverse environmental impacts. The RPRP would provide travelers and commuters with a new mobility option that would achieve more-efficient travel times...

7 CFR 52.50 - Travel and other expenses.

Code of Federal Regulations, 2012 CFR

2012-01-01

... and other expenses. Charges may be made to cover the cost of travel time incurred in connection with... hour. This includes time spent waiting for transportation as well as time spent traveling, but not to exceed eight hours of travel time for any one person for any one day: And provided further, that if...

Travelers’ Value of Time and Reliability as Measured on Katy Freeway : Final Report.

DOT National Transportation Integrated Search

2016-09-01

The value of travel time savings (VOT) is an estimate of what travelers would be willing to pay in order to save time on a particular trip. If travelers would pay $1 to reduce their travel time by six minutes, then they have a VOT of $10 per hour. VO...

A new short-term forecasting model for the total electron content storm time disturbances

NASA Astrophysics Data System (ADS)

Tsagouri, Ioanna; Koutroumbas, Konstantinos; Elias, Panagiotis

2018-06-01

This paper aims to introduce a new model for the short-term forecast of the vertical Total Electron Content (vTEC). The basic idea of the proposed model lies on the concept of the Solar Wind driven autoregressive model for Ionospheric short-term Forecast (SWIF). In its original version, the model is operationally implemented in the DIAS system (http://dias.space.noa.gr) and provides alerts and warnings for upcoming ionospheric disturbances, as well as single site and regional forecasts of the foF2 critical frequency over Europe up to 24 h in advance. The forecasts are driven by the real time assessment of the solar wind conditions at ACE location. The comparative analysis of the variations in foF2 and vTEC during eleven geomagnetic storm events that occurred in the present solar cycle 24 reveals similarities but also differences in the storm-time response of the two characteristics with respect to the local time and the latitude of the observation point. Since the aforementioned dependences drive the storm-time forecasts of the SWIF model, the results obtained here support the upgrade of the SWIF's modeling technique in forecasting the storm-time vTEC variation from its onset to full development and recovery. According to the proposed approach, the vTEC storm-time response can be forecasted from 1 to 12-13 h before its onset, depending on the local time of the observation point at storm onset at L1. Preliminary results on the assessment of the performance of the proposed model and further considerations on its potential implementation in operational mode are also discussed.

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.

Performance of time-series methods in forecasting the demand for red blood cell transfusion.

PubMed

Pereira, Arturo

2004-05-01

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

2016-12-01

Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.

Northern Hemisphere climate trends in reanalysis and forecast model predictions: The 500 hPa annual means

NASA Astrophysics Data System (ADS)

Bordi, I.; Fraedrich, K.; Sutera, A.

2010-06-01

The lead time dependent climates of the ECMWF weather prediction model, initialized with ERA-40 reanalysis, are analysed using 44 years of day-1 to day-10 forecasts of the northern hemispheric 500-hPa geopotential height fields. The study addresses the question whether short-term tendencies have an impact on long-term trends. Comparing climate trends of ERA-40 with those of the forecasts, it seems that the forecast model rapidly loses the memory of initial conditions creating its own climate. All forecast trends show a high degree of consistency. Comparison results suggest that: (i) Only centers characterized by an upward trend are statistical significant when increasing the lead time. (ii) In midilatitudes an upward trend larger than the one observed in the reanalysis characterizes the forecasts, while in the tropics there is a good agreement. (iii) The downward trend in reanalysis at high latitudes characterizes also the day-1 forecast which, however, increasing lead time approaches zero.

The travel-time ellipse: An approximate zone of transport

USGS Publications Warehouse

Almendinger, J.E.

1994-01-01

A zone of transport for a well is defined as the area in the horizontal plane bounded by a contour of equal ground-water travel time to the well. For short distances and ground-water travel times near a well, the potentiometric surface may be simulated analytically as that for a fully penetrating well in a uniform flow field. The zone of transport for this configuration is nearly elliptical. A simple method is derived to calculate a travel-time ellipse that approximates the zone of transport for a well in a uniform flow field. The travel-time ellipse was nearly congruent with the exact solution for the theoretical zone of transport for ground-water travel times of at least 10 years and for aquifer property values appropriate for southeastern Minnesota. For distances and travel times approaching infinity, however, the ellipse becomes slightly wider at its midpoint and narrower near its upgradient boundary than the theoretical zone of transport. The travel-time ellipse also may be used to simulate the plume area surrounding an injection well. However, the travel-time ellipse is an approximation that does not account for the effect of dispersion in enlarging the true area of an injection plume or zone of transport; hence, caution is advised in the use and interpretation of this simple construction.

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.

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

2016-06-01

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

PubMed Central

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

PubMed

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Forecasting Occurrences of Activities.

PubMed

Minor, Bryan; Cook, Diane J

2017-07-01

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

Ensemble-based methods for forecasting census in hospital units

PubMed Central

2013-01-01

Background The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. Methods In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Results Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Conclusions Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts. PMID:23721123

Ensemble-based methods for forecasting census in hospital units.

PubMed

Koestler, Devin C; Ombao, Hernando; Bender, Jesse

2013-05-30

The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts.

Skip the Trip: Air Travelers' Behavioral Responses to Pandemic Influenza

PubMed Central

Fenichel, Eli P.; Kuminoff, Nicolai V.; Chowell, Gerardo

2013-01-01

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication. PMID:23526970

Environmental consequences of Pollution and its Impact on earth's surface climate Using Geospatial Technology

NASA Astrophysics Data System (ADS)

Kumar, Amit

2016-07-01

Modern transportation is an indispensable ingredient for development, allowing the pressure group of labor, supplies and goods, and enabling general public to access key resources and services. Climate change is a most important threat to sustainable development in any developing or developed country. Urban air pollution is on the rise, due to rapid economic and inhabitants growth and an increase in motorization. Modern transport is fundamental for improvement, allowing the movement of goods and enabling general public to access key resources and services. Travel today is relatively faster and people across the world are travelling more than ever before. Its stipulate regarding forecast is an indispensable part of transportation development in order to evaluate future needs of an urban area. Over increasing traffic concentration posed continued threat to ambient air quality and responsible for producing agents of physical condition hazards. Geospatial technology provides the smartest approach to resolve these inconvenience as it can cover a large area in a fraction of time. The research work focuses on the recognition of traffic intensities with increasing of SO2, NO2 and noise level considered at particular traffic sites in the Varanasi, Uttar Pradesh, India. SO2, NO2 and Noise levels recorded in the city, are much higher than the permissible level and are likely to causes associated health and psychological illnesses to nearby inhabitant.

Environmental consequences of Pollution and its Impact on climate Using Geospatial Technology

NASA Astrophysics Data System (ADS)

Kumar, Amit; Vandana, Vandana

2016-07-01

Modern transportation is an indispensable ingredient for development, allowing the pressure group of labor, supplies and goods, and enabling general public to access key resources and services. Climate change is a most important threat to sustainable development in any developing or developed country. Urban air pollution is on the rise, due to rapid economic and inhabitants growth and an increase in motorization. Modern transport is fundamental for improvement, allowing the movement of goods and enabling general public to access key resources and services. Travel today is relatively faster and people across the world are travelling more than ever before. Its stipulate regarding forecast is an indispensable part of transportation development in order to evaluate future needs of an urban area. Over increasing traffic concentration posed continued threat to ambient air quality and responsible for producing agents of physical condition hazards. Geospatial technology provides the smartest approach to resolve these inconvenience as it can cover a large area in a fraction of time. The research work focuses on the recognition of traffic intensities with increasing of SO2, NO2 and noise level considered at particular traffic sites in the Varanasi, Uttar Pradesh, India. SO2, NO2 and Noise levels recorded in the city, are much higher than the permissible level and are likely to causes associated health and psychological illnesses to nearby inhabitant. Keywords: Population growth; Traffic; Transportation

Nonlinear refraction and reflection travel time tomography

USGS Publications Warehouse

Zhang, Jiahua; ten Brink, Uri S.; Toksoz, M.N.

1998-01-01

We develop a rapid nonlinear travel time tomography method that simultaneously inverts refraction and reflection travel times on a regular velocity grid. For travel time and ray path calculations, we apply a wave front method employing graph theory. The first-arrival refraction travel times are calculated on the basis of cell velocities, and the later refraction and reflection travel times are computed using both cell velocities and given interfaces. We solve a regularized nonlinear inverse problem. A Laplacian operator is applied to regularize the model parameters (cell slownesses and reflector geometry) so that the inverse problem is valid for a continuum. The travel times are also regularized such that we invert travel time curves rather than travel time points. A conjugate gradient method is applied to minimize the nonlinear objective function. After obtaining a solution, we perform nonlinear Monte Carlo inversions for uncertainty analysis and compute the posterior model covariance. In numerical experiments, we demonstrate that combining the first arrival refraction travel times with later reflection travel times can better reconstruct the velocity field as well as the reflector geometry. This combination is particularly important for modeling crustal structures where large velocity variations occur in the upper crust. We apply this approach to model the crustal structure of the California Borderland using ocean bottom seismometer and land data collected during the Los Angeles Region Seismic Experiment along two marine survey lines. Details of our image include a high-velocity zone under the Catalina Ridge, but a smooth gradient zone between. Catalina Ridge and San Clemente Ridge. The Moho depth is about 22 km with lateral variations. Copyright 1998 by the American Geophysical Union.

Real-time forecasting of the April 11, 2012 Sumatra tsunami

USGS Publications Warehouse

Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

2012-01-01

The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

26 CFR 1.274-4 - Disallowance of certain foreign travel expenses.

Code of Federal Regulations, 2010 CFR

2010-04-01

... paragraph (d) of this section) constitutes 25 percent or more of the total time on such travel. (c) Travel... percent or more of travel time under paragraph (d) of this section and for purposes of allocating expenses... percent or more of travel time—(1) In general. This section does not apply to any expense of travel...

26 CFR 1.274-4 - Disallowance of certain foreign travel expenses.

Code of Federal Regulations, 2011 CFR

2011-04-01

... paragraph (d) of this section) constitutes 25 percent or more of the total time on such travel. (c) Travel... percent or more of travel time under paragraph (d) of this section and for purposes of allocating expenses... percent or more of travel time—(1) In general. This section does not apply to any expense of travel...

26 CFR 1.274-4 - Disallowance of certain foreign travel expenses.

Code of Federal Regulations, 2014 CFR

2014-04-01

... paragraph (d) of this section) constitutes 25 percent or more of the total time on such travel. (c) Travel... percent or more of travel time under paragraph (d) of this section and for purposes of allocating expenses... percent or more of travel time—(1) In general. This section does not apply to any expense of travel...

26 CFR 1.274-4 - Disallowance of certain foreign travel expenses.

Code of Federal Regulations, 2013 CFR

2013-04-01

... paragraph (d) of this section) constitutes 25 percent or more of the total time on such travel. (c) Travel... percent or more of travel time under paragraph (d) of this section and for purposes of allocating expenses... percent or more of travel time—(1) In general. This section does not apply to any expense of travel...

26 CFR 1.274-4 - Disallowance of certain foreign travel expenses.

Code of Federal Regulations, 2012 CFR

2012-04-01

... paragraph (d) of this section) constitutes 25 percent or more of the total time on such travel. (c) Travel... percent or more of travel time under paragraph (d) of this section and for purposes of allocating expenses... percent or more of travel time—(1) In general. This section does not apply to any expense of travel...

An investigation into incident duration forecasting for FleetForward

DOT National Transportation Integrated Search

2000-08-01

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

Travel during Pregnancy

MedlinePlus

... are 36 weeks pregnant. When is the best time to travel during pregnancy? The best time to travel is the middle of your pregnancy—between week ... Sitting or not moving for long periods of time, such as during long-distance travel, can increase the risk of DVT. Pregnancy further ...

Assessing segment- and corridor-based travel-time reliability on urban freeways tech transfer summary : tech transfer summary.

DOT National Transportation Integrated Search

2016-09-01

Travel time and travel-time reliability have been used as performance : measures to evaluate traffic system conditions and develop advanced : traveler information and traffic management systems. The objectives of this research were to: : - Quantify s...

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

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; Li, Tim

2018-03-01

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

Traveller Information System for Heterogeneous Traffic Condition: A Case Study in Thiruvananthapuram City, India

NASA Astrophysics Data System (ADS)

Satyakumar, M.; Anil, R.; Sreeja, G. S.

2017-12-01

Traffic in Kerala has been growing at a rate of 10-11% every year, resulting severe congestion especially in urban areas. Because of the limitation of spaces it is not always possible to construct new roads. Road users rely on travel time information for journey planning and route choice decisions, while road system managers are increasingly viewing travel time as an important network performance indicator. More recently Advanced Traveler Information Systems (ATIS) are being developed to provide real-time information to roadway users. For ATIS various methodologies have been developed for dynamic travel time prediction. For this work the Kalman Filter Algorithm was selected for dynamic travel time prediction of different modes. The travel time data collected using handheld GPS device were used for prediction. Congestion Index were calculated and Range of CI values were determined according to the percentage speed drop. After prediction using Kalman Filter, the predicted values along with the GPS data was integrated to GIS and using Network Analysis of ArcGIS the offline route navigation guide was prepared. Using this database a program for route navigation based on travel time was developed. This system will help the travelers with pre-trip information.

An algorithm of Saxena-Easo on fuzzy time series forecasting

NASA Astrophysics Data System (ADS)

Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

2018-04-01

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting

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

Hodge, Brian S; Zhang, Xinmin; Li, Yuan

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, formore » both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.« less

41 CFR 301-71.306 - Are there exceptions to collecting an advance at the time the employee files a travel claim?

Code of Federal Regulations, 2014 CFR

2014-07-01

... collecting an advance at the time the employee files a travel claim? 301-71.306 Section 301-71.306 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES...-71.306 Are there exceptions to collecting an advance at the time the employee files a travel claim...

41 CFR 301-71.306 - Are there exceptions to collecting an advance at the time the employee files a travel claim?

Code of Federal Regulations, 2013 CFR

2013-07-01

... collecting an advance at the time the employee files a travel claim? 301-71.306 Section 301-71.306 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES...-71.306 Are there exceptions to collecting an advance at the time the employee files a travel claim...

41 CFR 301-71.306 - Are there exceptions to collecting an advance at the time the employee files a travel claim?

Code of Federal Regulations, 2012 CFR

2012-07-01

... collecting an advance at the time the employee files a travel claim? 301-71.306 Section 301-71.306 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES...-71.306 Are there exceptions to collecting an advance at the time the employee files a travel claim...

41 CFR 301-71.306 - Are there exceptions to collecting an advance at the time the employee files a travel claim?

Code of Federal Regulations, 2010 CFR

2010-07-01

... collecting an advance at the time the employee files a travel claim? 301-71.306 Section 301-71.306 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES...-71.306 Are there exceptions to collecting an advance at the time the employee files a travel claim...

41 CFR 301-71.306 - Are there exceptions to collecting an advance at the time the employee files a travel claim?

Code of Federal Regulations, 2011 CFR

2011-07-01

... collecting an advance at the time the employee files a travel claim? 301-71.306 Section 301-71.306 Public Contracts and Property Management Federal Travel Regulation System TEMPORARY DUTY (TDY) TRAVEL ALLOWANCES...-71.306 Are there exceptions to collecting an advance at the time the employee files a travel claim...

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

NASA Astrophysics Data System (ADS)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

NASA Astrophysics Data System (ADS)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2015-10-01

Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

Delay functions in trip assignment for transport planning process

NASA Astrophysics Data System (ADS)

Leong, Lee Vien

2017-10-01

In transportation planning process, volume-delay and turn-penalty functions are the functions needed in traffic assignment to determine travel time on road network links. Volume-delay function is the delay function describing speed-flow relationship while turn-penalty function is the delay function associated to making a turn at intersection. The volume-delay function used in this study is the revised Bureau of Public Roads (BPR) function with the constant parameters, α and β values of 0.8298 and 3.361 while the turn-penalty functions for signalized intersection were developed based on uniform, random and overflow delay models. Parameters such as green time, cycle time and saturation flow were used in the development of turn-penalty functions. In order to assess the accuracy of the delay functions, road network in areas of Nibong Tebal, Penang and Parit Buntar, Perak was developed and modelled using transportation demand forecasting software. In order to calibrate the models, phase times and traffic volumes at fourteen signalised intersections within the study area were collected during morning and evening peak hours. The prediction of assigned volumes using the revised BPR function and the developed turn-penalty functions show close agreement to actual recorded traffic volume with the lowest percentage of accuracy, 80.08% and the highest, 93.04% for the morning peak model. As for the evening peak model, they were 75.59% and 95.33% respectively for lowest and highest percentage of accuracy. As for the yield left-turn lanes, the lowest percentage of accuracy obtained for the morning and evening peak models were 60.94% and 69.74% respectively while the highest percentage of accuracy obtained for both models were 100%. Therefore, can be concluded that the development and utilisation of delay functions based on local road conditions are important as localised delay functions can produce better estimate of link travel times and hence better planning for future scenarios.

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

[Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

PubMed

Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

2015-02-01

It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation.

Comparison of discrete Fourier transform (DFT) and principal component analysis/DFT as forecasting tools for absorbance time series received by UV-visible probes installed in urban sewer systems.

PubMed

Plazas-Nossa, Leonardo; Torres, Andrés

2014-01-01

The objective of this work is to introduce a forecasting method for UV-Vis spectrometry time series that combines principal component analysis (PCA) and discrete Fourier transform (DFT), and to compare the results obtained with those obtained by using DFT. Three time series for three different study sites were used: (i) Salitre wastewater treatment plant (WWTP) in Bogotá; (ii) Gibraltar pumping station in Bogotá; and (iii) San Fernando WWTP in Itagüí (in the south part of Medellín). Each of these time series had an equal number of samples (1051). In general terms, the results obtained are hardly generalizable, as they seem to be highly dependent on specific water system dynamics; however, some trends can be outlined: (i) for UV range, DFT and PCA/DFT forecasting accuracy were almost the same; (ii) for visible range, the PCA/DFT forecasting procedure proposed gives systematically lower forecasting errors and variability than those obtained with the DFT procedure; and (iii) for short forecasting times the PCA/DFT procedure proposed is more suitable than the DFT procedure, according to processing times obtained.

76 FR 1501 - Preparation of Environmental Impact Statement for Transit Improvements in the US 90A/Southwest...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-10

... limited traffic capacity on existing streets and highways resulting in increased travel time, delays, and... result, bus travel times are influenced by roadway congestion which is anticipated to increase. Peak period bus travel times can be as much as 30 percent longer than travel times during off-peak periods. In...

Road Network State Estimation Using Random Forest Ensemble Learning

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

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less

Field deployment to quantify the value of real-time information by integrating driver routing decisions and route assignment strategies.

DOT National Transportation Integrated Search

2014-05-01

Advanced Traveler Information Systems (ATIS) have been proposed as a mechanism to generate and : distribute real-time travel information to drivers for the purpose of improving travel experience : represented by experienced travel time and enhancing ...

Walrus distributional and foraging response to changing ice and benthic conditions in the Chukchi Sea

USGS Publications Warehouse

Jay, Chadwick V.; Grebmeier, Jacqueline M.; Fischbach, Anthony S.

2012-01-01

Arctic species such as the Pacific walrus (Odobenus rosmarus divergens) are facing a rapidly changing environment. Walruses are benthic foragers and may shift their spatial patterns of foraging in response to changes in prey distribution. We used data from satellite radio-tags attached to walruses in 2009-2010 to map walrus foraging locations with concurrent sampling of benthic infauna to examine relationships between distributions of dominant walrus prey and spatial patterns of walrus foraging. Walrus foraging was concentrated offshore in the NE Chukchi Sea, and coastal areas of northwestern Alaska when sea ice was sparse. Walrus foraging areas in August-September were coincident with the biomass of two dominant bivalve taxa (Tellinidae and Nuculidae) and sipunculid worms. Walrusforaging costs associated with increased travel time to higher biomass food patches from land may be significantly higher than the costs from sea ice haul-outs and result in reduced energy storesin walruses. Identifying what resources are selected by walruses and how those resources are distributed in space and time will improve our ability to forecast how walruses might respond to a changing climate.

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.

A simplified real time method to forecast semi-enclosed basins storm surge

NASA Astrophysics Data System (ADS)

Pasquali, D.; Di Risio, M.; De Girolamo, P.

2015-11-01

Semi-enclosed basins are often prone to storm surge events. Indeed, their meteorological exposition, the presence of large continental shelf and their shape can lead to strong sea level set-up. A real time system aimed at forecasting storm surge may be of great help to protect human activities (i.e. to forecast flooding due to storm surge events), to manage ports and to safeguard coasts safety. This paper aims at illustrating a simple method able to forecast storm surge events in semi-enclosed basins in real time. The method is based on a mixed approach in which the results obtained by means of a simplified physics based model with low computational costs are corrected by means of statistical techniques. The proposed method is applied to a point of interest located in the Northern part of the Adriatic Sea. The comparison of forecasted levels against observed values shows the satisfactory reliability of the forecasts.

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

2014-01-01

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Work, family and daily mobility: a new approach to the problem through a mobility survey.

PubMed

Olabarria, Marta; Pérez, Katherine; Santamariña-Rubio, Elena; Aragay, Josep Maria; Capdet, Mayte; Peiró, Rosana; Rodríguez-Sanz, Maica; Artazcoz, Lucía; Borrell, Carme

2013-01-01

To analyze gender inequalities in socioeconomic factors affecting the amount of time spent travelling for work-related and home-related reasons among working individuals aged between 30 and 44 years old during a weekday in Catalonia (Spain). A cross-sectional study was conducted. Data were obtained from employed individuals aged between 30 and 44 years of age who reported travelling on the day prior to the interview in the Catalan Mobility Survey 2006 (N = 23,424). Multivariate logistic regression models were adjusted to determine the factors associated with longer time spent travelling according to the reason for travelling (work- or home-related journeys). Odds ratios and 95% confidence intervals are presented. A higher proportion of men travelled and spent more time travelling for work-related reasons, while a higher proportion of women travelled and spend more time travelling for home-related reasons. A higher educational level was associated with greater time spent travelling for work-related reasons in both men and women but was related to an increase in travelling time for home-related reasons only in men. In women, a larger household was associated with greater travel time for home-related reasons and with less travel time for work-related reasons. This study confirms the different mobility patterns in men and women, related to their distinct positions in the occupational, family and domestic spheres. Gender inequalities in mobility within the working population are largely determined by the greater responsibility of women in the domestic and family sphere. This finding should be taken into account in the design of future transport policies. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

[Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

PubMed

Luo, Yi; Zhang, Tao; Li, Xiao-song

2016-05-01

To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

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

NASA Astrophysics Data System (ADS)

Ancell, B. C.

2017-12-01

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

Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

NASA Astrophysics Data System (ADS)

BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

2013-09-01

The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

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

NASA Technical Reports Server (NTRS)

Rousseaux, Cecile S.; Gregg, Watson W.

2018-01-01

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

Forecasting Ocean Chlorophyll in the Equatorial Pacific.

PubMed

Rousseaux, Cecile S; Gregg, Watson W

2017-01-01

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

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.

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.

The impact of travel distance, travel time and waiting time on health-related quality of life of diabetes patients: An investigation in six European countries.

PubMed

Konerding, Uwe; Bowen, Tom; Elkhuizen, Sylvia G; Faubel, Raquel; Forte, Paul; Karampli, Eleftheria; Mahdavi, Mahdi; Malmström, Tomi; Pavi, Elpida; Torkki, Paulus

2017-04-01

The effects of travel distance and travel time to the primary diabetes care provider and waiting time in the practice on health-related quality of life (HRQoL) of patients with type 2 diabetes are investigated. Survey data of 1313 persons with type 2 diabetes from six regions in England (274), Finland (163), Germany (254), Greece (165), the Netherlands (354), and Spain (103) were analyzed. Various multiple linear regression analyses with four different EQ-5D-3L indices (English, German, Dutch and Spanish index) as target variables, with travel distance, travel time, and waiting time in the practice as focal predictors and with control for study region, patient's gender, patient's age, patient's education, time since diagnosis, thoroughness of provider-patient communication were computed. Interactions of regions with the remaining five control variables and the three focal predictors were also tested. There are no interactions of regions with control variables or focal predictors. The indices decrease with increasing travel time to the provider and increasing waiting time in the provider's practice. HRQoL of patients with type 2 diabetes might be improved by decreasing travel time to the provider and waiting time in the provider's practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

PubMed

Hansen, J V; Nelson, R D

1997-01-01

Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

A quantitative comparison of precipitation forecasts between the storm-scale numerical weather prediction model and auto-nowcast system in Jiangsu, China

NASA Astrophysics Data System (ADS)

Wang, Gaili; Yang, Ji; Wang, Dan; Liu, Liping

2016-11-01

Extrapolation techniques and storm-scale Numerical Weather Prediction (NWP) models are two primary approaches for short-term precipitation forecasts. The primary objective of this study is to verify precipitation forecasts and compare the performances of two nowcasting schemes: a Beijing Auto-Nowcast system (BJ-ANC) based on extrapolation techniques and a storm-scale NWP model called the Advanced Regional Prediction System (ARPS). The verification and comparison takes into account six heavy precipitation events that occurred in the summer of 2014 and 2015 in Jiangsu, China. The forecast performances of the two schemes were evaluated for the next 6 h at 1-h intervals using gridpoint-based measures of critical success index, bias, index of agreement, root mean square error, and using an object-based verification method called Structure-Amplitude-Location (SAL) score. Regarding gridpoint-based measures, BJ-ANC outperforms ARPS at first, but then the forecast accuracy decreases rapidly with lead time and performs worse than ARPS after 4-5 h of the initial forecast. Regarding the object-based verification method, most forecasts produced by BJ-ANC focus on the center of the diagram at the 1-h lead time and indicate high-quality forecasts. As the lead time increases, BJ-ANC overestimates precipitation amount and produces widespread precipitation, especially at a 6-h lead time. The ARPS model overestimates precipitation at all lead times, particularly at first.

31 CFR 515.564 - Professional research and professional meetings in Cuba.

Code of Federal Regulations, 2010 CFR

2010-07-01

... to professional research by full-time professionals who travel to Cuba to conduct professional... transactions as are directly incident to travel to Cuba by full-time professionals to attend professional... such a provider; and (ii) The traveler's schedule of activities does not include free time, travel, or...

31 CFR 515.564 - Professional research and professional meetings in Cuba.

Code of Federal Regulations, 2012 CFR

2012-07-01

... research by full-time professionals who travel to Cuba to conduct professional research in their... directly incident to travel to Cuba by full-time professionals to attend professional meetings or... provider; and (ii) The traveler's schedule of activities does not include free time, travel, or recreation...

31 CFR 515.564 - Professional research and professional meetings in Cuba.

Code of Federal Regulations, 2014 CFR

2014-07-01

... research by full-time professionals who travel to Cuba to conduct professional research in their... directly incident to travel to Cuba by full-time professionals to attend professional meetings or... provider; and (ii) The traveler's schedule of activities does not include free time, travel, or recreation...

31 CFR 515.564 - Professional research and professional meetings in Cuba.

Code of Federal Regulations, 2011 CFR

2011-07-01

... research by full-time professionals who travel to Cuba to conduct professional research in their... directly incident to travel to Cuba by full-time professionals to attend professional meetings or... provider; and (ii) The traveler's schedule of activities does not include free time, travel, or recreation...

31 CFR 515.564 - Professional research and professional meetings in Cuba.

Code of Federal Regulations, 2013 CFR

2013-07-01

... research by full-time professionals who travel to Cuba to conduct professional research in their... directly incident to travel to Cuba by full-time professionals to attend professional meetings or... provider; and (ii) The traveler's schedule of activities does not include free time, travel, or recreation...

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

[Travel times of patients to ambulatory care physicians in Germany].

PubMed

Schang, Laura; Kopetsch, Thomas; Sundmacher, Leonie

2017-12-01

The time needed by patients to get to a doctor's office represents an important indicator of realised access to care. In Germany, findings on travel times are only available from surveys or for some regions. For the first time, this study examines nationwide and physician group-specific travel times in the ambulatory care sector in Germany and describes demographic, supply-side and spatial determinants of variations. Using a full review of patient consultations in the statutory health insurance system from 2009/2010 for 14 physician groups (approximately 518 million cases), case-related travel times by car between patients' places of residence and physician's practices were estimated at the municipal level. Physicians were reached in less than 30 min in 90.8% of cases for primary care physicians and up to 63% of cases for radiologists. Patients between 18 and under 30 years of age travel longer to get to the doctor than other age groups. The average travel time at the county level systematically differs between urban and rural planning areas. In the case of gynecologists, dermatologists and ophthalmologists, the average journey time decreases with increasing physician density at the county level, but remains approximately constant from a recognisable point of inflection. There is no association between primary care physician density and travel time at the district level. Spatial analyses show physician group-specific patterns of regional concentrations with an increased proportion of cases with very long travel times. Patients' travel times are influenced by supply- and demand-side determinants. Interactions between influential determinants should be analysed in depth to examine the extent to which the time travelled is an expression of regional under- or over-supply rather than an expression of patient preferences.

Towards a Tool for Malaria Supply Chain Management Improvement in Rural Ghana.

PubMed

Carlo, Lorena; Bakken, Suzanne; Mamykina, Lena; Kodie, Richmond; Kanter, Andrew S

2015-01-01

The maintenance of adequate quantities of antimalarial medicines and rapid diagnostic tests (RDTs) at health facilities in rural areas of sub-Saharan Africa is a challenging task because of poor supply chain management. Antimalarial stock-outs in the communities could lead patients (that need to travel long distances to get medications) to remain untreated, develop severe malaria and die. A prototype to improve the management of health commodities in rural Ghana through the visualization of current stock levels and the forecasting of commodities is proposed.

Journal of Air Transportation, Volume 9, No. 1. Volume 9, No. 1

NASA Technical Reports Server (NTRS)

Bowen, Brent D. (Editor)

2004-01-01

The articles in this issue include: 1) Are Four Year Universities Better than Two-Year Colleges at Preparing Students to Pass the FAA Aircraft Mechanic Certification Written Examinations? 2) Assessing Perceived Risk of Consumers in Internet Airline Reservations Services; 3) Perceptions of Communication Training Among Collegiate Flight Educators; 4) Ethics Education in University Aviation Management Programs in the U.S.: Part Three - Qualitative Analysis and Recommendations; 5) Airline Flight Operations Internships Perspectives; 6) Applying Data Mining Techniques to Forecast Number of Airline Passengers in Saudi Arabia (Domestic and International Travels).

Real-time tsunami inundation forecasting and damage mapping towards enhancing tsunami disaster resiliency

NASA Astrophysics Data System (ADS)

Koshimura, S.; Hino, R.; Ohta, Y.; Kobayashi, H.; Musa, A.; Murashima, Y.

2014-12-01

With use of modern computing power and advanced sensor networks, a project is underway to establish a new system of real-time tsunami inundation forecasting, damage estimation and mapping to enhance society's resilience in the aftermath of major tsunami disaster. The system consists of fusion of real-time crustal deformation monitoring/fault model estimation by Ohta et al. (2012), high-performance real-time tsunami propagation/inundation modeling with NEC's vector supercomputer SX-ACE, damage/loss estimation models (Koshimura et al., 2013), and geo-informatics. After a major (near field) earthquake is triggered, the first response of the system is to identify the tsunami source model by applying RAPiD Algorithm (Ohta et al., 2012) to observed RTK-GPS time series at GEONET sites in Japan. As performed in the data obtained during the 2011 Tohoku event, we assume less than 10 minutes as the acquisition time of the source model. Given the tsunami source, the system moves on to running tsunami propagation and inundation model which was optimized on the vector supercomputer SX-ACE to acquire the estimation of time series of tsunami at offshore/coastal tide gauges to determine tsunami travel and arrival time, extent of inundation zone, maximum flow depth distribution. The implemented tsunami numerical model is based on the non-linear shallow-water equations discretized by finite difference method. The merged bathymetry and topography grids are prepared with 10 m resolution to better estimate the tsunami inland penetration. Given the maximum flow depth distribution, the system performs GIS analysis to determine the numbers of exposed population and structures using census data, then estimates the numbers of potential death and damaged structures by applying tsunami fragility curve (Koshimura et al., 2013). Since the tsunami source model is determined, the model is supposed to complete the estimation within 10 minutes. The results are disseminated as mapping products to responders and stakeholders, e.g. national and regional municipalities, to be utilized for their emergency/response activities. In 2014, the system is verified through the case studies of 2011 Tohoku event and potential earthquake scenarios along Nankai Trough with regard to its capability and robustness.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

PubMed Central

Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

PubMed

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

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

Travelers' Health: Yellow Fever

MedlinePlus

... and local rate of virus transmission at the time of travel. Although reported cases of human disease are the ... be receiving yellow fever vaccine for the first time. If travel is unavoidable, the decision to vaccinate travelers aged ≥ ...

An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Towards seasonal forecasting of malaria in India.

PubMed

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

2014-08-10

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

A Scheme for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

DTIC Science & Technology

1984-07-01

D.A. Lowry, 1978: Use of a real - time computer graphics system for diagnosis and forecasting . Preprints, Conf. on Wes. Forecasting and Analysis and...28 Figure 4.2.1. Graph for forecasting the night minimum temperature from observations at 1800-2000 local time . From Zverev (1972...3u 1. 2 much weather is produced by organized systems that translate, and forecast gains were made through use of the concepts of steering

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.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

Strategies in Forecasting Outcomes in Ethical Decision-making: Identifying and Analyzing the Causes of the Problem

PubMed Central

Beeler, Cheryl K.; Antes, Alison L.; Wang, Xiaoqian; Caughron, Jared J.; Thiel, Chase E.; Mumford, Michael D.

2010-01-01

This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of the problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. PMID:20352056

Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-12-01

In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS has a clear skill in predicting the actual probability distribution of sea level, and it outperforms simple "dressed" PF methods. A probability estimate based on the single DF is shown to be inadequate. However, a PF obtained with a prescribed Gaussian distribution and centered on the DF value performs very similarly to the EPS-based PF.

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.

76 FR 61622 - Potential Closing of Morses Line Border Crossing

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-05

... travelers would need to travel to an alternate crossing which could cost them both time and money. CBP does... measured the distance and estimated time for each combination assuming they could not travel through Morses Line. By comparing the distance and travel time for the fastest route to those for the fastest route...

5 CFR 610.123 - Travel on official time.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 5 Administrative Personnel 1 2014-01-01 2014-01-01 false Travel on official time. 610.123 Section... DUTY Weekly and Daily Scheduling of Work Work Schedules § 610.123 Travel on official time. Insofar as practicable travel during nonduty hours shall not be required of an employee. When it is essential that this...

5 CFR 610.123 - Travel on official time.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Travel on official time. 610.123 Section... DUTY Weekly and Daily Scheduling of Work Work Schedules § 610.123 Travel on official time. Insofar as practicable travel during nonduty hours shall not be required of an employee. When it is essential that this...

5 CFR 610.123 - Travel on official time.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 5 Administrative Personnel 1 2011-01-01 2011-01-01 false Travel on official time. 610.123 Section... DUTY Weekly and Daily Scheduling of Work Work Schedules § 610.123 Travel on official time. Insofar as practicable travel during nonduty hours shall not be required of an employee. When it is essential that this...

5 CFR 610.123 - Travel on official time.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 5 Administrative Personnel 1 2013-01-01 2013-01-01 false Travel on official time. 610.123 Section... DUTY Weekly and Daily Scheduling of Work Work Schedules § 610.123 Travel on official time. Insofar as practicable travel during nonduty hours shall not be required of an employee. When it is essential that this...

5 CFR 610.123 - Travel on official time.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 5 Administrative Personnel 1 2012-01-01 2012-01-01 false Travel on official time. 610.123 Section... DUTY Weekly and Daily Scheduling of Work Work Schedules § 610.123 Travel on official time. Insofar as practicable travel during nonduty hours shall not be required of an employee. When it is essential that this...

Ratio-based lengths of intervals to improve fuzzy time series forecasting.

PubMed

Huarng, Kunhuang; Yu, Tiffany Hui-Kuang

2006-04-01

The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

5 CFR 550.1403 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Compensatory Time Off for Travel § 550.1403 Definitions. In this subpart: Accrued compensatory time off means... pay limitations. Compensatory time off means compensatory time off for travel that is credited under... of determining whether travel time is compensable for the purpose of determining overtime pay...

5 CFR 550.1403 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Compensatory Time Off for Travel § 550.1403 Definitions. In this subpart: Accrued compensatory time off means... pay limitations. Compensatory time off means compensatory time off for travel that is credited under... of determining whether travel time is compensable for the purpose of determining overtime pay...

5 CFR 550.1403 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) Compensatory Time Off for Travel § 550.1403 Definitions. In this subpart: Accrued compensatory time off means... pay limitations. Compensatory time off means compensatory time off for travel that is credited under... of determining whether travel time is compensable for the purpose of determining overtime pay...

5 CFR 550.1403 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Compensatory Time Off for Travel § 550.1403 Definitions. In this subpart: Accrued compensatory time off means... pay limitations. Compensatory time off means compensatory time off for travel that is credited under... of determining whether travel time is compensable for the purpose of determining overtime pay...

5 CFR 550.1403 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) Compensatory Time Off for Travel § 550.1403 Definitions. In this subpart: Accrued compensatory time off means... pay limitations. Compensatory time off means compensatory time off for travel that is credited under... of determining whether travel time is compensable for the purpose of determining overtime pay...

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis.

PubMed

Linden, Ariel

2018-05-11

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. ITSA is commonly evaluated using methods which may produce biased results if model assumptions are violated. In this paper, treatment effects are alternatively assessed by using forecasting methods to closely fit the preintervention observations and then forecast the post-intervention trend. A treatment effect may be inferred if the actual post-intervention observations diverge from the forecasts by some specified amount. The forecasting approach is demonstrated using the effect of California's Proposition 99 for reducing cigarette sales. Three forecast models are fit to the preintervention series-linear regression (REG), Holt-Winters (HW) non-seasonal smoothing, and autoregressive moving average (ARIMA)-and forecasts are generated into the post-intervention period. The actual observations are then compared with the forecasts to assess intervention effects. The preintervention data were fit best by HW, followed closely by ARIMA. REG fit the data poorly. The actual post-intervention observations were above the forecasts in HW and ARIMA, suggesting no intervention effect, but below the forecasts in the REG (suggesting a treatment effect), thereby raising doubts about any definitive conclusion of a treatment effect. In a single-group ITSA, treatment effects are likely to be biased if the model is misspecified. Therefore, evaluators should consider using forecast models to accurately fit the preintervention data and generate plausible counterfactual forecasts, thereby improving causal inference of treatment effects in single-group ITSA studies. © 2018 John Wiley & Sons, Ltd.

Short-term Inundation Forecasting for Tsunamis Version 4.0 Brings Forecasting Speed, Accuracy, and Capability Improvements to NOAA's Tsunami Warning Centers

NASA Astrophysics Data System (ADS)

Sterling, K.; Denbo, D. W.; Eble, M. C.

2016-12-01

Short-term Inundation Forecasting for Tsunamis (SIFT) software was developed by NOAA's Pacific Marine Environmental Laboratory (PMEL) for use in tsunami forecasting and has been used by both U.S. Tsunami Warning Centers (TWCs) since 2012, when SIFTv3.1 was operationally accepted. Since then, advancements in research and modeling have resulted in several new features being incorporated into SIFT forecasting. Following the priorities and needs of the TWCs, upgrades to SIFT forecasting were implemented into SIFTv4.0, scheduled to become operational in October 2016. Because every minute counts in the early warning process, two major time saving features were implemented in SIFT 4.0. To increase processing speeds and generate high-resolution flooding forecasts more quickly, the tsunami propagation and inundation codes were modified to run on Graphics Processing Units (GPUs). To reduce time demand on duty scientists during an event, an automated DART inversion (or fitting) process was implemented. To increase forecasting accuracy, the forecasted amplitudes and inundations were adjusted to include dynamic tidal oscillations, thereby reducing the over-estimates of flooding common in SIFTv3.1 due to the static tide stage conservatively set at Mean High Water. Further improvements to forecasts were gained through the assimilation of additional real-time observations. Cabled array measurements from Bottom Pressure Recorders (BPRs) in the Oceans Canada NEPTUNE network are now available to SIFT for use in the inversion process. To better meet the needs of harbor masters and emergency managers, SIFTv4.0 adds a tsunami currents graphical product to the suite of disseminated forecast results. When delivered, these new features in SIFTv4.0 will improve the operational tsunami forecasting speed, accuracy, and capabilities at NOAA's Tsunami Warning Centers.

Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015

NASA Astrophysics Data System (ADS)

Min, Young-Mi; Kryjov, Vladimir N.; Oh, Sang Myeong; Lee, Hyun-Ju

2017-12-01

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008-2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012-2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008-2015 is similar to that of hindcast.

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.

41 CFR 301-11.10 - Am I required to record departure/arrival dates and times on my travel claim?

Code of Federal Regulations, 2010 CFR

2010-07-01

... departure/arrival dates and times on my travel claim? 301-11.10 Section 301-11.10 Public Contracts and... dates and times on my travel claim? You must record the date of departure from, and arrival at, the... visited. You do not have to record departure/arrival times, but you must annotate your travel claim when...

41 CFR 301-11.10 - Am I required to record departure/arrival dates and times on my travel claim?

Code of Federal Regulations, 2013 CFR

2013-07-01

... departure/arrival dates and times on my travel claim? 301-11.10 Section 301-11.10 Public Contracts and... dates and times on my travel claim? You must record the date of departure from, and arrival at, the... visited. You do not have to record departure/arrival times, but you must annotate your travel claim when...

41 CFR 301-11.10 - Am I required to record departure/arrival dates and times on my travel claim?

Code of Federal Regulations, 2012 CFR

2012-07-01

... departure/arrival dates and times on my travel claim? 301-11.10 Section 301-11.10 Public Contracts and... dates and times on my travel claim? You must record the date of departure from, and arrival at, the... visited. You do not have to record departure/arrival times, but you must annotate your travel claim when...

41 CFR 301-11.10 - Am I required to record departure/arrival dates and times on my travel claim?

Code of Federal Regulations, 2011 CFR

2011-07-01

... departure/arrival dates and times on my travel claim? 301-11.10 Section 301-11.10 Public Contracts and... dates and times on my travel claim? You must record the date of departure from, and arrival at, the... visited. You do not have to record departure/arrival times, but you must annotate your travel claim when...

41 CFR 301-11.10 - Am I required to record departure/arrival dates and times on my travel claim?

Code of Federal Regulations, 2014 CFR

2014-07-01

... departure/arrival dates and times on my travel claim? 301-11.10 Section 301-11.10 Public Contracts and... dates and times on my travel claim? You must record the date of departure from, and arrival at, the... visited. You do not have to record departure/arrival times, but you must annotate your travel claim when...

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.

Development of an aerosol assimilation/forecasting system with Himawari-8 aerosol products

NASA Astrophysics Data System (ADS)

Maki, T.; Yumimoto, K.; Tanaka, T. Y.; Yoshida, M.; Kikuchi, M.; Nagao, T. M.; Murakami, H.; Ogi, A.; Sekiyama, T. T.

2016-12-01

A new generation geostationary meteorological satellite (GMS), Himawari-8, was launched on 7 October 2014 and became operational on 7 July 2015. Himawari-8 is equipped with more advanced multispectral imager (Advanced Himawari Imager; AHI) ahead of other planned GMSs (e.g., GEOS-R). The AHI has 16 observational bands including three visible lights (i.e. RGB) with high spatial (0.5-2 km) and temporal (every 10 minutes full-disk images) resolutions, and provides about 50 times more data than previous GMSs. It is attractive characteristics for aerosol study that the visible and near-infrared observational bands allow us to obtain full-disk maps of aerosol optical properties (i.e., aerosol optical thickness (AOT) and ångström exponent) with unprecedented temporal resolution. Meteorological Research Institute (MRI)/JMA and Japan Aerospace Exploration Agency (JAXA) have been developing an aerosol assimilation/forecasting system with a global aerosol transport model (MASINGAR mk-2), 2 dimensional variational (2D-Var) method, and the Himawari-8 AOTs. Forecasting results are quantitatively validated by AOTs measured by AERONET and PM2.5 concentrations obtained by in-situ stations. Figure 1 shows model-predicted and satellite-observed AOTs during the 2016 Siberian wildfire. Upper and lower panels exhibit maps of AOT at analysis time (0000 UTC on May 18, 2016) and 27-hour forecast time (03 UTC on May 19, 2016), respectively. The 27-hour forecasted AOT starting with the analyzed initial condition (Figure 1f) successfully predicts heavy smokes covering the northern part of Japan, which forecast without assimilation (Figure 1e) failed to reproduces. Figure 1: Horizontal distribution of observed and forecasted AOTs at 0000 UTC 18 May, 2016 (analysis time; upper panels) and 0300 UTC 19 May, 2016 (18-h forecast from the analysis time; lower panel). (a, d) observed AOT from Himawari-8, (b, e) forecasted AOT without assimilation, and (c, f) forecast AOT with assimilation.

ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

EPA Science Inventory

The real-time forecasts of ozone (O₃) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

Value of travel time.

DOT National Transportation Integrated Search

2016-08-01

Knowingly or not, people generally place economic value on their time. Wage workers are paid a rate per hour, and service providers may charge per hour of their time. In the transportation realm, travelers place a value on their travel time and have ...

Tsunami Forecast Progress Five Years After Indonesian Disaster

NASA Astrophysics Data System (ADS)

Titov, Vasily V.; Bernard, Eddie N.; Weinstein, Stuart A.; Kanoglu, Utku; Synolakis, Costas E.

2010-05-01

Almost five years after the 26 December 2004 Indian Ocean tragedy, tsunami warnings are finally benefiting from decades of research toward effective model-based forecasts. Since the 2004 tsunami, two seminal advances have been (i) deep-ocean tsunami measurements with tsunameters and (ii) their use in accurately forecasting tsunamis after the tsunami has been generated. Using direct measurements of deep-ocean tsunami heights, assimilated into numerical models for specific locations, greatly improves the real-time forecast accuracy over earthquake-derived magnitude estimates of tsunami impact. Since 2003, this method has been used to forecast tsunamis at specific harbors for different events in the Pacific and Indian Oceans. Recent tsunamis illustrated how this technology is being adopted in global tsunami warning operations. The U.S. forecasting system was used by both research and operations to evaluate the tsunami hazard. Tests demonstrated the effectiveness of operational tsunami forecasting using real-time deep-ocean data assimilated into forecast models. Several examples also showed potential of distributed forecast tools. With IOC and USAID funding, NOAA researchers at PMEL developed the Community Model Interface for Tsunami (ComMIT) tool and distributed it through extensive capacity-building sessions in the Indian Ocean. Over hundred scientists have been trained in tsunami inundation mapping, leading to the first generation of inundation models for many Indian Ocean shorelines. These same inundation models can also be used for real-time tsunami forecasts as was demonstrated during several events. Contact Information Vasily V. Titov, Seattle, Washington, USA, 98115

A stochastic HMM-based forecasting model for fuzzy time series.

PubMed

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

The impact of travel time on geographic distribution of dialysis patients.

PubMed

Kashima, Saori; Matsumoto, Masatoshi; Ogawa, Takahiko; Eboshida, Akira; Takeuchi, Keisuke

2012-01-01

The geographic disparity of prevalence rates among dialysis patients is unclear. We evaluate the association between travel time to dialysis facilities and prevalence rates of dialysis patients living in 1,867 census areas of Hiroshima, Japan. Furthermore, we study the effects of geographic features (mainland or island) on the prevalence rates and assess if these effects modify the association between travel time and prevalence. The study subjects were all 7,374 people that were certified as the "renal disabled" by local governments in 2011. The travel time from each patient to the nearest available dialysis facility was calculated by incorporating both travel time and the capacity of all 98 facilities. The effect of travel time on the age- and sex-adjusted standard prevalence rate (SPR) and 95% confidence intervals (CIs) at each census area was evaluated in two-level Poisson regression models with 1,867 census areas (level 1) nested within 35 towns or cities (level 2). The results were adjusted for area-based parameters of socioeconomic status, urbanity, and land type. Furthermore, the SPR of dialysis patients was calculated in each specific subgroup of population for travel time, land type, and combination of land type and travel time. In the regression analysis, SPR decreased by 5.2% (95% CI: -7.9--2.3) per 10-min increase in travel time even after adjusting for potential confounders. The effect of travel time on prevalence was different in the mainland and island groups. There was no travel time-dependent SPR disparity on the islands. The SPR among remote residents (>30 min from facilities) in the mainland was lower (0.77, 95% CI: 0.71-0.85) than that of closer residents (≤ 30 min; 0.95, 95% CI: 0.92-0.97). The prevalence of dialysis patients was lower among remote residents. Geographic difficulties for commuting seem to decrease the prevalence rate.

Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

NASA Astrophysics Data System (ADS)

Suharsono, Agus; Suhartono, Masyitha, Aulia; Anuravega, Arum

2015-12-01

The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

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.

Observations and modelling of a meteotsunami across the English Channel on 23rd June 2016

NASA Astrophysics Data System (ADS)

Williams, David; Horsburgh, Kevin; Schultz, David; Hughes, Chris

2017-04-01

Meteotsunami are shallow water waves in the tsunami frequency band, which are generated by sub-mesoscale pressure and wind velocity fluctuations. Whilst documented meteotsunami on the north-western European shelf have not been hazardous, around the world they have caused fatalities and significant economic losses. Previous observational studies suggest that across Western Europe strongly convective storms are meteotsunami-generating. We give evidence for a meteotsunami on 23rd June 2016 along the northern coastline of France, following strongly convective storms. This includes 1-minute temporal resolution tide gauge data, in situ pressure and wind velocities, and infrared satellite images. With an estimated wave height of 0.8 m at Boulogne, this meteotsunami is particularly large compared to previous observations in Western Europe. The tsunami travel times have been estimated using the wavefront method, showing that a single, instantaneous source for the waves is highly unlikely. Using the ocean model Telemac2D, idealised models of pressure and wind have been used to simulate the meteotsunami. The model supports that across the English Channel thunderstorms with north-easterly tracks, moving at the shallow water wave speed, can generate wave amplification through Proudman resonance. The Weather Research and Forecasting (WRF) model has been used to produce numerically simulated thunderstorms, which have been used to force the Telemac2D ocean model with idealised bathymetries. The WRF-Telemac2D model results also support meteotsunami generation by thunderstorms. To the author's knowledge this is the first time a thunderstorm simulation has been used to produce a meteotsunami-like wave, and indicates that non-hydrostatic, convective atmospheric processes are important for meteotsunami generation. This suggests that with combined high resolution observations and modelling, a meteotsunami forecasting system may become possible in Western Europe.

Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Bellier, Joseph; Bontron, Guillaume; Zin, Isabella

2017-12-01

Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

NASA Astrophysics Data System (ADS)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

Simple Kinematic Pathway Approach (KPA) to Catchment-scale Travel Time and Water Age Distributions

NASA Astrophysics Data System (ADS)

Soltani, S. S.; Cvetkovic, V.; Destouni, G.

2017-12-01

The distribution of catchment-scale water travel times is strongly influenced by morphological dispersion and is partitioned between hillslope and larger, regional scales. We explore whether hillslope travel times are predictable using a simple semi-analytical "kinematic pathway approach" (KPA) that accounts for dispersion on two levels of morphological and macro-dispersion. The study gives new insights to shallow (hillslope) and deep (regional) groundwater travel times by comparing numerical simulations of travel time distributions, referred to as "dynamic model", with corresponding KPA computations for three different real catchment case studies in Sweden. KPA uses basic structural and hydrological data to compute transient water travel time (forward mode) and age (backward mode) distributions at the catchment outlet. Longitudinal and morphological dispersion components are reflected in KPA computations by assuming an effective Peclet number and topographically driven pathway length distributions, respectively. Numerical simulations of advective travel times are obtained by means of particle tracking using the fully-integrated flow model MIKE SHE. The comparison of computed cumulative distribution functions of travel times shows significant influence of morphological dispersion and groundwater recharge rate on the compatibility of the "kinematic pathway" and "dynamic" models. Zones of high recharge rate in "dynamic" models are associated with topographically driven groundwater flow paths to adjacent discharge zones, e.g. rivers and lakes, through relatively shallow pathway compartments. These zones exhibit more compatible behavior between "dynamic" and "kinematic pathway" models than the zones of low recharge rate. Interestingly, the travel time distributions of hillslope compartments remain almost unchanged with increasing recharge rates in the "dynamic" models. This robust "dynamic" model behavior suggests that flow path lengths and travel times in shallow hillslope compartments are controlled by topography, and therefore application and further development of the simple "kinematic pathway" approach is promising for their modeling.

The influence of travel time on breast cancer characteristics, receipt of primary therapy, and surveillance mammography.

PubMed

Onega, Tracy; Cook, Andrea; Kirlin, Beth; Shi, Xun; Alford-Teaster, Jennifer; Tuzzio, Leah; Buist, Diana S M

2011-08-01

Travel time has been shown to influence some aspects of cancer characteristics at diagnosis and care for women with breast cancer, but important gaps remain in our understanding of its impact. We examined the influence of travel time to the nearest radiology facility on breast cancer characteristics, treatment, and surveillance for women with early-stage invasive breast cancer. We included 1,012 women with invasive breast cancer (stages I and II) who had access to care within an integrated health care delivery system in western Washington State. The travel times to the nearest radiology facility were calculated for all the U.S. Census blocks within the study area and assigned to women based on residence at diagnosis. We collected cancer characteristics, primary and adjuvant therapies, and surveillance mammography for at least 2.5 years post diagnosis and used multivariable analyses to test the associations of travel time. The majority of women (68.6%) lived within 20 min of the nearest radiology facility, had stage I disease (72.7%), received breast conserving therapy (68.7%), and had annual surveillance mammography the first 2 years after treatment (73.7%). The travel time was not significantly associated with the stage or surveillance mammography after adjusting for covariates. Primary therapy was significantly related to travel time, with greater travel time (>30 min vs. ≤ 10 min) associated with a higher likelihood of mastectomy compared to breast conserving surgery (RR = 1.53; 95% CI, 1.16-2.01). The travel time was not associated with the stage at diagnosis or surveillance mammography receipt. The travel time does seem to influence the type of primary therapy among women with breast cancer, suggesting that women may prefer low frequency services, such as mastectomy, if geographic access to a radiology facility is limited.

A Conceptual Model of the Severe-Storm Environment for Inclusion into Air Weather Service Severe-Storm Analysis and Forecast Procedures.

DTIC Science & Technology

1984-11-16

thunderstorm forecasting , Bull. Am. Meteorol. Soc. 34:250-252. 19. Galway , J.G. (1956) The lifted index as a prediction of latent instability, Bull...downwind, which are geographically related and can be traced through time by a forecaster . In fact, a typical Great Plains severe-storm situation has...weather station setting, only one sounding can be plotted and anal- yzed because of time constraints. Appendix C contains two single-station forecast

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables in the North Central USA

NASA Astrophysics Data System (ADS)

Hoss, F.; Fischbeck, P. S.

2014-10-01

This study further develops the method of quantile regression (QR) to predict exceedance probabilities of flood stages by post-processing forecasts. Using data from the 82 river gages, for which the National Weather Service's North Central River Forecast Center issues forecasts daily, this is the first QR application to US American river gages. Archived forecasts for lead times up to six days from 2001-2013 were analyzed. Earlier implementations of QR used the forecast itself as the only independent variable (Weerts et al., 2011; López López et al., 2014). This study adds the rise rate of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago to the QR model. Including those four variables significantly improved the forecasts, as measured by the Brier Skill Score (BSS). Mainly, the resolution increases, as the original QR implementation already delivered high reliability. Combining the forecast with the other four variables results in much less favorable BSSs. Lastly, the forecast performance does not depend on the size of the training dataset, but on the year, the river gage, lead time and event threshold that are being forecast. We find that each event threshold requires a separate model configuration or at least calibration.

77 FR 69920 - Notice of Availability for Public Review of the Draft Tier 1 Environmental Impact Statement for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-21

... would provide an alternative to automobile, bus, and air travel by decreasing travel times, increasing... and need criteria by: decreasing travel times; increasing frequency of service; improving service... current and future demand for travel in the Study area. The need stems from travel demand and increasing...

Visiting Astronomers Travel Guide | CTIO

Science.gov Websites

please advise Ximena Herreros at the time that you initiate travel plans, if your stay in Chile will , well in advance of their travel time, regarding current visa requirements for Chile. back to top Visiting Astronomers Travel Guide Director's Discretionary (DD) Time CTIO 2016 Ephemeris ToO Policy CTIO

11 CFR 100.93 - Travel by aircraft or other means of transportation.

Code of Federal Regulations, 2014 CFR

2014-01-01

... traveled or within seven (7) calendar days thereof. The payment rate must be determined by the time the... travel on an aircraft that is owned or leased under a shared-ownership or other time-share arrangement... travel on an aircraft that is owned or leased under a shared-ownership or other time-share arrangement...

29 CFR 785.39 - Travel away from home community.

Code of Federal Regulations, 2011 CFR

2011-07-01

... employee's workday. The employee is simply substituting travel for other duties. The time is not only hours... travel time during these hours is worktime on Saturday and Sunday as well as on the other days. Regular... that time spent in travel away from home outside of regular working hours as a passenger on an airplane...

29 CFR 785.39 - Travel away from home community.

Code of Federal Regulations, 2014 CFR

2014-07-01

... employee's workday. The employee is simply substituting travel for other duties. The time is not only hours... travel time during these hours is worktime on Saturday and Sunday as well as on the other days. Regular... that time spent in travel away from home outside of regular working hours as a passenger on an airplane...

29 CFR 785.39 - Travel away from home community.

Code of Federal Regulations, 2013 CFR

2013-07-01

... employee's workday. The employee is simply substituting travel for other duties. The time is not only hours... travel time during these hours is worktime on Saturday and Sunday as well as on the other days. Regular... that time spent in travel away from home outside of regular working hours as a passenger on an airplane...

11 CFR 100.93 - Travel by airplane or other means of transportation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... committee on behalf of which the travel is conducted pays the service provider, within the required time... traveled or within seven (7) calendar days thereof. The payment rate must be determined by the time the... travel on an aircraft that is owned or leased under a shared-ownership or other time-share arrangement...

29 CFR 785.39 - Travel away from home community.

Code of Federal Regulations, 2010 CFR

2010-07-01

... employee's workday. The employee is simply substituting travel for other duties. The time is not only hours... travel time during these hours is worktime on Saturday and Sunday as well as on the other days. Regular... that time spent in travel away from home outside of regular working hours as a passenger on an airplane...

29 CFR 785.39 - Travel away from home community.

Code of Federal Regulations, 2012 CFR

2012-07-01

... employee's workday. The employee is simply substituting travel for other duties. The time is not only hours... travel time during these hours is worktime on Saturday and Sunday as well as on the other days. Regular... that time spent in travel away from home outside of regular working hours as a passenger on an airplane...

Wireless data collection system for real-time arterial travel time estimates.

DOT National Transportation Integrated Search

2011-03-01

This project pursued several objectives conducive to the implementation and testing of a Bluetooth (BT) based system to collect travel time data, including the deployment of a BT-based travel time data collection system to perform comprehensive testi...

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

A multi-source data assimilation framework for flood forecasting: Accounting for runoff routing lags

NASA Astrophysics Data System (ADS)

Meng, S.; Xie, X.

2015-12-01

In the flood forecasting practice, model performance is usually degraded due to various sources of uncertainties, including the uncertainties from input data, model parameters, model structures and output observations. Data assimilation is a useful methodology to reduce uncertainties in flood forecasting. For the short-term flood forecasting, an accurate estimation of initial soil moisture condition will improve the forecasting performance. Considering the time delay of runoff routing is another important effect for the forecasting performance. Moreover, the observation data of hydrological variables (including ground observations and satellite observations) are becoming easily available. The reliability of the short-term flood forecasting could be improved by assimilating multi-source data. The objective of this study is to develop a multi-source data assimilation framework for real-time flood forecasting. In this data assimilation framework, the first step is assimilating the up-layer soil moisture observations to update model state and generated runoff based on the ensemble Kalman filter (EnKF) method, and the second step is assimilating discharge observations to update model state and runoff within a fixed time window based on the ensemble Kalman smoother (EnKS) method. This smoothing technique is adopted to account for the runoff routing lag. Using such assimilation framework of the soil moisture and discharge observations is expected to improve the flood forecasting. In order to distinguish the effectiveness of this dual-step assimilation framework, we designed a dual-EnKF algorithm in which the observed soil moisture and discharge are assimilated separately without accounting for the runoff routing lag. The results show that the multi-source data assimilation framework can effectively improve flood forecasting, especially when the runoff routing has a distinct time lag. Thus, this new data assimilation framework holds a great potential in operational flood forecasting by merging observations from ground measurement and remote sensing retrivals.

Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

NASA Astrophysics Data System (ADS)

Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

2017-04-01

This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.

Implementation and testing of the travel time prediction system (TIPS) : final report, May 2001.

DOT National Transportation Integrated Search

2001-05-01

The Travel Time Prediction System (TIPS) is a portable automated system for predicting and displaying travel time for motorists in advance of and through freeway construction work zones, on a real-time basis. It collects real-time traffic flow data u...

Implementation and testing of the travel time prediction system (TIPS) : executive summary, May 2001.

DOT National Transportation Integrated Search

2001-05-01

The Travel Time Prediction System (TIPS) is a portable automated system for predicting and displaying travel time for motorists in advance of and through freeway construction work zones, on a real-time basis. It collects real-time traffic flow data u...

A hydro-mechanical framework for early warning of rainfall-induced landslides (Invited)

NASA Astrophysics Data System (ADS)

Godt, J.; Lu, N.; Baum, R. L.

2013-12-01

Landslide early warning requires an estimate of the location, timing, and magnitude of initial movement, and the change in volume and momentum of material as it travels down a slope or channel. In many locations advance assessment of landslide location, volume, and momentum is possible, but prediction of landslide timing entails understanding the evolution of rainfall and soil-water conditions, and consequent effects on slope stability in real time. Existing schemes for landslide prediction generally rely on empirical relations between landslide occurrence and rainfall amount and duration, however, these relations do not account for temporally variable rainfall nor the variably saturated processes that control the hydro-mechanical response of hillside materials to rainfall. Although limited by the resolution and accuracy of rainfall forecasts and now-casts in complex terrain and by the inherent difficulty in adequately characterizing subsurface materials, physics-based models provide a general means to quantitatively link rainfall and landslide occurrence. To obtain quantitative estimates of landslide potential from physics-based models using observed or forecasted rainfall requires explicit consideration of the changes in effective stress that result from changes in soil moisture and pore-water pressures. The physics that control soil-water conditions are transient, nonlinear, hysteretic, and dependent on material composition and history. In order to examine the physical processes that control infiltration and effective stress in variably saturated materials, we present field and laboratory results describing intrinsic relations among soil water and mechanical properties of hillside materials. At the REV (representative elementary volume) scale, the interaction between pore fluids and solid grains can be effectively described by the relation between soil suction, soil water content, hydraulic conductivity, and suction stress. We show that these relations can be obtained independently from outflow, shear strength, and deformation tests for a wide range of earth materials. We then compare laboratory results with measurements of pore pressure and moisture content from landslide-prone settings and demonstrate that laboratory results obtained for hillside materials are representative of field conditions. These fundamental relations provide a basis to combine observed or forecasted rainfall with in-situ measurements of soil water conditions using hydro-mechanical models that simulate transient variably saturated flow and slope stability. We conclude that early warning using an approach in which in-situ observations are used to establish initial conditions for hydro-mechanical models is feasible in areas of high landslide risk where laboratory characterization of materials is practical and accurate rainfall information can be obtained. Analogous to weather and climate forecasting, such models could then be applied in an ensemble fashion to obtain quantitative estimates of landslide probability and error. Application to broader regions likely awaits breakthroughs in the development of remotely sensed proxies of soil properties and subsurface moisture conditions.

Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

NASA Astrophysics Data System (ADS)

Beria, H.; Nanda, T., Sr.; Chatterjee, C.

2015-12-01

High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.

PubMed

Ben Taieb, Souhaib; Atiya, Amir F

2016-01-01

Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead time series model or directly by estimating a separate model for each forecast horizon. In addition, there are other strategies; some of them combine aspects of both aforementioned concepts. In this paper, we present a comprehensive investigation into the bias and variance behavior of multistep-ahead forecasting strategies. We provide a detailed review of the different multistep-ahead strategies. Subsequently, we perform a theoretical study that derives the bias and variance for a number of forecasting strategies. Finally, we conduct a Monte Carlo experimental study that compares and evaluates the bias and variance performance of the different strategies. From the theoretical and the simulation studies, we analyze the effect of different factors, such as the forecast horizon and the time series length, on the bias and variance components, and on the different multistep-ahead strategies. Several lessons are learned, and recommendations are given concerning the advantages, disadvantages, and best conditions of use of each strategy.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

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

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

NASA Astrophysics Data System (ADS)

Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Ha, Yao

2018-04-01

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.