Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method

PubMed Central

Li, Nan; Lee, Ronald

2005-01-01

Mortality patterns and trajectories in closely related populations are likely to be similar in some respects, and differences are unlikely to increase in the long run. It should therefore be possible to improve the mortality forecasts for individual countries by taking into account the patterns in a larger group. Using the Human Mortality Database, we apply the Lee-Carter model to a group of populations, allowing each its own age pattern and level of mortality but imposing shared rates of change by age. Our forecasts also allow divergent patterns to continue for a while before tapering off. We forecast greater longevity gains for the US and lesser ones for Japan relative to separate forecasts. PMID:16235614

The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada

NASA Astrophysics Data System (ADS)

Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René

2017-11-01

Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada's (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Québec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

A High Resolution Tropical Cyclone Power Outage Forecasting Model for the Continental United States

NASA Astrophysics Data System (ADS)

Pino, J. V.; Quiring, S. M.; Guikema, S.; Shashaani, S.; Linger, S.; Backhaus, S.

2017-12-01

Tropical cyclones cause extensive damage to the power infrastructure system throughout the United States. This damage can leave millions without power for extended periods of time, as most recently seen with Hurricane Matthew (2016). Accurate and timely prediction of power outages are essential for utility companies, emergency management agencies, and governmental organizations. Here we present a high-resolution (250 m x 250 m) hurricane power outage model for the United States. The model uses only publicly-available data to make predictions. It uses forecasts of storm variables such as maximum 3-second wind gust, duration of strong winds > 20 m s-2, soil moisture, and precipitation. It also incorporates static environmental variables such as elevation characteristics, land cover type, population density, tree species data, and root zone depth. A web tool was established for use by the Department of Energy (DOE) so that the model can be used for real-time outage forecasting or for synthetic tropical cyclones as an exercise in emergency management. This web tool provides DOE decision-makers with high impact analytic results and products that can be disseminated to federal, local, and state agencies. The results then aid utility companies in their pre- and post-storm activities, thus decreasing restoration times and lowering costs.

Bridge Frost Prediction by Heat and Mass Transfer Methods

NASA Astrophysics Data System (ADS)

Greenfield, Tina M.; Takle, Eugene S.

2006-03-01

Frost on roadways and bridges can present hazardous conditions to motorists, particularly when it occurs in patches or on bridges when adjacent roadways are clear of frost. To minimize materials costs, vehicle corrosion, and negative environmental impacts, frost-suppression chemicals should be applied only when, where, and in the appropriate amounts needed to maintain roadways in a safe condition for motorists. Accurate forecasts of frost onset times, frost intensity, and frost disappearance (e.g., melting or sublimation) are needed to help roadway maintenance personnel decide when, where, and how much frost-suppression chemical to use. A finite-difference algorithm (BridgeT) has been developed that simulates vertical heat transfer in a bridge based on evolving meteorological conditions at its top and bottom as supplied by a weather forecast model. BridgeT simulates bridge temperatures at numerous points within the bridge (including its upper and lower surface) at each time step of the weather forecast model and calculates volume per unit area (i.e., depth) of deposited, melted, or sublimed frost. This model produces forecasts of bridge surface temperature, frost depth, and bridge condition (i.e., dry, wet, icy/snowy). Bridge frost predictions and bridge surface temperature are compared with observed and measured values to assess BridgeT's skill in forecasting bridge frost and associated conditions.

Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, S. R.

2010-01-01

As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.

Parametrisation of initial conditions for seasonal stream flow forecasting in the Swiss Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2016-04-01

Current climate forecast models show - to the best of our knowledge - low skill in forecasting climate variability in Central Europe at seasonal lead times. When it comes to seasonal stream flow forecasting, initial conditions thus play an important role. Here, initial conditions refer to the catchments moisture at the date of forecast, i.e. snow depth, stream flow and lake level, soil moisture content, and groundwater level. The parametrisation of these initial conditions can take place at various spatial and temporal scales. Examples are the grid size of a distributed model or the time aggregation of predictors in statistical models. Therefore, the present study aims to investigate the extent to which the parametrisation of initial conditions at different spatial scales leads to differences in forecast errors. To do so, we conduct a forecast experiment for the Swiss Rhine at Basel, which covers parts of Germany, Austria, and Switzerland and is southerly bounded by the Alps. Seasonal mean stream flow is defined for the time aggregation of 30, 60, and 90 days and forecasted at 24 dates within the calendar year, i.e. at the 1st and 16th day of each month. A regression model is employed due to the various anthropogenic effects on the basins hydrology, which often are not quantifiable but might be grasped by a simple black box model. Furthermore, the pool of candidate predictors consists of antecedent temperature, precipitation, and stream flow only. This pragmatic approach follows the fact that observations of variables relevant for hydrological storages are either scarce in space or time (soil moisture, groundwater level), restricted to certain seasons (snow depth), or regions (lake levels, snow depth). For a systematic evaluation, we therefore focus on the comprehensive archives of meteorological observations and reanalyses to estimate the initial conditions via climate variability prior to the date of forecast. The experiment itself is based on four different approaches, whose differences in model skill were estimated within a rigorous cross-validation framework for the period 1982-2013: The predictands are regressed on antecedent temperature, precipitation, and stream flow. Here, temperature and precipitation constitute basin averages out of the E-OBS gridded data set. As in 1., but temperature and precipitation are used at the E-OBS grid scale (0.25 degree in longitude and latitude) without spatial averaging. As in 1., but the regression model is applied to 66 gauged subcatchments of the Rhine basin. Forecasts for these subcatchments are then simply summed and upscaled to the area of the Rhine basin. As in 3., but the forecasts at the subcatchment scale are additionally weighted in terms of hydrological representativeness of the corresponding subcatchment.

Fluid injection and induced seismicity

NASA Astrophysics Data System (ADS)

Kendall, Michael; Verdon, James

2016-04-01

The link between fluid injection, or extraction, and induced seismicity has been observed in reservoirs for many decades. In fact spatial mapping of low magnitude events is routinely used to estimate a stimulated reservoir volume. However, the link between subsurface fluid injection and larger felt seismicity is less clear and has attracted recent interest with a dramatic increase in earthquakes associated with the disposal of oilfield waste fluids. In a few cases, hydraulic fracturing has also been linked to induced seismicity. Much can be learned from past case-studies of induced seismicity so that we can better understand the risks posed. Here we examine 12 case examples and consider in particular controls on maximum event size, lateral event distributions, and event depths. Our results suggest that injection volume is a better control on maximum magnitude than past, natural seismicity in a region. This might, however, simply reflect the lack of baseline monitoring and/or long-term seismic records in certain regions. To address this in the UK, the British Geological Survey is leading the deployment of monitoring arrays in prospective shale gas areas in Lancashire and Yorkshire. In most cases, seismicity is generally located in close vicinity to the injection site. However, in some cases, the nearest events are up to 5km from the injection point. This gives an indication of the minimum radius of influence of such fluid injection projects. The most distant events are never more than 20km from the injection point, perhaps implying a maximum radius of influence. Some events are located in the target reservoir, but most occur below the injection depth. In fact, most events lie in the crystalline basement underlying the sedimentary rocks. This suggests that induced seismicity may not pose a leakage risk for fluid migration back to the surface, as it does not impact caprock integrity. A useful application for microseismic data is to try and forecast induced seismicity during injection, with the aim of mitigating large induced events before they happen. Microseismic event population statistics can be used to make forecasts about the future maximum event magnitude as the injection program continues. By making such forecasts, mitigating actions may be possible if forecast maximum magnitudes exceed a predefined limit.

Disaster loss and social media: Can online information increase flood resilience?

NASA Astrophysics Data System (ADS)

Allaire, Maura C.

2016-09-01

When confronted with natural disasters, individuals around the world increasingly use online resources to become informed of forecasted conditions and advisable actions. This study tests the effectiveness of online information and social media in enabling households to reduce disaster losses. The 2011 Bangkok flood is utilized as a case study since it was one of the first major disasters to affect a substantial population connected to social media. The role of online information is investigated with a mixed methods approach. Both quantitative (propensity score matching) and qualitative (in-depth interviews) techniques are employed. The study relies on two data sources—survey responses from 469 Bangkok households and in-depth interviews with internet users who were a subset of the survey participants. Propensity score matching indicates that social media enabled households to reduce flood losses by an average of 37% (USD 3708 per household), using a nearest neighbor estimator. This reduction is substantial when considering that household flood losses for the matched sample averaged USD 8278. Social media offered information not available from other sources, such as localized and nearly real-time updates of flood location and depth. With this knowledge, households could move belongings to higher ground before floodwaters arrived. These findings suggest that utilizing social media users as sensors could better inform populations during disasters. Overall, the study reveals that online information can enable effective disaster preparedness and reduce losses.

Disaster Loss and Social Media: Can Online Information Increase Flood Resilience?

NASA Astrophysics Data System (ADS)

Allaire, M.

2016-12-01

When confronted with natural disasters, individuals around the world increasingly use online resources to become informed of forecasted conditions and advisable actions. This study tests the effectiveness of online information and social media in enabling households to reduce disaster losses. The 2011 Bangkok flood is utilized as a case study since it was one of the first major disasters to affect a substantial population connected to social media. The role of online information is investigated with a mixed methods approach. Both quantitative (propensity score matching) and qualitative (in-depth interviews) techniques are employed. The study relies on two data sources - survey responses from 469 Bangkok households and in-depth interviews with twenty-three internet users who are a subset of the survey participants. Propensity score matching indicates that social media enabled households to reduce flood losses by an average of 37% (USD 3,708), using a nearest neighbor estimator. This reduction is massive when considering that total flood losses for the full sample averaged USD 4,903. Social media offered information not available from other sources, such as localized and nearly real-time updates of flood location and depth. With this knowledge, households could move belongings to higher ground before floodwaters arrived. These findings suggest that utilizing social media users as sensors could better inform populations during disasters. Overall, the study reveals that online information can enable effective disaster preparedness and reduce losses.

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

ERIC Educational Resources Information Center

Loyens, Kim; Maesschalck, Jeroen; Bouckaert, Geert

2011-01-01

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

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.

A Local Forecast of Land Surface Wetness Conditions, Drought, and St. Louis Encephalitis Virus Transmission Derived from Seasonal Climate Predictions

NASA Astrophysics Data System (ADS)

Shaman, J.; Stieglitz, M.; Zebiak, S.; Cane, M.; Day, J. F.

2002-12-01

We present an ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute (IRI) for Climate Prediction. Three- month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at a Florida and New York site. Forecast skill was assessed for mean area modeled water table depth (WTD), i.e. near surface soil wetness conditions, and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site but not at the New York site. At the Florida site, persistence of hydrologic conditions and local skill of the IRI seasonal forecast contributed to the local hydrologic forecast skill. This forecast will permit probabilistic prediction of future hydrologic conditions. At the Florida site, we have also quantified the link between modeled WTD (i.e. drought) and the amplification and transmission of St. Louis Encephalitis virus (SLEV). We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission associated with human clinical cases. We then combine the seasonal forecasts of local, modeled WTD with this empirical relationship and produce retrospective probabilistic seasonal forecasts of epidemic SLEV transmission in Florida. Epidemic SLEV transmission forecast skill is demonstrated. These findings will permit real-time forecast of drought and resultant SLEV transmission in Florida.

Air travel forecasting : 1965-1975

DOT National Transportation Integrated Search

1957-01-01

The forecast presented herein illustrates methods developed by The Port of New York Authority for measuring the market for travel by application of national survey findings to the census : of population and national population projections furnished b...

Assessing and forecasting population health: integrating knowledge and beliefs in a comprehensive framework.

PubMed

Van Meijgaard, Jeroen; Fielding, Jonathan E; Kominski, Gerald F

2009-01-01

A comprehensive population health-forecasting model has the potential to interject new and valuable information about the future health status of the population based on current conditions, socioeconomic and demographic trends, and potential changes in policies and programs. Our Health Forecasting Model uses a continuous-time microsimulation framework to simulate individuals' lifetime histories by using birth, risk exposures, disease incidence, and death rates to mark changes in the state of the individual. The model generates a reference forecast of future health in California, including details on physical activity, obesity, coronary heart disease, all-cause mortality, and medical expenditures. We use the model to answer specific research questions, inform debate on important policy issues in public health, support community advocacy, and provide analysis on the long-term impact of proposed changes in policies and programs, thus informing stakeholders at all levels and supporting decisions that can improve the health of populations.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

Development of a model forecasting Dermanyssus gallinae's population dynamics for advancing Integrated Pest Management in laying hen facilities.

PubMed

Mul, Monique F; van Riel, Johan W; Roy, Lise; Zoons, Johan; André, Geert; George, David R; Meerburg, Bastiaan G; Dicke, Marcel; van Mourik, Simon; Groot Koerkamp, Peter W G

2017-10-15

The poultry red mite, Dermanyssus gallinae, is the most significant pest of egg laying hens in many parts of the world. Control of D. gallinae could be greatly improved with advanced Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. The development of a model forecasting the pests' population dynamics in laying hen facilities without and post-treatment will contribute to this advanced IPM and could consequently improve implementation of IPM by farmers. The current work describes the development and demonstration of a model which can follow and forecast the population dynamics of D. gallinae in laying hen facilities given the variation of the population growth of D. gallinae within and between flocks. This high variation could partly be explained by house temperature, flock age, treatment, and hen house. The total population growth variation within and between flocks, however, was in part explained by temporal variation. For a substantial part this variation was unexplained. A dynamic adaptive model (DAP) was consequently developed, as models of this type are able to handle such temporal variations. The developed DAP model can forecast the population dynamics of D. gallinae, requiring only current flock population monitoring data, temperature data and information of the dates of any D. gallinae treatment. Importantly, the DAP model forecasted treatment effects, while compensating for location and time specific interactions, handling the variability of these parameters. The characteristics of this DAP model, and its compatibility with different mite monitoring methods, represent progression from existing approaches for forecasting D. gallinae that could contribute to advancing improved Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. Copyright © 2017 Elsevier B.V. All rights reserved.

Earthquake forecasting test for Kanto district to reduce vulnerability of urban mega earthquake disasters

NASA Astrophysics Data System (ADS)

Yokoi, S.; Tsuruoka, H.; Nanjo, K.; Hirata, N.

2012-12-01

Collaboratory for the Study of Earthquake Predictability (CSEP) is a global project on earthquake predictability research. The final goal of this project is to search for the intrinsic predictability of the earthquake rupture process through forecast testing experiments. The Earthquake Research Institute, the University of Tokyo joined CSEP and started the Japanese testing center called as CSEP-Japan. This testing center provides an open access to researchers contributing earthquake forecast models applied to Japan. Now more than 100 earthquake forecast models were submitted on the prospective experiment. The models are separated into 4 testing classes (1 day, 3 months, 1 year and 3 years) and 3 testing regions covering an area of Japan including sea area, Japanese mainland and Kanto district. We evaluate the performance of the models in the official suite of tests defined by CSEP. The total number of experiments was implemented for approximately 300 rounds. These results provide new knowledge concerning statistical forecasting models. We started a study for constructing a 3-dimensional earthquake forecasting model for Kanto district in Japan based on CSEP experiments under the Special Project for Reducing Vulnerability for Urban Mega Earthquake Disasters. Because seismicity of the area ranges from shallower part to a depth of 80 km due to subducting Philippine Sea plate and Pacific plate, we need to study effect of depth distribution. We will develop models for forecasting based on the results of 2-D modeling. We defined the 3D - forecasting area in the Kanto region with test classes of 1 day, 3 months, 1 year and 3 years, and magnitudes from 4.0 to 9.0 as in CSEP-Japan. In the first step of the study, we will install RI10K model (Nanjo, 2011) and the HISTETAS models (Ogata, 2011) to know if those models have good performance as in the 3 months 2-D CSEP-Japan experiments in the Kanto region before the 2011 Tohoku event (Yokoi et al., in preparation). We use CSEP-Japan experiments as a starting model of non-divided column in a depth. In the presentation, we will discuss the performance of the models comparing results of the Kanto district with those obtained in all over Japan by CSEP-Japan and also add to discuss the results of the 3-month experiments after the 2011 Tohoku earthquake to understand the learning ability of the models associated with recent seismicity of the area.

Evaluating the performance of the Lee-Carter method and its variants in modelling and forecasting Malaysian mortality

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-12-01

This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980-2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly).

USDA-ARS?s Scientific Manuscript database

This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulatingthe relative populations of non-indigenousarthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather dataas influenced by...

Long-Term Economic and Labor Forecast Trends for Washington. 1996.

ERIC Educational Resources Information Center

Lefberg, Irv; And Others

This publication provides actual historical and long-term forecast data on labor force, total wage and salary employment, industry employment, and personal income for the state of Washington. The data are based upon the Washington Office of Financial Management long-term population forecast. Chapter 1 presents long-term forecasts of Washington…

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; AlSaadi, Jassim A.; Neil, Doreen O.; Pierce, Robert B.; Pippin, Margartet R.; Roell, Marilee M.; Kittaka, Chieko; Szykman, James J.

2004-01-01

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

Evaluation of a new microphysical aerosol module in the ECMWF Integrated Forecasting System

NASA Astrophysics Data System (ADS)

Woodhouse, Matthew; Mann, Graham; Carslaw, Ken; Morcrette, Jean-Jacques; Schulz, Michael; Kinne, Stefan; Boucher, Olivier

2013-04-01

The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting atmospheric composition. As part of the first phase of MACC, the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) was incorporated within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model. The microphysical, process-based GLOMAP-mode scheme allows an improved representation of aerosol size and composition and can simulate aerosol evolution in the troposphere and stratosphere. The new aerosol forecasting and re-analysis system (known as IFS-GLOMAP) will also provide improved boundary conditions for regional air quality forecasts, and will benefit from assimilation of observed aerosol optical depths in near real time. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to in situ aerosol mass and number measurements, and remotely-sensed aerosol optical depth measurements. Future development will provide a fully-coupled chemistry-aerosol scheme, and the capability to resolve nitrate aerosol.

Influenza forecasting in human populations: a scoping review.

PubMed

Chretien, Jean-Paul; George, Dylan; Shaman, Jeffrey; Chitale, Rohit A; McKenzie, F Ellis

2014-01-01

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms "influenza AND (forecast* OR predict*)", excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

Influenza Forecasting in Human Populations: A Scoping Review

PubMed Central

Chretien, Jean-Paul; George, Dylan; Shaman, Jeffrey; Chitale, Rohit A.; McKenzie, F. Ellis

2014-01-01

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials. PMID:24714027

On forecasting mortality.

PubMed

Olshansky, S J

1988-01-01

Official forecasts of mortality made by the U.S. Office of the Actuary throughout this century have consistently underestimated observed mortality declines. This is due, in part, to their reliance on the static extrapolation of past trends, an atheoretical statistical method that pays scant attention to the behavioral, medical, and social factors contributing to mortality change. A "multiple cause-delay model" more realistically portrays the effects on mortality of the presence of more favorable risk factors at the population level. Such revised assumptions produce large increases in forecasts of the size of the elderly population, and have a dramatic impact on related estimates of population morbidity, disability, and health care costs.

Developing a Model for Predicting Snowpack Parameters Affecting Vehicle Mobility,

DTIC Science & Technology

1983-05-01

Service River Forecast System -Snow accumulation and JO ablation model. NOAA Technical Memorandum NWS HYDRO-17, National Weather Service, JS Silver Spring... Forecast System . This model indexes each phys- ical process that occurs in the snowpack to the air temperature. Although this results in a signifi...pressure P Probability Q Energy Q Specific humidity R Precipitation s Snowfall depth T Air temperature t Time U Wind speed V Water vapor

Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China

PubMed Central

Wang, Xin-ping; Schaffer, Benjamin Eli; Yang, Zhenlei; Rodriguez-Iturbe, Ignacio

2017-01-01

The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend—for each season—on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons. PMID:28584097

Flood forecasting within urban drainage systems using NARX neural network.

PubMed

Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi

2017-11-01

Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

Forecasting the mortality rates of Malaysian population using Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, Rose Irnawaty; Mohd, Razak; Ngataman, Nuraini; Abrisam, Wan Nur Azifah Wan Mohd

2017-08-01

Actuaries, demographers and other professionals have always been aware of the critical importance of mortality forecasting due to declining trend of mortality and continuous increases in life expectancy. Heligman-Pollard model was introduced in 1980 and has been widely used by researchers in modelling and forecasting future mortality. This paper aims to estimate an eight-parameter model based on Heligman and Pollard's law of mortality. Since the model involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 7.0 (MATLAB 7.0) software will be used in order to estimate the parameters. Statistical Package for the Social Sciences (SPSS) will be applied to forecast all the parameters according to Autoregressive Integrated Moving Average (ARIMA). The empirical data sets of Malaysian population for period of 1981 to 2015 for both genders will be considered, which the period of 1981 to 2010 will be used as "training set" and the period of 2011 to 2015 as "testing set". In order to investigate the accuracy of the estimation, the forecast results will be compared against actual data of mortality rates. The result shows that Heligman-Pollard model fit well for male population at all ages while the model seems to underestimate the mortality rates for female population at the older ages.

Population forecasts for Bangladesh, using a Bayesian methodology.

PubMed

Mahsin, Md; Hossain, Syed Shahadat

2012-12-01

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

NSF's Perspective on Space Weather Research for Building Forecasting Capabilities

NASA Astrophysics Data System (ADS)

Bisi, M. M.; Pulkkinen, A. A.; Bisi, M. M.; Pulkkinen, A. A.; Webb, D. F.; Oughton, E. J.; Azeem, S. I.

2017-12-01

Space weather research at the National Science Foundation (NSF) is focused on scientific discovery and on deepening knowledge of the Sun-Geospace system. The process of maturation of knowledge base is a requirement for the development of improved space weather forecast models and for the accurate assessment of potential mitigation strategies. Progress in space weather forecasting requires advancing in-depth understanding of the underlying physical processes, developing better instrumentation and measurement techniques, and capturing the advancements in understanding in large-scale physics based models that span the entire chain of events from the Sun to the Earth. This presentation will provide an overview of current and planned programs pertaining to space weather research at NSF and discuss the recommendations of the Geospace Section portfolio review panel within the context of space weather forecasting capabilities.

On Constructing Ageing Rural Populations: "Capturing" the Grey Nomad

ERIC Educational Resources Information Center

Davies, Amanda

2011-01-01

The world's population is ageing, with forecasts predicting this ageing is likely to be particularly severe in the rural areas of more developed countries. These forecasts are developed from nationally aggregated census and survey data and assume spatial homogeneity in ageing. They also draw on narrow understandings of older people and construct…

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1975-01-01

LANDSAT and NOAA satellites data were used to study snow depth. These snow measurements were used to help forecast runoff and flooding. Many areas of California, Arizona, Colorado, and Wyoming were emphasized.

Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd

2017-09-01

Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.

A national econometric forecasting model of the dental sector.

PubMed Central

Feldstein, P J; Roehrig, C S

1980-01-01

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

Spatial Interpolation of Aerosol Optical Depth Pollution: Comparison of Methods for the Development of Aerosol Distribution

NASA Astrophysics Data System (ADS)

Safarpour, S.; Abdullah, K.; Lim, H. S.; Dadras, M.

2017-09-01

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe, III; Short, David; Roeder, William

2008-01-01

The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of precipitation at the SLF, and 6) strongest wind in the lowest 3000 ft. The forecast tool was developed as a graphical user interface with Microsoft Excel to help the forecaster enter the variables, and run the appropriate regression equations. Based on the forecaster's input and regression equations, a forecast of the day's peak and average wind is generated and displayed. The application also outputs the probability that the peak wind speed will be ^ 35 kt, 50 kt, and 60 kt.

Stochastic Forecasting of Labor Supply and Population: An Integrated Model.

PubMed

Fuchs, Johann; Söhnlein, Doris; Weber, Brigitte; Weber, Enzo

2018-01-01

This paper presents a stochastic model to forecast the German population and labor supply until 2060. Within a cohort-component approach, our population forecast applies principal components analysis to birth, mortality, emigration, and immigration rates, which allows for the reduction of dimensionality and accounts for correlation of the rates. Labor force participation rates are estimated by means of an econometric time series approach. All time series are forecast by stochastic simulation using the bootstrap method. As our model also distinguishes between German and foreign nationals, different developments in fertility, migration, and labor participation could be predicted. The results show that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run. An important finding from an endogenous modeling of emigration rates is that high net migration in the long run will be difficult to achieve. Our stochastic perspective suggests therefore a high probability of substantially decreasing the labor supply in Germany.

Forecasting the mortality rates of Indonesian population by using neural network

NASA Astrophysics Data System (ADS)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

NASA's Potential Contributions to Avalanche Forecasting Using Active and Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Blonski, Slawomir

2007-01-01

This Candidate Solution is based on using active and passive microwave measurements acquired from NASA satellites to improve USDA (U.S. Department of Agriculture) Forest Service forecasting of avalanche danger. Regional Avalanche Centers prepare avalanche forecasts using ground measurements of snowpack and mountain weather conditions. In this Solution, range of the in situ observations is extended by adding remote sensing measurements of snow depth, snow water equivalent, and snowfall rate acquired by satellite missions that include Aqua, CloudSat, future GPM (Global Precipitation Measurement), and the proposed SCLP (Snow and Cold Land Processes). Measurements of snowpack conditions and time evolution are improved by combining the in situ and satellite observations with a snow model. Recurring snow observations from NASA satellites increase accuracy of avalanche forecasting, which helps the public and the managers of public facilities make better avalanche safety decisions.

Forecasting magma-chamber rupture at Santorini volcano, Greece.

PubMed

Browning, John; Drymoni, Kyriaki; Gudmundsson, Agust

2015-10-28

How much magma needs to be added to a shallow magma chamber to cause rupture, dyke injection, and a potential eruption? Models that yield reliable answers to this question are needed in order to facilitate eruption forecasting. Development of a long-lived shallow magma chamber requires periodic influx of magmas from a parental body at depth. This redistribution process does not necessarily cause an eruption but produces a net volume change that can be measured geodetically by inversion techniques. Using continuum-mechanics and fracture-mechanics principles, we calculate the amount of magma contained at shallow depth beneath Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011-2012 unrest period, that the measured 0.02% increase in volume of Santorini's shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano.

On an improvement of UV index forecast: UV index diagnosis and forecast for Belsk, Poland, in Spring/Summer 1999

NASA Astrophysics Data System (ADS)

Krzyścin, J. W.; Jaroslawski, J.; Sobolewski, P.

2001-10-01

A forecast of the UV index for the following day is presented. The standard approach to the UV index modelling is applied, i.e., the clear-sky UV index is multiplied by the UV cloud transmission factor. The input to the clear-sky model (tropospheric ultraviolet and visible-TUV model, Madronich, in: M. Tevini (Ed.), Environmental Effects of Ultraviolet Radiation, Lewis Publisher, Boca Raton, /1993, p. 17) consists of the total ozone forecast (by a regression model using the observed and forecasted meteorological variables taken as the initial values of aviation (AVN) global model and their 24-hour forecasts, respectively) and aerosols optical depth (AOD) forecast (assumed persistence). The cloud transmission factor forecast is inferred from the 24-h AVN model run for the total (Sun/+sky) solar irradiance at noon. The model is validated comparing the UV index forecasts with the observed values, which are derived from the daily pattern of the UV erythemal irradiance taken at Belsk (52°N,21°E), Poland, by means of the UV Biometer Solar model 501A for the period May-September 1999. Eighty-one percent and 92% of all forecasts fall into /+/-1 and /+/-2 index unit range, respectively. Underestimation of UV index occurs only in 15%. Thus, the model gives a high security in Sun protection for the public. It is found that in /~35% of all cases a more accurate forecast of AOD is needed to estimate the daily maximum of clear-sky irradiance with the error not exceeding 5%. The assumption of the persistence of the cloud characteristics appears as an alternative to the 24-h forecast of the cloud transmission factor in the case when the AVN prognoses are not available.

Near-real-time simulation and internet-based delivery of forecast-flood inundation maps using two-dimensional hydraulic modeling--A pilot study for the Snoqualmie River, Washington

USGS Publications Warehouse

Jones, Joseph L.; Fulford, Janice M.; Voss, Frank D.

2002-01-01

A system of numerical hydraulic modeling, geographic information system processing, and Internet map serving, supported by new data sources and application automation, was developed that generates inundation maps for forecast floods in near real time and makes them available through the Internet. Forecasts for flooding are generated by the National Weather Service (NWS) River Forecast Center (RFC); these forecasts are retrieved automatically by the system and prepared for input to a hydraulic model. The model, TrimR2D, is a new, robust, two-dimensional model capable of simulating wide varieties of discharge hydrographs and relatively long stream reaches. TrimR2D was calibrated for a 28-kilometer reach of the Snoqualmie River in Washington State, and is used to estimate flood extent, depth, arrival time, and peak time for the RFC forecast. The results of the model are processed automatically by a Geographic Information System (GIS) into maps of flood extent, depth, and arrival and peak times. These maps subsequently are processed into formats acceptable by an Internet map server (IMS). The IMS application is a user-friendly interface to access the maps over the Internet; it allows users to select what information they wish to see presented and allows the authors to define scale-dependent availability of map layers and their symbology (appearance of map features). For example, the IMS presents a background of a digital USGS 1:100,000-scale quadrangle at smaller scales, and automatically switches to an ortho-rectified aerial photograph (a digital photograph that has camera angle and tilt distortions removed) at larger scales so viewers can see ground features that help them identify their area of interest more effectively. For the user, the option exists to select either background at any scale. Similar options are provided for both the map creator and the viewer for the various flood maps. This combination of a robust model, emerging IMS software, and application interface programming should allow the technology developed in the pilot study to be applied to other river systems where NWS forecasts are provided routinely.

Quality Assessment of the Cobel-Isba Numerical Forecast System of Fog and Low Clouds

NASA Astrophysics Data System (ADS)

Bergot, Thierry

2007-06-01

Short-term forecasting of fog is a difficult issue which can have a large societal impact. Fog appears in the surface boundary layer and is driven by the interactions between land surface and the lower layers of the atmosphere. These interactions are still not well parameterized in current operational NWP models, and a new methodology based on local observations, an adaptive assimilation scheme and a local numerical model is tested. The proposed numerical forecast method of foggy conditions has been run during three years at Paris-CdG international airport. This test over a long-time period allows an in-depth evaluation of the forecast quality. This study demonstrates that detailed 1-D models, including detailed physical parameterizations and high vertical resolution, can reasonably represent the major features of the life cycle of fog (onset, development and dissipation) up to +6 h. The error on the forecast onset and burn-off time is typically 1 h. The major weakness of the methodology is related to the evolution of low clouds (stratus lowering). Even if the occurrence of fog is well forecasted, the value of the horizontal visibility is only crudely forecasted. Improvements in the microphysical parameterization and in the translation algorithm converting NWP prognostic variables into a corresponding horizontal visibility seems necessary to accurately forecast the value of the visibility.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

A practitioner's tool for assessing glide crack activity

USGS Publications Warehouse

Hendrikx, Jordy; Peitzsch, Erich H.; Fagre, Daniel B.

2010-01-01

Glide cracks can result in full-depth glide avalanche release. Avalanches from glide cracks are notoriously difficult to forecast, but are a reoccurring problem in a number of different avalanche forecasting programs across a range of snow climates. Despite this, there is no consensus for how to best manage, mitigate, or even observe glide cracks and the potential resultant avalanche activity. It is thought that an increase in the rate of snow gliding occurs prior to full-depth avalanche activity, so frequent measuring of glide crack movement provides an index of instability. Therefore, a comprehensive avalanche program with glide crack avalanche activity, should at the least, undertake some form of direct monitoring of glide crack movement. In this paper we present a simple, cheap and repeatable method to track glide crack activity using a series of stakes, reflectors and a laser rangefinder (LaserTech TruPulse360B) linked to a GPS (Trimble Geo XH). We tested the methodology in April 2010, on a glide crack above the Going to the Sun Road in Glacier National Park, Montana, USA. This study suggests a new method to better track the development and movement of glide cracks. It is hoped that by introducing a workable method to easily record glide crack movement, avalanche forecasters will improve their understanding of when, or if, avalanche activity will ensue. Our initial results suggest that these new observations, when combined with local micrometeorological data will result in improved process understanding and forecasting of these phenomena.

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.

Development, Application, and Transition of Aerosol and Trace Gas Products Derived from Next-Generation Satellite Observations to Operations

NASA Technical Reports Server (NTRS)

Berndt, Emily; Naeger, Aaron; Zavodsky, Bradley; McGrath, Kevin; LaFontaine, Frank

2016-01-01

NASA Short-term Prediction Research and Transition (SPoRT) Center has a history of successfully transitioning unique observations and research capabilities to the operational weather community to improve short-term forecasts. SPoRTstrives to bridge the gap between research and operations by maintaining interactive partnerships with end users to develop products that match specific forecast challenges, provide training, and assess the products in the operational environment. This presentation focuses on recent product development, application, and transition of aerosol and trace gas products to operations for specific forecasting applications. Recent activities relating to the SPoRT ozone products, aerosol optical depth composite product, sulfur dioxide, and aerosol index products are discussed.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose.

PubMed

Desrochers, Julie; Wojciechowski, Jessica; Klein-Schwartz, Wendy; Gobburu, Jogarao V S; Gopalakrishnan, Mathangi

2017-08-01

Acetaminophen (APAP) overdose is the leading cause of acute liver injury in the United States. Patients with elevated plasma acetaminophen concentrations (PACs) require hepatoprotective treatment with N-acetylcysteine (NAC). These patients have been primarily risk-stratified using the Rumack-Matthew nomogram. Previous studies of acute APAP overdoses found that the nomogram failed to accurately predict the need for the antidote. The objectives of this study were to develop a population pharmacokinetic (PK) model for APAP following acute overdose and evaluate the utility of population PK model-based Bayesian forecasting in NAC administration decisions. Limited APAP concentrations from a retrospective cohort of acute overdosed subjects from the Maryland Poison Center were used to develop the population PK model and to investigate the effect of type of APAP products and other prognostic factors. The externally validated population PK model was used a prior for Bayesian forecasting to predict the individual PK profile when one or two observed PACs were available. The utility of Bayesian forecasted APAP concentration-time profiles inferred from one (first) or two (first and second) PAC observations were also tested in their ability to predict the observed NAC decisions. A one-compartment model with first-order absorption and elimination adequately described the data with single activated charcoal and APAP products as significant covariates on absorption and bioavailability. The Bayesian forecasted individual concentration-time profiles had acceptable bias (6.2% and 9.8%) and accuracy (40.5% and 41.9%) when either one or two PACs were considered, respectively. The sensitivity and negative predictive value of the Bayesian forecasted NAC decisions using one PAC were 84% and 92.6%, respectively. The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions. © 2017 Pharmacotherapy Publications, Inc.

The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

NASA Astrophysics Data System (ADS)

Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths correspond approximately to a return period of 200 years. Most rivers in Umbria have been involved, exceeding hydrometric thresholds and causing flooding (e.g. Chiascio river). The flood event was continuously monitored at the Umbria Region CF and the possible evolution predicted and assessed on the basis of the model forecasts. The predictions provided by MISDc and STAFOM-RCM were found useful to support real-time decision-making addressed to flood risk management. Moreover, the quantification of the uncertainty affecting the deterministic forecast stages was found consistent with the level of confidence selected and had practical utility corroborating the need of coupling deterministic forecast and 'uncertainty' when the model output is used to support decisions about flood management. REFERENCES Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., 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. Brocca, L., Melone, F., Moramarco, T. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrological Processes, 25 (18), 2801-2813

Recent updates in the aerosol component of the C-IFS model run by ECMWF

NASA Astrophysics Data System (ADS)

Remy, Samuel; Boucher, Olivier; Hauglustaine, Didier; Kipling, Zak; Flemming, Johannes

2017-04-01

The Composition-Integrated Forecast System (C-IFS) is a global atmospheric composition forecasting tool, run by ECMWF within the framework of the Copernicus Atmospheric Monitoring Service (CAMS). The aerosol model of C-IFS is a simple bulk scheme that forecasts 5 species: dust, sea-salt, black carbon, organic matter and sulfate. Three bins represent the dust and sea-salt, for the super-coarse, coarse and fine mode of these species (Morcrette et al., 2009). This talk will present recent updates of the aerosol model, and also introduce forthcoming developments. It will also present the impact of these changes as measured scores against AERONET Aerosol Optical Depth (AOD) and Airbase PM10 observations. The next cycle of C-IFS will include a mass fixer, because the semi-Lagrangian advection scheme used in C-IFS is not mass-conservative. C-IFS now offers the possibility to emit biomass-burning aerosols at an injection height that is provided by a new version of the Global Fire Assimilation System (GFAS). Secondary Organic Aerosols (SOA) production will be scaled on non-biomass burning CO fluxes. This approach allows to represent the anthropogenic contribution to SOA production; it brought a notable improvement in the skill of the model, especially over Europe. Lastly, the emissions of SO2 are now provided by the MACCity inventory instead of and older version of the EDGAR dataset. The seasonal and yearly variability of SO2 emissions are better captured by the MACCity dataset. Upcoming developments of the aerosol model of C-IFS consist mainly in the implementation of a nitrate and ammonium module, with 2 bins (fine and coarse) for nitrate. Nitrate and ammonium sulfate particle formation from gaseous precursors is represented following Hauglustaine et al. (2014); formation of coarse nitrate over pre-existing sea-salt or dust particles is also represented. This extension of the forward model improved scores over heavily populated areas such as Europe, China and Eastern United States. A new sea-salt scheme following Grythe et al (2014) has been adapted into C-IFS, which brings optical depths closer to MODIS values over oceans, and also has a beneficial impact on PM10 forecasts over Europe. The model also offers the possibility to use dynamically computed dry deposition velocities following Zhang et al (2001). These new developments come as options in C-IFS; the decision of use these options in the operational configuration will be taken by ECMWF after considering input from various parties.

Evaluation of atmospheric dust prediction models using ground-based observations

NASA Astrophysics Data System (ADS)

Terradellas, Enric; María Baldasano, José; Cuevas, Emilio; Basart, Sara; Huneeus, Nicolás; Camino, Carlos; Dundar, Cinhan; Benincasa, Francesco

2013-04-01

An important step in numerical prediction of mineral dust is the model evaluation aimed to assess its performance to forecast the atmospheric dust content and to lead to new directions in model development and improvement. The first problem to address the evaluation is the scarcity of ground-based routine observations intended for dust monitoring. An alternative option would be the use of satellite products. They have the advantage of a large spatial coverage and a regular availability. However, they do have numerous drawbacks that make the quantitative retrievals of aerosol-related variables difficult and imprecise. This work presents the use of different ground-based observing systems for the evaluation of dust models in the Regional Center for Northern Africa, Middle East and Europe of the World Meteorological Organization (WMO) Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS). The dust optical depth at 550 nm forecast by different models is regularly compared with the AERONET measurements of Aerosol Optical Depth (AOD) for 40 selected stations. Photometric measurements are a powerful tool for remote sensing of the atmosphere allowing retrieval of aerosol properties, such as AOD. This variable integrates the contribution of different aerosol types, but may be complemented with spectral information that enables hypotheses about the nature of the particles. Comparison is restricted to cases with low Ångström exponent values in order to ensure that coarse mineral dust is the dominant aerosol type. Additionally to column dust load, it is important to evaluate dust surface concentration and dust vertical profiles. Air quality monitoring stations are the main source of data for the evaluation of surface concentration. However they are concentrated in populated and industrialized areas around the Mediterranean. In the present contribution, results of different models are compared with observations of PM10 from the Turkish air quality network for April 2011, when several dust episodes where recorded. In regions devoid of air quality stations (as Saharan and Arabian deserts), model forecasts are regularly evaluated for 38 dust-prone sites through the use of an empirical relationship between visibility data (obtained from meteorological reports) and dust surface concentration. Finally, active remote sensing with lidar or ceilometers is the only way to inquire about the dust vertical distribution. Analysis of selected cases comparing model forecasts and lidar observations at Santa Cruz de Tenerife (Canary Islands) yields promising results regarding the identification of the dust plume thickness. From the results of this pilot trial, the convenience of a regular evaluation will be assessed.

Nambe Pueblo Water Budget and Forecasting model.

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

Brainard, James Robert

2009-10-01

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

Rapid shelf-wide cooling response of a stratified coastal ocean to hurricanes.

PubMed

Seroka, Greg; Miles, Travis; Xu, Yi; Kohut, Josh; Schofield, Oscar; Glenn, Scott

2017-06-01

Large uncertainty in the predicted intensity of tropical cyclones (TCs) persists compared to the steadily improving skill in the predicted TC tracks. This intensity uncertainty has its most significant implications in the coastal zone, where TC impacts to populated shorelines are greatest. Recent studies have demonstrated that rapid ahead-of-eye-center cooling of a stratified coastal ocean can have a significant impact on hurricane intensity forecasts. Using observation-validated, high-resolution ocean modeling, the stratified coastal ocean cooling processes observed in two U.S. Mid-Atlantic hurricanes were investigated: Hurricane Irene (2011)-with an inshore Mid-Atlantic Bight (MAB) track during the late summer stratified coastal ocean season-and Tropical Storm Barry (2007)-with an offshore track during early summer. For both storms, the critical ahead-of-eye-center depth-averaged force balance across the entire MAB shelf included an onshore wind stress balanced by an offshore pressure gradient. This resulted in onshore surface currents opposing offshore bottom currents that enhanced surface to bottom current shear and turbulent mixing across the thermocline, resulting in the rapid cooling of the surface layer ahead-of-eye-center. Because the same baroclinic and mixing processes occurred for two storms on opposite ends of the track and seasonal stratification envelope, the response appears robust. It will be critical to forecast these processes and their implications for a wide range of future storms using realistic 3-D coupled atmosphere-ocean models to lower the uncertainty in predictions of TC intensities and impacts and enable coastal populations to better respond to increasing rapid intensification threats in an era of rising sea levels.

Forecasting the use of elderly care: a static micro-simulation model.

PubMed

Eggink, Evelien; Woittiez, Isolde; Ras, Michiel

2016-07-01

This paper describes a model suitable for forecasting the use of publicly funded long-term elderly care, taking into account both ageing and changes in the health status of the population. In addition, the impact of socioeconomic factors on care use is included in the forecasts. The model is also suitable for the simulation of possible implications of some specific policy measures. The model is a static micro-simulation model, consisting of an explanatory model and a population model. The explanatory model statistically relates care use to individual characteristics. The population model mimics the composition of the population at future points in time. The forecasts of care use are driven by changes in the composition of the population in terms of relevant characteristics instead of dynamics at the individual level. The results show that a further 37 % increase in the use of elderly care (from 7 to 9 % of the Dutch 30-plus population) between 2008 and 2030 can be expected due to a further ageing of the population. However, the use of care is expected to increase less than if it were based on the increasing number of elderly only (+70 %), due to decreasing disability levels and increasing levels of education. As an application of the model, we simulated the effects of restricting access to residential care to elderly people with severe physical disabilities. The result was a lower growth of residential care use (32 % instead of 57 %), but a somewhat faster growth in the use of home care (35 % instead of 32 %).

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...

In-Depth Analysis of the JACK Model.

DOT National Transportation Integrated Search

2009-04-30

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

Forecasting consequences of changing sea ice availability for Pacific walruses

USGS Publications Warehouse

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

2017-01-01

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

SEASAT economic assessment. Volume 8: Ocean fishing case study. [economic benefits of SEASAT satellites to ocean fishing industries in the United States and Canada

NASA Technical Reports Server (NTRS)

1975-01-01

The potential application of SEASAT data with regard to ocean fisheries is discussed. Tracking fish populations, indirect assistance in forecasting expected populations and assistance to fishing fleets in avoiding costs incurred due to adverse weather through improved ocean conditions forecasts were investigated. Case studies on fisheries in the United States and Canada are cited.

[Forecast of costs of ecodependent cancer treatment for the development of management decisions].

PubMed

Krasovskiy, V O

2014-01-01

The methodical approach for probabilistic forecasting and differentiation of treatment of costs of ecodependent cancer cases has been elaborated. The modality is useful in the organization of medical aid to cancer patients, in developing management decisions for the reduction the occupational load on the population, as well as in solutions problems in compensation to the population economic and social loss from industrial plants.

Model Integration for Real-Time Flood Forecasting Inundation Mapping for Nashville Tributaries

NASA Astrophysics Data System (ADS)

Charley, W.; Moran, B.; LaRosa, J.

2012-12-01

In May of 2010, between 14 and 19 inches of rain fell on the Nashville metro area in two days, quickly overwhelming tributaries to the Cumberland River and causing wide-spread, serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. Emergency responders were unable to deliver aid or preventive measures to areas under threat of flooding (or under water) in time to reduce damages because they could not identify those areas far enough in advance of the floods. Nashville Metro Water, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers established a joint venture to seek ways to better forecast short-term flood events in the region. One component of this effort was a pilot project to compute and display real time inundation maps for Mill Creek, a 108 square-mile basin to the south east of Nashville. HEC-RTS (Real-Time Simulation) was used to assimilate and integrate the hydrologic model HEC-HMS with the hydraulics model HEC-RAS and the inundation mapping program HEC-RAS Mapper. The USGS, along with the other agencies, installed additional precipitation and flow/stage gages in the area. Measurements are recorded every 5-30 minutes and are posted on the USGS NWIS database, which are downloaded by HEC-RTS. Using this data in combination with QPFs (Quantitative Precipitation Forecasts) from the NWS, HEC-RTS applies HEC-HMS and HEC-RAS to estimate current and forecast stage hydrographs. The peak stages are read by HEC-RAS Mapper to compute inundation depths for 6 by 6 foot grid cells. HEC-RTS displays the inundation on a high resolution MrSid aerial photo, along with subbasin boundary, street and various other layers. When a user zooms in and "mouses" over a cell, the inundation depth for that cell is displayed as a tool-tip. This procedure for real-time inundation mapping provides a relatively accurate depiction of water depths throughout the basin, as it is computed using the temporal and spatial distribution of rainfall that has actually occurred and will compute depths based on forecasted rainfall. In addition, the HEC-RAS hydraulics model can be modified as the event is occurring to represent changes in the stream channels, such as obstructions at bridges. This paper covers the procedure used and provides results and images from the integrated models for various precipitation scenarios.

Forecasting wildlife response to rapid warming in the Alaskan Arctic

USGS Publications Warehouse

Van Hemert, Caroline R.; Flint, Paul L.; Udevitz, Mark S.; Koch, Joshua C.; Atwood, Todd C.; Oakley, Karen L.; Pearce, John M.

2015-01-01

Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation, and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic, associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.

Forecasting differences in life expectancy by education.

PubMed

van Baal, Pieter; Peters, Frederik; Mackenbach, Johan; Nusselder, Wilma

2016-07-01

Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method.

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

PubMed

Roy, L; Guimond, E

1995-01-01

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

A statistical approach to quasi-extinction forecasting.

PubMed

Holmes, Elizabeth Eli; Sabo, John L; Viscido, Steven Vincent; Fagan, William Fredric

2007-12-01

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.

Implementation and test of a coastal forecasting system for wind waves in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Inghilesi, R.; Catini, F.; Orasi, A.; Corsini, S.

2010-09-01

A coastal forecasting system has been implemented in order to provide a coverage of the whole Mediterranean Sea and of several enclosed coastal areas as well. The problem is to achieve a good definition of the small scale coastal processes which affect the propagation of waves toward the shores while retaining the possibility of selecting any of the possible coastal areas in the whole Mediterranean Sea. The system is built on a very high resolution parallel implementation of the WAM and SWAN models, one-way chain-nested in key areas. The system will shortly be part of the ISPRA SIMM forecasting system which has been operative since 2001. The SIMM sistem makes available the high resolution wind fields (0.1/0.1 deg) used in the coastal system. The coastal system is being tested on several Italian coastal areas (Ligurian Sea, Lower Tyrrenian Sea, Sicily Channel, Lower Adriatic Sea) in order to optimise the numerics of the coastal processes and to verify the results in shallow waters and complex bathymetries. The results of the comparison between hindcast and buoy data in very shallow (14m depth) and deep sea (150m depth) will be shown for several episodes in the upper Tyrrenian Sea.

Forecasting magma-chamber rupture at Santorini volcano, Greece

PubMed Central

Browning, John; Drymoni, Kyriaki; Gudmundsson, Agust

2015-01-01

How much magma needs to be added to a shallow magma chamber to cause rupture, dyke injection, and a potential eruption? Models that yield reliable answers to this question are needed in order to facilitate eruption forecasting. Development of a long-lived shallow magma chamber requires periodic influx of magmas from a parental body at depth. This redistribution process does not necessarily cause an eruption but produces a net volume change that can be measured geodetically by inversion techniques. Using continuum-mechanics and fracture-mechanics principles, we calculate the amount of magma contained at shallow depth beneath Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011–2012 unrest period, that the measured 0.02% increase in volume of Santorini’s shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano. PMID:26507183

Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia

NASA Astrophysics Data System (ADS)

Liu, Zhiquan; Liu, Quanhua; Lin, Hui-Chuan; Schwartz, Craig S.; Lee, Yen-Huei; Wang, Tijian

2011-12-01

Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one-step procedure, the analysis of 3-D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3-D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM10 (particulate matter with diameters less than 10 μm) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.

Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan

Impact of the springtime Himalayan-Tibetan Plateau on the onset on the Indian summer monsoon in coupled forecasts

NASA Astrophysics Data System (ADS)

Orsolini, Yvan; Senan, Retish; Weisheimer, Antje; Vitart, Frederic; Balsamo, Gianpaolo; Doblas-Reyes, Francisco; Stockdale, Timothy; Dutra, Emanuel

2016-04-01

The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region has long been suggested to be an influential factor on the onset of the Indian summer monsoon. In the frame of the SPECS project, we have assessed the impact of realistic snow initialization in springtime over HTP on the onset of the Indian summer monsoon. We examine a suite of coupled ocean-atmosphere 4-month ensemble reforecasts made at the European Centre for Medium-Range Weather Forecasts (ECMWF), using the Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (i) the onset is delayed by about 8 days, and (ii) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by either the El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

Linking Seismicity at Depth to the Mechanics of a Lava Dome Failure - a Forecasting Approach

NASA Astrophysics Data System (ADS)

Salvage, R. O.; Neuberg, J. W.; Murphy, W.

2014-12-01

Soufriere Hills volcano (SHV), Montserrat has been in a state of ongoing unrest since 1995. Prior to eruptions, an increase in the number of seismic events has been observed. We use the Material Failure Law (MFL) (Voight, 1988) to investigate how an accelerating number of low frequency seismic events are related to the timing of a large scale dome collapse in June 1997. We show that although the forecasted timing of a dome collapse may coincide with the known timing, the accuracy of the application of the MFL to the data is poor. Using a cross correlation technique we show how characterising seismicity into similar waveform "families'' allows us to focus on a single process at depth and improve the reliability of our forecast. A number of families are investigated to assess their relative importance. We show that despite the timing of a forecasted dome collapse ranging between several hours of the known timing of collapse, each of the families produces a better forecast in terms of fit to the seismic acceleration data than when using all low frequency seismicity. In addition, we investigate the stability of such families between major dome collapses (1997 and 2003), assessing their potential for use in real-time forecasting. Initial application of Grey's Incidence Analysis suggests that a key parameter influencing the potential for a large scale slumping on the dome of SHV is the rate of low frequency seismicity associated with magma movement and dome growth. We undertook numerical modelling of an andesitic dome with a hydrothermally altered layer down to 800m. The geometry of the dome is based on SHV prior to the collapse of 2003. We show that a critical instability is reached once slope angles exceed 25°, corresponding to a summit height of just over 1100m a.s.l.. The geometry of failure is in close agreement with the identified failure plane suggesting that the input mechanical properties are broadly consistent with reality. We are therefore able to compare different failure geometries based on edifice geomorphology and determine a Factor of Safety associated with such scenarios. This modelling would be extremely useful in a holistic forecasting approach within a volcanic environment. Reference: Voight, B. (1988). A method for prediction of volcanic eruptions. Nature, 332: 125-130.

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. PMID:27219341

The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Does the ``National Free Health Care'' have financial sustainability in China? A case of Shenmu County, Shaanxi Province, China.

PubMed

Wang, Lijian

2015-01-01

The most populous country in the world, China needs more health care. Shenmu's practices show that National Free Health Care (NFHC) system has played an active role in fulfilling the citizen's needs of health care, and alleviating the difficulties in affording the medical costs and seeing doctors. But NFHC system needs substantial financial supporting. It is worth in-depth discussing and reflecting on whether the NFHC system suits China's national conditions. The paper establishes the population age structure forecasting model and NFHC financial burden model by decomposition methodology and actuarial modelling. And it finds than the financial burden of NFHC system in Shenmu will be increased from 256.64 million Yuan to 656.04 million Yuan in 2013-2020, with the growth rate 12.45%. Meanwhile, the percentage of the financial burden in government's annual revenue is less than 2% in 2013-2020, NFHC system has financial sustainability in Shenmu. According to the successful experience abroad and the calculated results in Shenmu, to extend the NFHC system to the whole of China has financial sustainability and realistic possibility.

Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections

PubMed Central

Raymer, James; Abel, Guy J.; Rogers, Andrei

2012-01-01

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper, we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends end with a discussion of our results and possible directions for future research. PMID:23236221

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

PubMed

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

2014-12-01

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

Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

NASA Astrophysics Data System (ADS)

Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

2015-04-01

One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Ghimire, S., and Dhondia, J. (2015) Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, In Press.

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

PubMed Central

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

2015-01-01

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

Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach.

PubMed

Boonen, Tim J; Li, Hong

2017-10-01

Research on mortality modeling of multiple populations focuses mainly on extrapolating past mortality trends and summarizing these trends by one or more common latent factors. This article proposes a multipopulation stochastic mortality model that uses the explanatory power of economic growth. In particular, we extend the Li and Lee model (Li and Lee 2005) by including economic growth, represented by the real gross domestic product (GDP) per capita, to capture the common mortality trend for a group of populations with similar socioeconomic conditions. We find that our proposed model provides a better in-sample fit and an out-of-sample forecast performance. Moreover, it generates lower (higher) forecasted period life expectancy for countries with high (low) GDP per capita than the Li and Lee model.

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions.

PubMed

Gan, Ryan W; Ford, Bonne; Lassman, William; Pfister, Gabriele; Vaidyanathan, Ambarish; Fischer, Emily; Volckens, John; Pierce, Jeffrey R; Magzamen, Sheryl

2017-03-01

Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM 2.5 ) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM 2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM 2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM 2.5 . A 10 μg/m 3 increase in GWR smoke PM 2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019-1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM 2.5 exposure method: a 10 μg/m 3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026-1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931-1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM 2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

Project 1990: Educational Planning at the Metropolitan Level.

ERIC Educational Resources Information Center

Swanson, Austin D.; Lamitie, Robert E.

This paper describes a project designed to provide educational decisionmakers with projections of and forecasts about future metropolitan conditions and problems, and information about the implications of alternative ways of solving metropolitan problems. Project components included (1) population and economic projections and forecasts, (2)…

Forecasting extinction risk with nonstationary matrix models.

PubMed

Gotelli, Nicholas J; Ellison, Aaron M

2006-02-01

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.

Flood Foresight: A near-real time flood monitoring and forecasting tool for rapid and predictive flood impact assessment

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Shelton, Kay; Wood, Elizabeth; Berry, Robert; Bevington, John; Hankin, Barry; Lewis, Gavin; Gubbin, Andrew; Griffiths, Samuel; Barnard, Paul; Pinnell, Marc; Huyck, Charles

2017-04-01

The hours and days immediately after a major flood event are often chaotic and confusing, with first responders rushing to mobilise emergency responders, provide alleviation assistance and assess loss to assets of interest (e.g., population, buildings or utilities). Preparations in advance of a forthcoming event are becoming increasingly important; early warning systems have been demonstrated to be useful tools for decision markers. The extent of damage, human casualties and economic loss estimates can vary greatly during an event, and the timely availability of an accurate flood extent allows emergency response and resources to be optimised, reduces impacts, and helps prioritise recovery. In the insurance sector, for example, insurers are under pressure to respond in a proactive manner to claims rather than waiting for policyholders to report losses. Even though there is a great demand for flood inundation extents and severity information in different sectors, generating flood footprints for large areas from hydraulic models in real time remains a challenge. While such footprints can be produced in real time using remote sensing, weather conditions and sensor availability limit their ability to capture every single flood event across the globe. In this session, we will present Flood Foresight (www.floodforesight.com), an operational tool developed to meet the universal requirement for rapid geographic information, before, during and after major riverine flood events. The tool provides spatial data with which users can measure their current or predicted impact from an event - at building, basin, national or continental scales. Within Flood Foresight, the Screening component uses global rainfall predictions to provide a regional- to continental-scale view of heavy rainfall events up to a week in advance, alerting the user to potentially hazardous situations relevant to them. The Forecasting component enhances the predictive suite of tools by providing a local-scale view of the extent and depth of possible riverine flood events several days in advance by linking forecast river flow from a hydrological model to a global flood risk map. The Monitoring component provides a similar local-scale view of a flood inundation extent but in near real time, as an event unfolds, by combining the global flood risk map with observed river gauge telemetry. Immediately following an event, the maximum extent of the flood is also generated. Users of Flood Foresight will be able to receive current and forecast flood extents and depth information via API into their own GIS or analytics software. The set of tools is currently operational for the UK and Europe; the methods presented can be applied globally, allowing provision of service to any country or region. This project was supported by InnovateUK under the Solving Business Problems with Environmental Data competition.

Long-term population cycles in human societies.

PubMed

Turchin, Peter

2009-04-01

Human population dynamics are usually conceptualized as either boundless growth or growth to an equilibrium. The implicit assumption underlying these paradigms is that any feedback processes regulating population density, if they exist, operate on a fast-time-scale, and therefore we do not expect to observe population oscillations in human population numbers. This review asks, are population processes in historical and prehistorical human populations characterized by second-order feedback loops, that is, regulation involving lags? If yes, then the implications for forecasting future population change are obvious--what may appear as inexplicable, exogenously driven reverses in population trends may actually be a result of feedbacks operating with substantial time lags. This survey of a variety of historical and archeological data indicates that slow oscillations in population numbers, with periods of roughly two to three centuries, are observed in a number of world regions and historical periods. Next, a potential explanation for this pattern, the demographic-structural theory, is discussed. Finally, the implications of these results for global population forecasts is discussed.

Forecasting Tools Point to Fishing Hotspots

NASA Technical Reports Server (NTRS)

2009-01-01

Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.

The evolutionary time machine: forecasting how populations can adapt to changing environments using dormant propagules

PubMed Central

Orsini, Luisa; Schwenk, Klaus; De Meester, Luc; Colbourne, John K.; Pfrender, Michael E.; Weider, Lawrence J.

2013-01-01

Evolutionary changes are determined by a complex assortment of ecological, demographic and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils and permafrost are convenient natural archives of population-histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change. PMID:23395434

Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting

USGS Publications Warehouse

Basak, Aniruddha; Kulkarni, Chinmay; Schmidt, Kevin M.; Mengshoel, Ole

2016-01-01

This paper investigates methods to analyze and forecast soil moisture time series. We extend an existing Antecedent Water Index (AWI) model, which expresses soil moisture as a function of time and rainfall. Unfortunately, the existing AWI model does not forecast effectively for time periods beyond a few hours. To overcome this limitation, we develop a novel AWI-based model. Our model accumulates rainfall over a time interval and can fit a diverse range of wetting and drying curves. In addition, parameters in our model reflect hydrologic redistribution processes of gravity and suction.We validate our models using experimental soil moisture and rainfall time series data collected from steep gradient post-wildfire sites in Southern California, where rapid landscape change was observed in response to small to moderate rain storms. We found that our novel model fits the data for three distinct soil textures, occurring at different depths below the ground surface (5, 15, and 30 cm). Our model also successfully forecasts soil moisture trends, such as drying and wetting rate.

Introduction on the operational storm surge forecasting system in Korea Operational Oceanographic System (KOOS)

NASA Astrophysics Data System (ADS)

Kwon, Jae-Il; Park, Kwang-Soon; Choi, Jung-Woon; Lee, Jong-Chan; Heo, Ki-Young; Kim, Sang-Ik

2017-04-01

During last more than 50 years, 258 typhoons passed and affected the Korean peninsula in terms of high winds, storm surges and extreme waves. In this study we explored the performance of the operational storm surge forecasting system in the Korea Operational Oceanographic System (KOOS) with 8 typhoons from 2010 to 2016. The operation storm surge forecasting system for the typhoon in KOOS is based on 2D depth averaged model with tides and CE (U.S. Army Corps of Engineers) wind model. Two key parameters of CE wind model, the locations of typhoon center and its central atmospheric pressure are based from Korea Meteorological administrative (KMA)'s typhoon information provided from 1 day to 3 hour intervals with the approach of typhoon through the KMA's web-site. For 8 typhoons cases, the overall errors, other performances and analysis such as peak time and surge duration are presented in each case. The most important factor in the storm surge errors in the operational forecasting system is the accuracy of typhoon passage prediction.

Statistical earthquake focal mechanism forecasts

NASA Astrophysics Data System (ADS)

Kagan, Yan Y.; Jackson, David D.

2014-04-01

Forecasts of the focal mechanisms of future shallow (depth 0-70 km) earthquakes are important for seismic hazard estimates and Coulomb stress, and other models of earthquake occurrence. Here we report on a high-resolution global forecast of earthquake rate density as a function of location, magnitude and focal mechanism. In previous publications we reported forecasts of 0.5° spatial resolution, covering the latitude range from -75° to +75°, based on the Global Central Moment Tensor earthquake catalogue. In the new forecasts we have improved the spatial resolution to 0.1° and the latitude range from pole to pole. Our focal mechanism estimates require distance-weighted combinations of observed focal mechanisms within 1000 km of each gridpoint. Simultaneously, we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms, using the method of Kagan & Jackson proposed in 1994. This average angle reveals the level of tectonic complexity of a region and indicates the accuracy of the prediction. The procedure becomes problematical where longitude lines are not approximately parallel, and where shallow earthquakes are so sparse that an adequate sample spans very large distances. North or south of 75°, the azimuths of points 1000 km away may vary by about 35°. We solved this problem by calculating focal mechanisms on a plane tangent to the Earth's surface at each forecast point, correcting for the rotation of the longitude lines at the locations of earthquakes included in the averaging. The corrections are negligible between -30° and +30° latitude, but outside that band uncorrected rotations can be significantly off. Improved forecasts at 0.5° and 0.1° resolution are posted at http://eq.ess.ucla.edu/kagan/glob_gcmt_index.html.

Assessing three fish species ecological status in Colorado River, Grand Canyon based on physical habitat and population models.

PubMed

Yao, Weiwei; Chen, Yuansheng

2018-04-01

Colorado River is a unique ecosystem and provides important ecological services such as habitat for fish species as well as water power energy supplies. River management for this ecosystem requires assessment and decision support tools for fish which involves protecting, restoring as well as forecasting of future conditions. In this paper, a habitat and population model was developed and used to determine the levels of fish habitat suitability and population density in Colorado River between Lees Ferry and Lake Mead. The short term target fish populations are also predicted based on native fish recovery strategy. This model has been developed by combining hydrodynamics, heat transfer and sediment transport models with a habitat suitability index model and then coupling with habitat model into life stage population model. The fish were divided into four life stages according to the fish length. Three most abundant and typical native and non-native fish were selected as target species, which are rainbow trout (Oncorhynchus mykiss), brown trout (Salmo trutta) and flannelmouth sucker (Catostomus latipinnis). Flow velocity, water depth, water temperature and substrates were used as the suitability indicators in habitat model and overall suitability index (OSI) as well as weight usable area (WUA) was used as an indicator in population model. A comparison was made between simulated fish population alteration and surveyed fish number fluctuation during 2000 to 2009. The application of this habitat and population model indicates that this model can be accurate present habitat situation and targets fish population dynamics of in the study areas. The analysis also indicates the flannelmouth sucker population will steadily increase while the rainbow trout will decrease based on the native fish recovery scheme. Copyright © 2018. Published by Elsevier Inc.

Alaska Division of Geological and Geophysical Surveys

Science.gov Websites

Name Title Gabriel Wolken, Ph.D. Program Manager Katreen Wikstrom Jones M.Sc. Geologist Research flood forecasting) rely on a quantitative assessment of distributed snow thickness and stored water . 2015. End-of-winter snow depth variability on glaciers in Alaska. Journal of Geophysical Research

Predictable Unpredictability: the Problem with Basing Medicare Policy on Long-Term Financial Forecasting.

PubMed

Glied, Sherry; Zaylor, Abigail

2015-07-01

The authors assess how Medicare financing and projections of future costs have changed since 2000. They also assess the impact of legislative reforms on the sources and levels of financing and compare cost forecasts made at different times. Although the aging U.S. population and rising health care costs are expected to increase the share of gross domestic product devoted to Medicare, changes made in the program over the past decade have helped stabilize Medicare's financial outlook--even as benefits have been expanded. Long-term forecasting uncertainty should make policymakers and beneficiaries wary of dramatic changes to the program in the near term that are intended to alter its long-term forecast: the range of error associated with cost forecasts rises as the forecast window lengthens. Instead, policymakers should focus on the immediate policy window, taking steps to reduce the current burden of Medicare costs by containing spending today.

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Fennec dust forecast intercomparison over the Sahara in June 2011

NASA Astrophysics Data System (ADS)

Chaboureau, Jean-Pierre; Flamant, Cyrille; Dauhut, Thibaut; Kocha, Cécile; Lafore, Jean-Philippe; Lavaysse, Chistophe; Marnas, Fabien; Mokhtari, Mohamed; Pelon, Jacques; Reinares Martínez, Irene; Schepanski, Kerstin; Tulet, Pierre

2016-06-01

In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynamics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

Fennec dust forecast intercomparison over the Sahara in June 2011

NASA Astrophysics Data System (ADS)

Chaboureau, J. P.; Flamant, C.; Dauhut, T.; Lafore, J. P.; Lavaysse, C.; Pelon, J.; Schepanski, K.; Tulet, P.

2016-12-01

In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynam-ics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

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

PubMed

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

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change and demonstrate the importance of incorporating biogeographical variability into predictive models for an accurate prediction of species dynamics as climate changes. © 2017 John Wiley & Sons Ltd.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Predicting foraging wading bird populations in Everglades National Park from seasonal hydrologic statistics under different management scenarios

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Han; Lall, Upmanu; Engel, Vic

2011-09-01

The ability to map relationships between ecological outcomes and hydrologic conditions in the Everglades National Park (ENP) is a key building block for their restoration program, a primary goal of which is to improve conditions for wading birds. This paper presents a model linking wading bird foraging numbers to hydrologic conditions in the ENP. Seasonal hydrologic statistics derived from a single water level recorder are well correlated with water depths throughout most areas of the ENP, and are effective as predictors of wading bird numbers when using a nonlinear hierarchical Bayesian model to estimate the conditional distribution of bird populations. Model parameters are estimated using a Markov chain Monte Carlo (MCMC) procedure. Parameter and model uncertainty is assessed as a byproduct of the estimation process. Water depths at the beginning of the nesting season, the average dry season water level, and the numbers of reversals from the dry season recession are identified as significant predictors, consistent with the hydrologic conditions considered important in the production and concentration of prey organisms in this system. Long-term hydrologic records at the index location allow for a retrospective analysis (1952-2006) of foraging bird numbers showing low frequency oscillations in response to decadal fluctuations in hydroclimatic conditions. Simulations of water levels at the index location used in the Bayesian model under alternative water management scenarios allow the posterior probability distributions of the number of foraging birds to be compared, thus providing a mechanism for linking management schemes to seasonal rainfall forecasts.

Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

USGS Publications Warehouse

Balk, B.; Elder, K.; Baron, Jill S.

1998-01-01

Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

NASA Astrophysics Data System (ADS)

Cong, Z.

2015-12-01

In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting and other information about the irrigation. This system can be expanded in other irrigation districts. In future, it is even possible to upgrade the system for the mobile user.

Forecasting patient outcomes in the management of hyperlipidemia.

PubMed

Brier, K L; Tornow, J J; Ries, A J; Weber, M P; Downs, J R

1999-03-22

To forecast adult patient outcomes in the management of hyperlipidemia using adult National Health and Examination Survey III (NHANES III) population statistics and National Cholesterol Education Program (NCEP) guidelines for goals of therapy. Review of the hyperlipidemia drug therapy English-language medical literature with emphasis on randomized controlled trials of more than 6 weeks' duration published in the last 7 years, product package inserts, US Food and Drug Administration submission information, and NHANES III population statistics. Data were extracted from studies of lipid-lowering therapy to modify low-density lipoprotein (LDL) levels for primary and secondary prevention of coronary heart disease. The data that were evaluated included sample size, study design, therapeutic intervention, length of study, percentage change in LDL levels, and patient demographics. Cumulative frequency curves of the LDL distribution among the US adult population were constructed. The mean efficacy of drug therapy from qualified studies was used to extrapolate the percentage of the population expected to respond to the intervention and to forecast the patient outcome. A useful tool for clinicians was constructed to approximate the percentage of patients, based on risk stratification, who would reach NCEP target goal after a given pharmacotherapeutic intervention to decrease LDL levels.

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

USGS Publications Warehouse

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

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior's listing-decision process, the US Geological Survey (USGS) produced a series of nine research reports evaluating the present and future status of polar bears throughout their range. In response, Armstrong et al. [Armstrong, J. S., K. C. Green, W. Soon. 2008. Polar bear population forecasts: A public-policy forecasting audit. Interfaces 38(5) 382–405], which we will refer to as AGS, performed an audit of two of these nine reports. AGS claimed that the general circulation models upon which the USGS reports relied were not valid forecasting tools, that USGS researchers were not objective or lacked independence from policy decisions, that they did not utilize all available information in constructing their forecasts, and that they violated numerous principles of forecasting espoused by AGS. AGS (p. 382) concluded that the two USGS reports were "unscientific and inconsequential to decision makers." We evaluate the AGS audit and show how AGS are mistaken or misleading on every claim. We provide evidence that general circulation models are useful in forecasting future climate conditions and that corporate and government leaders are relying on these models to do so. We clarify the strict independence of the USGS from the listing decision. We show that the allegations of failure to follow the principles of forecasting espoused by AGS are either incorrect or are based on misconceptions about the Arctic environment, polar bear biology, or statistical and mathematical methods. We conclude by showing that the AGS principles of forecasting are too ambiguous and subjective to be used as a reliable basis for auditing scientific investigations. In summary, we show that the AGS audit offers no valid criticism of the USGS conclusion that global warming poses a serious threat to the future welfare of polar bears and that it only serves to distract from reasoned public-policy debate.

Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Evaluation of the Impact of Atmospheric Infrared Sounder (AIRS) Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of 6 weeks of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

ASSESSMENT OF THE WINTER-TIME PERFORMANCE OF DEVELOPMENTAL PARTICULATE MATTER FORECASTS WITH THE ETA-CMAQ MODELING SYSTEM

EPA Science Inventory

It is desirable for local air quality agencies to accurately forecast tropospheric PM_2.5 concentrations to alert the sensitive population of the onset, severity and duration of unhealthy air, and to encourage the public and industry to reduce emissions-producing activi...

Forecasting Social Trends as a Basis for Formulating Educational Policy.

ERIC Educational Resources Information Center

Lewis, Arthur J.

The paper describes how information regarding future trends is collected and made available to educational policy makers. Focusing on educational implications of social and population trends, the paper is based on data derived from use of trend forecasting by educational policy makers in Florida and other southeastern states. The document is…

Relationship of physiography and snow area to stream discharge. [Kings River Watershed, California

NASA Technical Reports Server (NTRS)

Mccuen, R. H. (Principal Investigator)

1979-01-01

The author has identified the following significant results. A comparison of snowmelt runoff models shows that the accuracy of the Tangborn model and regression models is greater if the test data falls within the range of calibration than if the test data lies outside the range of calibration data. The regression models are significantly more accurate for forecasts of 60 days or more than for shorter prediction periods. The Tangborn model is more accurate for forecasts of 90 days or more than for shorter prediction periods. The Martinec model is more accurate for forecasts of one or two days than for periods of 3,5,10, or 15 days. Accuracy of the long-term models seems to be independent of forecast data. The sufficiency of the calibration data base is a function not only of the number of years of record but also of the accuracy with which the calibration years represent the total population of data years. Twelve years appears to be a sufficient length of record for each of the models considered, as long as the twelve years are representative of the population.

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.

New forecasting methodology indicates more disease and earlier mortality ahead for today's younger Americans.

PubMed

Reither, Eric N; Olshansky, S Jay; Yang, Yang

2011-08-01

Traditional methods of projecting population health statistics, such as estimating future death rates, can give inaccurate results and lead to inferior or even poor policy decisions. A new "three-dimensional" method of forecasting vital health statistics is more accurate because it takes into account the delayed effects of the health risks being accumulated by today's younger generations. Applying this forecasting technique to the US obesity epidemic suggests that future death rates and health care expenditures could be far worse than currently anticipated. We suggest that public policy makers adopt this more robust forecasting tool and redouble efforts to develop and implement effective obesity-related prevention programs and interventions.

A Social Justice Agenda: Ready, or Not?

ERIC Educational Resources Information Center

Speight, Suzette L.; Vera, Elizabeth M.

2004-01-01

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

Can a coupled meteorology–chemistry model reproduce the historical trend in aerosol direct radiative effects over the Northern Hemisphere?

EPA Science Inventory

The ability of a coupled meteorology–chemistry model, i.e., Weather Research and Forecast and Community Multiscale Air Quality (WRF-CMAQ), to reproduce the historical trend in aerosol optical depth (AOD) and clear-sky shortwave radiation (SWR) over the Northern Hemisphere h...

Long-term forecasting of anesthesia workload in operating rooms from changes in a hospital's local population can be inaccurate.

PubMed

Masursky, Danielle; Dexter, Franklin; O'Leary, Colleen E; Applegeet, Carol; Nussmeier, Nancy A

2008-04-01

Anesthesia department planning depends on forecasting future demand for perioperative services. Little is known about long-range forecasting of anesthesia workload. We studied operating room (OR) times at Hospital A over 16 yr (1991-2006), anesthesia times at Hospital B over 26 yr (1981-2006), and cases at Hospital C over 13 yr (1994-2006). Each hospital is >100 yr old and is located in a US city with other hospitals that are >50 yr old. Hospitals A and B are the sole University hospitals in their metropolitan statistical areas (and many counties beyond). Hospital C is the sole tertiary hospital for >375 km. Each hospital's choice of a measure of anesthesia work to be analyzed was likely unimportant, as the annual hours of anesthesia correlated highly both with annual numbers of cases (r = 0.98) and with American Society of Anesthesiologist's Relative Value Guide units of work (r = 0.99). Despite a 2% decline in the local population, the hours of OR time at Hospital A increased overall (Pearson r = -0.87, P < 0.001) and for children (r = -0.84). At Hospital B, there was a strong positive correlation between population and hours of anesthesia (r = 0.97, P < 0.001), but not between annual increases in population and workload (r = -0.18). At Hospital C, despite a linear increase in population, the annual numbers of cases increased, declined with opening of two outpatient surgery facilities, and then stabilized. The predictive value of local personal income was low. In contrast, the annual increases in the hours of OR time and anesthesia could be modeled using simple time series methods. Although growth of the elderly population is a simple justification for building more ORs, managers should be cautious in arguing for strategic changes in capacity at individual hospitals based on future changes in the national age-adjusted population. Local population can provide little value in forecasting future anesthesia workloads at individual hospitals. In addition, anesthesia groups and hospital administrators should not focus on quarterly changes in workload, because workload can vary widely, despite consistent patterns over decades. To facilitate long-range planning, anesthesia groups and hospitals should save their billing and OR time data, display it graphically over years, and supplement with corresponding forecasting methods (e.g., staff an additional OR when an upper prediction bound of workload per OR exceeds a threshold).

Typhoon air-sea drag coefficient in coastal regions

NASA Astrophysics Data System (ADS)

Zhao, Zhong-Kuo; Liu, Chun-Xia; Li, Qi; Dai, Guang-Feng; Song, Qing-Tao; Lv, Wei-Hua

2015-02-01

The air-sea drag during typhoon landfalls is investigated for a 10 m wind speed as high as U10 ≈ 42 m s-1, based on multilevel wind measurements from a coastal tower located in the South China Sea. The drag coefficient (CD) plotted against the typhoon wind speed is similar to that of open ocean conditions; however, the CD curve shifts toward a regime of lower winds, and CD increases by a factor of approximately 0.5 relative to the open ocean. Our results indicate that the critical wind speed at which CD peaks is approximately 24 m s-1, which is 5-15 m s-1 lower than that from deep water. Shoaling effects are invoked to explain the findings. Based on our results, the proposed CD formulation, which depends on both water depth and wind speed, is applied to a typhoon forecast model. The forecasts of typhoon track and surface wind speed are improved. Therefore, a water-depth-dependence formulation of CD may be particularly pertinent for parameterizing air-sea momentum exchanges over shallow water.

An improved method for predicting the evolution of the characteristic parameters of an information system

NASA Astrophysics Data System (ADS)

Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

2018-03-01

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

Forecast of the number of patients with end-stage renal disease in the United States to the year 2010.

PubMed

Xue, J L; Ma, J Z; Louis, T A; Collins, A J

2001-12-01

As the United States end-stage renal disease (ESRD) program enters the new millennium, the continued growth of the ESRD population poses a challenge for policy makers, health care providers, and financial planners. To assist in future planning for the ESRD program, the growth of patient numbers and Medicare costs was forecasted to the year 2010 by modeling of historical data from 1982 through 1997. A stepwise autoregressive method and exponential smoothing models were used. The forecasting models for ESRD patient numbers demonstrated mean errors of -0.03 to 1.03%, relative to the observed values. The model for Medicare payments demonstrated -0.12% mean error. The R(2) values for the forecasting models ranged from 99.09 to 99.98%. On the basis of trends in patient numbers, this forecast projects average annual growth of the ESRD populations of approximately 4.1% for new patients, 6.4% for long-term ESRD patients, 7.1% for dialysis patients, 6.1% for patients with functioning transplants, and 8.2% for patients on waiting lists for transplants, as well as 7.7% for Medicare expenditures. The numbers of patients with ESRD in 2010 are forecasted to be 129,200 +/- 7742 (95% confidence limits) new patients, 651,330 +/- 15,874 long-term ESRD patients, 520,240 +/- 25,609 dialysis patients, 178,806 +/- 4349 patients with functioning transplants, and 95,550 +/- 5478 patients on waiting lists. The forecasted Medicare expenditures are projected to increase to $28.3 +/- 1.7 billion by 2010. These projections are subject to many factors that may alter the actual growth, compared with the historical patterns. They do, however, provide a basis for discussing the future growth of the ESRD program and how the ESRD community can meet the challenges ahead.

The future of death in America

PubMed Central

King, Gary; Soneji, Samir

2013-01-01

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public and private pensions, and health care financing systems. In part because existing methods forecast less accurately when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too. PMID:24696636

Social Effects of Prospective Population Changes in the United States.

ERIC Educational Resources Information Center

Kirk, Dudley

Unlike many population forecasts, the thesis of this paper is that present and prospective effects of population growth in the United States have been exaggerated in comparison with other aspects of population change. The effects of national population growth have been confused with those of growing affluence, changing technology, and…

State of Washington Population Trends, 1977. Washington State Information Report.

ERIC Educational Resources Information Center

Washington State Office of Program Planning and Fiscal Management, Olympia.

As of April 1, 1977, Washington's population was estimated at 3,661,975--an increase of 248,725 since 1970. Prepared yearly, this report presents data on the official April 1 population estimates for cities, towns, and counties; components of population change; planned population forecasting activities; procedures which help make the housing unit…

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Spatial forecast of landslides in three gorges based on spatial data mining.

PubMed

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

PubMed Central

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. PMID:22573999

Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.

Achieving the NOAA Arctic Action Plan: The Missing Permafrost Element - Permafrost Forecasting Listening Session Results

NASA Astrophysics Data System (ADS)

Buxbaum, T. M.; Thoman, R.; Romanovsky, V. E.

2015-12-01

Permafrost is ground at or below freezing for at least two consecutive years. It currently occupies 80% of Alaska. Permafrost temperature and active layer thickness (ALT) are key climatic variables for monitoring permafrost conditions. Active layer thickness is the depth that the top layer of ground above the permafrost thaws each summer season and permafrost temperature is the temperature of the frozen permafrost under this active layer. Knowing permafrost conditions is key for those individuals working and living in Alaska and the Arctic. The results of climate models predict vast changes and potential permafrost degradation across Alaska and the Arctic. NOAA is working to implement its 2014 Arctic Action Plan and permafrost forecasting is a missing piece of this plan. The Alaska Center for Climate Assessment and Policy (ACCAP), using our webinar software and our diverse network of statewide stakeholder contacts, hosted a listening session to bring together a select group of key stakeholders. During this listening session the National Weather Service (NWS) and key permafrost researchers explained what is possible in the realm of permafrost forecasting and participants had the opportunity to discuss and share with the group (NWS, researchers, other stakeholders) what is needed for usable permafrost forecasting. This listening session aimed to answer the questions: Is permafrost forecasting needed? If so, what spatial scale is needed by stakeholders? What temporal scales do stakeholders need/want? Are there key times (winter, fall freeze-up, etc.) or locations (North Slope, key oil development areas, etc.) where forecasting would be most applicable and useful? Are there other considerations or priority needs we haven't thought of regarding permafrost forecasting? This presentation will present the results of that listening session.

Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

NASA Astrophysics Data System (ADS)

Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

2014-05-01

The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

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

Treesearch

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

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior...

Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

Forecasting residential electricity demand in provincial China.

PubMed

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

2017-03-01

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

Applying Thiessen Polygon Catchment Areas and Gridded Population Weights to Estimate Conflict-Driven Population Changes in South Sudan

NASA Astrophysics Data System (ADS)

Jordan, L.

2017-10-01

Recent violence in South Sudan produced significant levels of conflict-driven migration undermining the accuracy and utility of both national and local level population forecasts commonly used in demographic estimates, public health metrics and food security proxies. This article explores the use of Thiessen Polygons and population grids (Gridded Population of the World, WorldPop and LandScan) as weights for estimating the catchment areas for settlement locations that serve large populations of internally displaced persons (IDP), in order to estimate the county-level in- and out-migration attributable to conflict-driven displacement between 2014-2015. Acknowledging IDP totals improves internal population estimates presented by global population databases. Unlike other forecasts, which produce spatially uniform increases in population, accounting for displaced population reveals that 15 percent of counties (n = 12) increased in population over 20 percent, and 30 percent of counties (n = 24) experienced zero or declining population growth, due to internal displacement and refugee out-migration. Adopting Thiessen Polygon catchment zones for internal migration estimation can be applied to other areas with United Nations IDP settlement data, such as Yemen, Somalia, and Nigeria.

Improving the Resilience of Major Ports and Critical Supply Chains to Extreme Coastal Flooding: a Combined Artificial Neural Network and Hydrodynamic Simulation Approach to Predicting Tidal Surge Inundation of Port Infrastructure and Impact on Operations.

NASA Astrophysics Data System (ADS)

French, J.

2015-12-01

Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost. As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration. We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port. Functional relationships between peak water level and surge 'shape' allow estimation of flood depths and durations for any location. Supplementing existing surge warning systems, our approach predicts the location and duration of flooding in detail, and allows port managers to take steps to minimize its impact on the most critical aspects of port operation.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2015-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

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)

Definition of Pluviometric Thresholds For A Real Time Flood Forecasting System In The Arno Watershed

NASA Astrophysics Data System (ADS)

Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.

The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.

Current-Sensitive Path Planning for an Underactuated Free-Floating Ocean Sensorweb

NASA Technical Reports Server (NTRS)

Dahl, Kristen P.; Thompson, David R.; McLaren, David; Chao, Yi; Chien, Steve

2011-01-01

This work investigates multi-agent path planning in strong, dynamic currents using thousands of highly under-actuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.

Using Multi-Angle WorldView-2 Imagery to Determine Ocean Depth Near Oahu, Hawaii

DTIC Science & Technology

2012-09-01

Reflection geometry used in the definition of BRDF (From McConnon [2010...Visible/InfraRed Imaging Spectrometer BRDF : Bidirectional Reflectance Distribution Function DHMs: Digital Height Maps DNs: Digital Numbers EM...navigation and fisheries management, and are also helpful for improving models of ocean circulation, air-sea interaction, weather forecasting, and

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... facilities; (3) Provide an in-depth analysis of the regional markets for power if loan feasibility depends to any degree on a borrower's ability to sell surplus power while its system loads grow to meet the... sensitivity analysis if required by RUS pursuant to § 1710.300(d)(5). (e) The projections shall be coordinated...

Forecasting human exposure to atmospheric pollutants in Portugal - A modelling approach

NASA Astrophysics Data System (ADS)

Borrego, C.; Sá, E.; Monteiro, A.; Ferreira, J.; Miranda, A. I.

2009-12-01

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure. To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM 10 (<10 μg m -3 h), but for O 3, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.

A new approach on seismic mortality estimations based on average population density

NASA Astrophysics Data System (ADS)

Zhu, Xiaoxin; Sun, Baiqing; Jin, Zhanyong

2016-12-01

This study examines a new methodology to predict the final seismic mortality from earthquakes in China. Most studies established the association between mortality estimation and seismic intensity without considering the population density. In China, however, the data are not always available, especially when it comes to the very urgent relief situation in the disaster. And the population density varies greatly from region to region. This motivates the development of empirical models that use historical death data to provide the path to analyze the death tolls for earthquakes. The present paper employs the average population density to predict the final death tolls in earthquakes using a case-based reasoning model from realistic perspective. To validate the forecasting results, historical data from 18 large-scale earthquakes occurred in China are used to estimate the seismic morality of each case. And a typical earthquake case occurred in the northwest of Sichuan Province is employed to demonstrate the estimation of final death toll. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20 %, and opens the door for conducting final death forecasts with a qualitative and quantitative approach. Limitations and future research are also analyzed and discussed in the conclusion.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan.

PubMed

Chen, Brian K; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-Chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-12-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan's future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model - the Japanese Future Elderly Model (FEM) - for Japan. We use the model to forecast disability and health for Japan's future elderly. Our simulation suggests that by 2040, over 27 percent of Japan's elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan's future.

Integrating bioclimate with population models to improve forecasts of species extinctions under climate change.

PubMed

Brook, Barry W; Akçakaya, H Resit; Keith, David A; Mace, Georgina M; Pearson, Richard G; Araújo, Miguel B

2009-12-23

Climate change is already affecting species worldwide, yet existing methods of risk assessment have not considered interactions between demography and climate and their simultaneous effect on habitat distribution and population viability. To address this issue, an international workshop was held at the University of Adelaide in Australia, 25-29 May 2009, bringing leading species distribution and population modellers together with plant ecologists. Building on two previous workshops in the UK and Spain, the participants aimed to develop methodological standards and case studies for integrating bioclimatic and metapopulation models, to provide more realistic forecasts of population change, habitat fragmentation and extinction risk under climate change. The discussions and case studies focused on several challenges, including spatial and temporal scale contingencies, choice of predictive climate, land use, soil type and topographic variables, procedures for ensemble forecasting of both global climate and bioclimate models and developing demographic structures that are realistic and species-specific and yet allow generalizations of traits that make species vulnerable to climate change. The goal is to provide general guidelines for assessing the Red-List status of large numbers of species potentially at risk, owing to the interactions of climate change with other threats such as habitat destruction, overexploitation and invasive species.

Methane and nitrous oxide emissions from livestock agriculture in 16 local administrative districts of Korea.

PubMed

Ji, Eun Sook; Park, Kyu-Hyun

2012-12-01

This study was conducted to evaluate methane (CH4) and nitrous oxide (N2O) emissions from livestock agriculture in 16 local administrative districts of Korea from 1990 to 2030. National Inventory Report used 3 yr averaged livestock population but this study used 1 yr livestock population to find yearly emission fluctuations. Extrapolation of the livestock population from 1990 to 2009 was used to forecast future livestock population from 2010 to 2030. Past (yr 1990 to 2009) and forecasted (yr 2010 to 2030) averaged enteric CH4 emissions and CH4 and N2O emissions from manure treatment were estimated. In the section of enteric fermentation, forecasted average CH4 emissions from 16 local administrative districts were estimated to increase by 4%-114% compared to that of the past except for Daejeon (-63%), Seoul (-36%) and Gyeonggi (-7%). As for manure treatment, forecasted average CH4 emissions from the 16 local administrative districts were estimated to increase by 3%-124% compared to past average except for Daejeon (-77%), Busan (-60%), Gwangju (-48%) and Seoul (-8%). For manure treatment, forecasted average N2O emissions from the 16 local administrative districts were estimated to increase by 10%-153% compared to past average CH4 emissions except for Daejeon (-60%), Seoul (-4.0%), and Gwangju (-0.2%). With the carbon dioxide equivalent emissions (CO2-Eq), forecasted average CO2-Eq from the 16 local administrative districts were estimated to increase by 31%-120% compared to past average CH4 emissions except Daejeon (-65%), Seoul (-24%), Busan (-18%), Gwangju (-8%) and Gyeonggi (-1%). The decreased CO2-Eq from 5 local administrative districts was only 34 kt, which was insignificantly small compared to increase of 2,809 kt from other 11 local administrative districts. Annual growth rates of enteric CH4 emissions, CH4 and N2O emissions from manure management in Korea from 1990 to 2009 were 1.7%, 2.6%, and 3.2%, respectively. The annual growth rate of total CO2-Eq was 2.2%. Efforts by the local administrative offices to improve the accuracy of activity data are essential to improve GHG inventories. Direct measurements of GHG emissions from enteric fermentation and manure treatment systems will further enhance the accuracy of the GHG data. (Key Words: Greenhouse Gas, Methane, Nitrous Oxide, Carbon Dioxide Equivalent Emission, Climate Change).

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

What is the relative role of initial hydrological conditions and meteorological forcing to the seasonal hydrological forecasting skill? Analysis along Europe's hydro-climatic gradient

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; Crochemore, Louise

2017-04-01

Recent advances in understanding and forecasting of climate have led into skilful seasonal meteorological predictions, which can consequently increase the confidence of hydrological prognosis. The majority of seasonal impact modelling has commonly been conducted at only one or a limited number of basins limiting the potential to understand large systems. Nevertheless, there is a necessity to develop operational seasonal forecasting services at the pan-European scale, capable of addressing the end-user needs. The skill of such forecasting services is subject to a number of sources of uncertainty, i.e. model structure, parameters, and forcing input. In here, we complement the "deep" knowledge from basin based modelling by investigating the relative contributions of initial hydrological conditions (IHCs) and meteorological forcing (MF) to the skill of a seasonal pan-European hydrological forecasting system. We use the Ensemble Streamflow Prediction (ESP) and reverse ESP (revESP) procedure to show a proxy of hydrological forecasting uncertainty due to MF and IHC uncertainties respectively. We further calculate the critical lead time (CLT), as a proxy of the river memory, after which the importance of MFs surpasses the importance of IHCs. We analyze these results in the context of prevailing hydro-climatic conditions for about 35000 European basins. Both model state initialisation (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth) and provision of climatology are based on forcing input derived from the WFDEI product for the period 1981-2010. The analysis shows that the contribution of ICs and MFs to the hydrological forecasting skill varies considerably according to location, season and lead time. This analysis allows clustering of basins in which hydrological forecasting skill may be improved by better estimation of IHCs, e.g. via data assimilation of in-situ and/or satellite observations; whereas in other basins skill improvement depends on better MFs.

Peak Wind Tool for General Forecasting

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III

2010-01-01

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples.

Treesearch

Richard R. Mason; Donald W. Scott; H. Gene Paul

1993-01-01

A predictive technique using a simple linear regression was developed to forecast the midcrown density of small tussock moth larvae from estimates of cocoon density in the previous generation. The regression estimator was derived from field samples of cocoons and larvae taken from a wide range of nonoutbreak tussock moth populations. The accuracy of the predictions was...

Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

ERIC Educational Resources Information Center

Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

2017-01-01

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.

PubMed

Yang, Wan; Olson, Donald R; Shaman, Jeffrey

2016-11-01

The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.

Blending forest fire smoke forecasts with observed data can improve their utility for public health applications

NASA Astrophysics Data System (ADS)

Yuchi, Weiran; Yao, Jiayun; McLean, Kathleen E.; Stull, Roland; Pavlovic, Radenko; Davignon, Didier; Moran, Michael D.; Henderson, Sarah B.

2016-11-01

Fine particulate matter (PM2.5) generated by forest fires has been associated with a wide range of adverse health outcomes, including exacerbation of respiratory diseases and increased risk of mortality. Due to the unpredictable nature of forest fires, it is challenging for public health authorities to reliably evaluate the magnitude and duration of potential exposures before they occur. Smoke forecasting tools are a promising development from the public health perspective, but their widespread adoption is limited by their inherent uncertainties. Observed measurements from air quality monitoring networks and remote sensing platforms are more reliable, but they are inherently retrospective. It would be ideal to reduce the uncertainty in smoke forecasts by integrating any available observations. This study takes spatially resolved PM2.5 estimates from an empirical model that integrates air quality measurements with satellite data, and averages them with PM2.5 predictions from two smoke forecasting systems. Two different indicators of population respiratory health are then used to evaluate whether the blending improved the utility of the smoke forecasts. Among a total of six models, including two single forecasts and four blended forecasts, the blended estimates always performed better than the forecast values alone. Integrating measured observations into smoke forecasts could improve public health preparedness for smoke events, which are becoming more frequent and intense as the climate changes.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

PubMed

Liu, Yan; Watson, Stella C; Gettings, Jenna R; Lund, Robert B; Nordone, Shila K; Yabsley, Michael J; McMahan, Christopher S

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Forecasting Japanese encephalitis incidence from historical morbidity patterns: Statistical analysis with 27 years of observation in Assam, India.

PubMed

Handique, Bijoy K; Khan, Siraj A; Mahanta, J; Sudhakar, S

2014-09-01

Japanese encephalitis (JE) is one of the dreaded mosquito-borne viral diseases mostly prevalent in south Asian countries including India. Early warning of the disease in terms of disease intensity is crucial for taking adequate and appropriate intervention measures. The present study was carried out in Dibrugarh district in the state of Assam located in the northeastern region of India to assess the accuracy of selected forecasting methods based on historical morbidity patterns of JE incidence during the past 22 years (1985-2006). Four selected forecasting methods, viz. seasonal average (SA), seasonal adjustment with last three observations (SAT), modified method adjusting long-term and cyclic trend (MSAT), and autoregressive integrated moving average (ARIMA) have been employed to assess the accuracy of each of the forecasting methods. The forecasting methods were validated for five consecutive years from 2007-2012 and accuracy of each method has been assessed. The forecasting method utilising seasonal adjustment with long-term and cyclic trend emerged as best forecasting method among the four selected forecasting methods and outperformed the even statistically more advanced ARIMA method. Peak of the disease incidence could effectively be predicted with all the methods, but there are significant variations in magnitude of forecast errors among the selected methods. As expected, variation in forecasts at primary health centre (PHC) level is wide as compared to that of district level forecasts. The study showed that adopted forecasting techniques could reasonably forecast the intensity of JE cases at PHC level without considering the external variables. The results indicate that the understanding of long-term and cyclic trend of the disease intensity will improve the accuracy of the forecasts, but there is a need for making the forecast models more robust to explain sudden variation in the disease intensity with detail analysis of parasite and host population dynamics.

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

PubMed

Yun, Jae-Won; Son, Mia

2016-08-01

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

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

PubMed Central

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

2012-01-01

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

Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections.

PubMed

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E

2015-01-01

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010-2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(± 2)% to 36(± 5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(± 5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(± 1) to 40(± 2) with a corresponding increase from 59(± 2)% to 80(± 6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

Short-term sea ice forecasts with the RASM-ESRL coupled model: A testbed for improving simulations of ocean-ice-atmosphere interactions in the marginal ice zone

NASA Astrophysics Data System (ADS)

Solomon, A.; Cox, C. J.; Hughes, M.; Intrieri, J. M.; Persson, O. P. G.

2015-12-01

The dramatic decrease of Arctic sea-ice has led to a new Arctic sea-ice paradigm and to increased commercial activity in the Arctic Ocean. NOAA's mission to provide accurate and timely sea-ice forecasts, as explicitly outlined in the National Ocean Policy and the U.S. National Strategy for the Arctic Region, needs significant improvement across a range of time scales to improve safety for human activity. Unfortunately, the sea-ice evolution in the new Arctic involves the interaction of numerous physical processes in the atmosphere, ice, and ocean, some of which are not yet understood. These include atmospheric forcing of sea-ice movement through stress and stress deformation; atmospheric forcing of sea-ice melt and formation through energy fluxes; and ocean forcing of the atmosphere through new regions of seasonal heat release. Many of these interactions involve emerging complex processes that first need to be understood and then incorporated into forecast models in order to realize the goal of useful sea-ice forecasting. The underlying hypothesis for this study is that errors in simulations of "fast" atmospheric processes significantly impact the forecast of seasonal sea-ice retreat in summer and its advance in autumn in the marginal ice zone (MIZ). We therefore focus on short-term (0-20 day) ice-floe movement, the freeze-up and melt-back processes in the MIZ, and the role of storms in modulating stress and heat fluxes. This study uses a coupled ocean-atmosphere-seaice forecast model as a testbed to investigate; whether ocean-sea ice-atmosphere coupling improves forecasts on subseasonal time scales, where systematic biases develop due to inadequate parameterizations (focusing on mixed-phase clouds and surface fluxes), how increased atmospheric resolution of synoptic features improves the forecasts, and how initialization of sea ice area and thickness and snow depth impacts the skill of the forecasts. Simulations are validated with measurements at pan-Arctic land sites, satellite data, and recent ocean field campaigns.

Forecasting outdoor recreation participation: a cohort-component projection model for Illinois residents

Treesearch

John F. Dwyer

1995-01-01

Population projections for Illinois predicts lower growth, an older population, and increased racial diversity. If percent of the population participating in outdoor recreation activities by age and race remains at present levels, cohort-component projection models suggest that with projected changes in the population between 1990 and 2025, the number of Illinois...

An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Tampis, R. L.; E Atienza, JB

2017-03-01

This study aims to formulate a mathematical model for forecasting and estimating unemployment rate in the Philippines. Also, factors which can predict the unemployment is to be determined among the considered variables namely Labor Force Rate, Population, Inflation Rate, Gross Domestic Product, and Gross National Income. Granger-causal relationship and integration among the dependent and independent variables are also examined using Pairwise Granger-causality test and Johansen Cointegration Test. The data used were acquired from the Philippine Statistics Authority, National Statistics Office, and Bangko Sentral ng Pilipinas. Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79. The actual values are 99 percent identical to the predicted values obtained through the model, and are 72 percent closely relative to the forecasted ones. According to the results of the regression analysis, Labor Force Rate and Population are the significant factors of unemployment rate. Among the independent variables, Population, GDP, and GNI showed to have a granger-causal relationship with unemployment. It is also found that there are at least four cointegrating relations between the dependent and independent variables.

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions

PubMed Central

Gan, Ryan W.; Ford, Bonne; Lassman, William; Pfister, Gabriele; Vaidyanathan, Ambarish; Fischer, Emily; Volckens, John; Pierce, Jeffrey R.; Magzamen, Sheryl

2017-01-01

Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM2.5) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM2.5. A 10 μg/m3 increase in GWR smoke PM2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019–1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM2.5 exposure method: a 10 μg/m3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026–1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931–1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study. PMID:28868515

Sources and Sinks: Elucidating Mechanisms, Documenting Patterns, and Forecasting Impacts

DTIC Science & Technology

2017-01-18

Molecular Ecology 17: 3628-3639. Fazio III, V. W., Miles, D. B., & White, M. M. 2004. Genetic differentiation in the endangered Black-capped Vireo...exploration of accuracy and power. Molecular Ecology 13: 55–65. Raymond, M., & Rousset, F. 1995. GENEPOP (version 1.2): population genetics software for...SUPPLEMENTAL GENETICS MEMO Sources and Sinks: Elucidating Mechanisms, Documenting Patterns, and Forecasting Impacts SERDP Project RC-2120

Forecasting the Range-wide Status of Polar Bears at Selected Times in the 21st Century

Treesearch

Steven C. Amstrup; Bruce G. Marcot; David C. Douglas

2007-01-01

To inform the U.S. Fish and Wildlife Service decision whether or not to list polar bears as threatened under the Endangered Species Act (ESA), we forecast the status of the world's polar bear (Ursus maritimus) populations 45, 75 and 100 years into the future. We applied the best available information about predicted changes in sea ice in the...

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions.

PubMed

Mogasale, Vittal; Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-09-02

A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy.

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions

PubMed Central

Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-01-01

ABSTRACT A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy. PMID:28604164

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

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea

PubMed Central

Graham, Matthew; Suk, Jonathan E.; Takahashi, Saki; Metcalf, C. Jessica; Jimenez, A. Paez; Prikazsky, Vladimir; Ferrari, Matthew J.; Lessler, Justin

2018-01-01

Abstract. We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response. PMID:29532773

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea.

PubMed

Graham, Matthew; Suk, Jonathan E; Takahashi, Saki; Metcalf, C Jessica; Jimenez, A Paez; Prikazsky, Vladimir; Ferrari, Matthew J; Lessler, Justin

2018-05-01

We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response.

Seismic Forecasting of Eruptions at Dormant StratoVolcanoes

NASA Astrophysics Data System (ADS)

White, R. A.

2015-12-01

Seismic monitoring data provide important constraints on tracking magmatic ascent and eruption. Based on direct experience with over 25 and review of over 10 additional eruption sequences at 24 volcanoes, we have identified 4 phases of precursory seismicity. 1) Deep (>20 km) low frequency (DLF) earthquakes occur near the base of the crust as magma rises toward crustal reservoirs. This seismicity is the most difficult to observe, owing to generally small magnitudes (M<2.5) the significant depth. 2) Distal volcano-tectonic (DVT) earthquakes occur on tectonic faults from a 2 to 30+ km distance laterally from (not beneath) the eventual eruption site as magma intrudes into and rises out of upper crustal reservoirs to depths of 2-3 km. A survey of 111 eruptions of 83 previously dormant volcanoes, (including all eruptions of VEI >4 since 1955) shows they were all preceded by significant DVT seismicity, usually felt. This DVT seismicity is easily observed owing to magnitudes generally reaching M>3.5. The cumulative DVT energy correlates to the intruding magma volume. 3) Low frequency (LF) earthquakes, LF tremor and contained explosions occur as magma interacts with the shallow hydrothermal system (<2 km depth), while the distal seismicity dies off.4) Shortly after this, repetitive self-similar proximal seismicity may occur and may dominate the seismic records as magma rises to the surface. We present some examples of this seismic progression to demonstrate that data from a single short-period vertical station are often sufficient to forecast eruption onsets.

Modelling and forecasting 3D-hypocentre seismicity in the Kanto region

NASA Astrophysics Data System (ADS)

Guo, Yicun; Zhuang, Jiancang; Hirata, Naoshi

2018-04-01

This study analyses the seismicity in the Kanto region by fitting the 2D-epicentre and 3D-hypocentre ETAS models to the JMA catalogue for events above magnitude M4.0. In the 3D ETAS model, the focal depth is assumed to follow the beta distribution. Compared with results from the 2D-epicentre ETAS model, the 3D ETAS model greatly improves the data fitting. In addition, the stochastic reconstruction method is used when validating the results of the 3D ETAS model, with results indicating that the shallow events are more productive and their aftershocks decay slightly faster in the time and epicentre dimensions. We also study the changes of seismicity patterns before and after the 2011 Tohoku earthquake. The direct aftershocks of events from the post-Tohoku period are more diffusive in time and epicentre but more concentrated in depth. The seismicity rate increases significantly following the Tohoku earthquake, especially along the interface of the subducting Pacific plate. The curve of cumulative background probabilities for events above M4.0 implies that the background rate decays back to the pre-Tohoku level in about 5 years after the Tohoku earthquake. However, the occurrence rates of smaller events (from M2.0 to M4.0) indicate that the adjustments of local stress field continue at finer scales. Finally, we verify that the 3D model can reproduce the focal depths better than the 2D model and improve the forecasting performance.

Implementations of the Navy Coupled Ocean Data Assimilation System at the Naval Oceanographic Office

DTIC Science & Technology

2010-06-01

Clim ( GDEM ) +−2std = 95.4% GDEM POE at Depth MODAS Synthetic Profile T,S with Sat SST Local OI of Nearby Valid Data Global3D Analysis Fig. 3. NCODA...observation (Obs), NCODA analysis (Anal), RNCOM nowcast (NCST) for today, RNCOM 24–hour forecast (FCST) from yesterday, GDEM climatology (Clim), and the

An operational procedure for rapid flood risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

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.

Operational use of the AIRS Total Column Ozone Retrievals along with the RGB Airmass Product as Part of the GOES-R Proving Ground

NASA Technical Reports Server (NTRS)

Folmer, M.; Zavodsky, Bradley; Molthan, Andrew

2012-01-01

The Red, Green, Blue (RGB) Air Mass product has been demonstrated in the GOES ]R Proving Ground as a possible decision aid. Forecasters have been trained on the usefulness of identifying stratospheric intrusions and potential vorticity (PV) anomalies that can lead to explosive cyclogenesis, genesis of mesoscale convective systems (MCSs), or the transition of tropical cyclones to extratropical cyclones. It has also been demonstrated to distinguish different air mass types from warm, low ozone air masses to cool, high ozone air masses and the various interactions with the PV anomalies. To assist the forecasters in understanding the stratospheric contribution to high impact weather systems, the Atmospheric Infrared Sounder (AIRS) Total Column Ozone Retrievals have been made available as an operational tool. These AIRS retrievals provide additional information on the amount of ozone that is associated with the red coloring seen in the RGB Air Mass product. This paper discusses how the AIRS retrievals can be used to quantify the red coloring in RGB Air Mass product. These retrievals can be used to diagnose the depth of the stratospheric intrusions associated with different types of weather systems and provide the forecasters decision aid tools that can improve the quality of forecast products.

The Development of New Solar Indices for use in Thermospheric Density Modeling

NASA Technical Reports Server (NTRS)

Tobiska, W. Kent; Bouwer, S. Dave; Bowman, Bruce R.

2006-01-01

New solar indices have been developed to improve thermospheric density modeling for research and operational purposes. Out of 11 new and 4 legacy indices and proxies, we have selected three (F10.7, S10.7, and M10.7) for use in the new JB2006 empirical thermospheric density model. In this work, we report on the development of these solar irradiance indices. The rationale for their use, their definitions, and their characteristics, including the ISO 21348 spectral category and sub-category, wavelength range, solar source temperature region, solar source feature, altitude region of terrestrial atmosphere absorption at unit optical depth, and terrestrial atmosphere thermal processes in the region of maximum energy absorption, are described. We also summarize for each solar index, the facility and instrument(s) used to observe the solar emission, the time frame over which the data exist, the measurement cadence, the data latency, and the research as well as operational availability. The new solar indices are provided in forecast (http://SpaceWx.com) as well as real-time and historical (http://sol.spacenvironment.net/jb2006/) time frames. We describe the forecast methodology, compare results with actual data for active and quiet solar conditions, and compare improvements in F10.7 forecasting with legacy High Accuracy Satellite Drag Model (HASDM) and NOAA SEC forecasts.

Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

NASA Astrophysics Data System (ADS)

Kauker, F.; Kaminski, T.; Ricker, R.; Toudal-Pedersen, L.; Dybkjaer, G.; Melsheimer, C.; Eastwood, S.; Sumata, H.; Karcher, M.; Gerdes, R.

2015-10-01

The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; Funk, Christopher C.; Hoell, Andrew

2014-01-01

In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Earthquake focal mechanism forecasting in Italy for PSHA purposes

NASA Astrophysics Data System (ADS)

Roselli, Pamela; Marzocchi, Warner; Mariucci, Maria Teresa; Montone, Paola

2018-01-01

In this paper, we put forward a procedure that aims to forecast focal mechanism of future earthquakes. One of the primary uses of such forecasts is in probabilistic seismic hazard analysis (PSHA); in fact, aiming at reducing the epistemic uncertainty, most of the newer ground motion prediction equations consider, besides the seismicity rates, the forecast of the focal mechanism of the next large earthquakes as input data. The data set used to this purpose is relative to focal mechanisms taken from the latest stress map release for Italy containing 392 well-constrained solutions of events, from 1908 to 2015, with Mw ≥ 4 and depths from 0 down to 40 km. The data set considers polarity focal mechanism solutions until to 1975 (23 events), whereas for 1976-2015, it takes into account only the Centroid Moment Tensor (CMT)-like earthquake focal solutions for data homogeneity. The forecasting model is rooted in the Total Weighted Moment Tensor concept that weighs information of past focal mechanisms evenly distributed in space, according to their distance from the spatial cells and magnitude. Specifically, for each cell of a regular 0.1° × 0.1° spatial grid, the model estimates the probability to observe a normal, reverse, or strike-slip fault plane solution for the next large earthquakes, the expected moment tensor and the related maximum horizontal stress orientation. These results will be available for the new PSHA model for Italy under development. Finally, to evaluate the reliability of the forecasts, we test them with an independent data set that consists of some of the strongest earthquakes with Mw ≥ 3.9 occurred during 2016 in different Italian tectonic provinces.

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

NASA Technical Reports Server (NTRS)

Yang, Shu-Chih; Rienecker, Michele; Keppenne, Christian

2010-01-01

This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

Development of Parallel Code for the Alaska Tsunami Forecast Model

NASA Astrophysics Data System (ADS)

Bahng, B.; Knight, W. R.; Whitmore, P.

2014-12-01

The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes and other means in both the Pacific and Atlantic Oceans. At the U.S. National Tsunami Warning Center (NTWC), the model is mainly used in a pre-computed fashion. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves get closer to coastal waters. Even with the pre-computation the task becomes non-trivial as sub-grid resolution gets finer. Currently, the finest resolution Digital Elevation Models (DEM) used by ATFM are 1/3 arc-seconds. With a serial code, large or multiple areas of very high resolution can produce run-times that are unrealistic even in a pre-computed approach. One way to increase the model performance is code parallelization used in conjunction with a multi-processor computing environment. NTWC developers have undertaken an ATFM code-parallelization effort to streamline the creation of the pre-computed database of results with the long term aim of tsunami forecasts from source to high resolution shoreline grids in real time. Parallelization will also permit timely regeneration of the forecast model database with new DEMs; and, will make possible future inclusion of new physics such as the non-hydrostatic treatment of tsunami propagation. The purpose of our presentation is to elaborate on the parallelization approach and to show the compute speed increase on various multi-processor systems.

Validation of an ocean shelf model for the prediction of mixed-layer properties in the Mediterranean Sea west of Sardinia

NASA Astrophysics Data System (ADS)

Onken, Reiner

2017-04-01

The Regional Ocean Modeling System (ROMS) has been employed to explore the sensitivity of the forecast skill of mixed-layer properties to initial conditions, boundary conditions, and vertical mixing parameterisations. The initial and lateral boundary conditions were provided by the Mediterranean Forecasting System (MFS) or by the MERCATOR global ocean circulation model via one-way nesting; the initial conditions were additionally updated through the assimilation of observations. Nowcasts and forecasts from the weather forecast models COSMO-ME and COSMO-IT, partly melded with observations, served as surface boundary conditions. The vertical mixing was parameterised by the GLS (generic length scale) scheme Umlauf and Burchard (2003) in four different set-ups. All ROMS forecasts were validated against the observations which were taken during the REP14-MED survey to the west of Sardinia. Nesting ROMS in MERCATOR and updating the initial conditions through data assimilation provided the best agreement of the predicted mixed-layer properties with the time series from a moored thermistor chain. Further improvement was obtained by the usage of COSMO-ME atmospheric forcing, which was melded with real observations, and by the application of the k-ω vertical mixing scheme with increased vertical eddy diffusivity. The predicted temporal variability of the mixed-layer temperature was reasonably well correlated with the observed variability, while the modelled variability of the mixed-layer depth exhibited only agreement with the observations near the diurnal frequency peak. For the forecasted horizontal variability, reasonable agreement was found with observations from a ScanFish section, but only for the mesoscale wave number band; the observed sub-mesoscale variability was not reproduced by ROMS.

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City

PubMed Central

2016-01-01

The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast. PMID:27855155

Forecasting the impact of transport improvements on commuting and residential choice

NASA Astrophysics Data System (ADS)

Elhorst, J. Paul; Oosterhaven, Jan

2006-03-01

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

In Brief: Forecasting meningitis threats

NASA Astrophysics Data System (ADS)

Showstack, Randy

2008-12-01

The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.

Increased Accuracy in Statistical Seasonal Hurricane Forecasting

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

PubMed Central

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

PubMed

Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M

2014-10-01

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

A forecast experiment of earthquake activity in Japan under Collaboratory for the Study of Earthquake Predictability (CSEP)

NASA Astrophysics Data System (ADS)

Hirata, N.; Yokoi, S.; Nanjo, K. Z.; Tsuruoka, H.

2012-04-01

One major focus of the current Japanese earthquake prediction research program (2009-2013), which is now integrated with the research program for prediction of volcanic eruptions, is to move toward creating testable earthquake forecast models. For this purpose we started an experiment of forecasting earthquake activity in Japan under the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP) through an international collaboration. We established the CSEP Testing Centre, an infrastructure to encourage researchers to develop testable models for Japan, and to conduct verifiable prospective tests of their model performance. We started the 1st earthquake forecast testing experiment in Japan within the CSEP framework. We use the earthquake catalogue maintained and provided by the Japan Meteorological Agency (JMA). The experiment consists of 12 categories, with 4 testing classes with different time spans (1 day, 3 months, 1 year, and 3 years) and 3 testing regions called "All Japan," "Mainland," and "Kanto." A total of 105 models were submitted, and are currently under the CSEP official suite of tests for evaluating the performance of forecasts. The experiments were completed for 92 rounds for 1-day, 6 rounds for 3-month, and 3 rounds for 1-year classes. For 1-day testing class all models passed all the CSEP's evaluation tests at more than 90% rounds. The results of the 3-month testing class also gave us new knowledge concerning statistical forecasting models. All models showed a good performance for magnitude forecasting. On the other hand, observation is hardly consistent in space distribution with most models when many earthquakes occurred at a spot. Now we prepare the 3-D forecasting experiment with a depth range of 0 to 100 km in Kanto region. The testing center is improving an evaluation system for 1-day class experiment to finish forecasting and testing results within one day. The special issue of 1st part titled Earthquake Forecast Testing Experiment in Japan was published on the Earth, Planets and Space Vol. 63, No.3, 2011 on March, 2011. The 2nd part of this issue, which is now on line, will be published soon. An outline of the experiment and activities of the Japanese Testing Center are published on our WEB site; http://wwweic.eri.u-tokyo.ac.jp/ZISINyosoku/wiki.en/wiki.cgi

Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas

NASA Astrophysics Data System (ADS)

Federico, Ivan; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Lecci, Rita; Mossa, Michele

2017-01-01

SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System) is a coastal-ocean operational system based on the unstructured grid finite-element three-dimensional hydrodynamic SHYFEM model, providing short-term forecasts. The operational chain is based on a downscaling approach starting from the large-scale system for the entire Mediterranean Basin (MFS, Mediterranean Forecasting System), which provides initial and boundary condition fields to the nested system. The model is configured to provide hydrodynamics and active tracer forecasts both in open ocean and coastal waters of southeastern Italy using a variable horizontal resolution from the open sea (3-4 km) to coastal areas (50-500 m). Given that the coastal fields are driven by a combination of both local (also known as coastal) and deep-ocean forcings propagating along the shelf, the performance of SANIFS was verified both in forecast and simulation mode, first (i) on the large and shelf-coastal scales by comparing with a large-scale survey CTD (conductivity-temperature-depth) in the Gulf of Taranto and then (ii) on the coastal-harbour scale (Mar Grande of Taranto) by comparison with CTD, ADCP (acoustic doppler current profiler) and tide gauge data. Sensitivity tests were performed on initialization conditions (mainly focused on spin-up procedures) and on surface boundary conditions by assessing the reliability of two alternative datasets at different horizontal resolution (12.5 and 6.5 km). The SANIFS forecasts at a lead time of 1 day were compared with the MFS forecasts, highlighting that SANIFS is able to retain the large-scale dynamics of MFS. The large-scale dynamics of MFS are correctly propagated to the shelf-coastal scale, improving the forecast accuracy (+17 % for temperature and +6 % for salinity compared to MFS). Moreover, the added value of SANIFS was assessed on the coastal-harbour scale, which is not covered by the coarse resolution of MFS, where the fields forecasted by SANIFS reproduced the observations well (temperature RMSE equal to 0.11 °C). Furthermore, SANIFS simulations were compared with hourly time series of temperature, sea level and velocity measured on the coastal-harbour scale, showing a good agreement. Simulations in the Gulf of Taranto described a circulation mainly characterized by an anticyclonic gyre with the presence of cyclonic vortexes in shelf-coastal areas. A surface water inflow from the open sea to Mar Grande characterizes the coastal-harbour scale.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan

PubMed Central

Chen, Brian K.; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-01-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan’s future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model – the Japanese Future Elderly Model (FEM) – for Japan. We use the model to forecast disability and health for Japan’s future elderly. Our simulation suggests that by 2040, over 27 percent of Japan’s elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan’s future. PMID:28580275

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States

PubMed Central

Liu, Yan; Watson, Stella C.; Gettings, Jenna R.; Lund, Robert B.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. PMID:28738085

Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

NASA Astrophysics Data System (ADS)

Pulwarty, Roger S.; Redmond, Kelly T.

1997-03-01

The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

Medium- and long-term electric power demand forecasting based on the big data of smart city

NASA Astrophysics Data System (ADS)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada

EPA Science Inventory

A Beneficial Use Impairment (BUI) common at Great Lakes Areas of Concern (AOCs) is loss of fish and wildlife populations. Consequently, recovery of populations after stressor mitigation serves as a basis for evaluating remediation success. We describe a framework that can be a...

Application and Validation of a GIS Model for Local Tsunami Vulnerability and Mortality Risk Analysis

NASA Astrophysics Data System (ADS)

Harbitz, C. B.; Frauenfelder, R.; Kaiser, G.; Glimsdal, S.; Sverdrup-thygeson, K.; Løvholt, F.; Gruenburg, L.; Mc Adoo, B. G.

2015-12-01

The 2011 Tōhoku tsunami caused a high number of fatalities and massive destruction. Data collected after the event allow for retrospective analyses. Since 2009, NGI has developed a generic GIS model for local analyses of tsunami vulnerability and mortality risk. The mortality risk convolves the hazard, exposure, and vulnerability. The hazard is represented by the maximum tsunami flow depth (with a corresponding likelihood), the exposure is described by the population density in time and space, while the vulnerability is expressed by the probability of being killed as a function of flow depth and building class. The analysis is further based on high-resolution DEMs. Normally a certain tsunami scenario with a corresponding return period is applied for vulnerability and mortality risk analysis. Hence, the model was first employed for a tsunami forecast scenario affecting Bridgetown, Barbados, and further developed in a forecast study for the city of Batangas in the Philippines. Subsequently, the model was tested by hindcasting the 2009 South Pacific tsunami in American Samoa. This hindcast was based on post-tsunami information. The GIS model was adapted for optimal use of the available data and successfully estimated the degree of mortality.For further validation and development, the model was recently applied in the RAPSODI project for hindcasting the 2011 Tōhoku tsunami in Sendai and Ishinomaki. With reasonable choices of building vulnerability, the estimated expected number of fatalities agree well with the reported death toll. The results of the mortality hindcast for the 2011 Tōhoku tsunami substantiate that the GIS model can help to identify high tsunami mortality risk areas, as well as identify the main risk drivers.The research leading to these results has received funding from CONCERT-Japan Joint Call on Efficient Energy Storage and Distribution/Resilience against Disasters (http://www.concertjapan.eu; project RAPSODI - Risk Assessment and design of Prevention Structures fOr enhanced tsunami DIsaster resilience http://www.ngi.no/en/Project-pages/RAPSODI/), and from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 603839 (Project ASTARTE - Assessment, STrategy And Risk reduction for Tsunamis in Europe http://www.astarte-project.eu/).

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.

Megaregion freight movements : a case study of the Texas Triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas with large regional economies forecasted to contain over two-thirds of the national economic activity. In Texas, population growth from 2000 t...

Mega-region freight movements : a case study of the Texas triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas : with large regional economies forecasted to contain over two-thirds of the national economic activity. In : Texas, population growth from 20...

Cod Collapse and the Climate in the North Atlantic

NASA Astrophysics Data System (ADS)

Meng, K. C.; Oremus, K. L.; Gaines, S.

2014-12-01

Effective fisheries management requires forecasting population changes. We find a negative relationship between the North Atlantic Oscillation (NAO) index and subsequently surveyed biomass and catch of Atlantic cod, Gadus morhua, off the New England coast. A 1-unit NAO increase is associated with a 17% decrease in surveyed biomass of age-1 cod the following year. This relationship persists as the cod mature, such that observed NAO can be used to forecast future adult biomass. We also document that an NAO event lowers catch for up to 15 years afterward. In contrast to forecasts by existing stock assessment models, our NAO-driven statistical model successfully hindcasts the recent collapse of New England cod fisheries following strong NAO events in 2007 and 2008 (see figure). This finding can serve as a template for forecasting other fisheries affected by climatic conditions.

Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

2017-04-01

Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive uncertainty properly. Additionally for the key locations at the international waterways Rhine, Elbe and Danube three competing forecast approaches are currently tested in a pre-operational set-up in order to generate monthly to seasonal (up to 3 months) forecasts: (1) the well-known Ensemble Streamflow Prediction approach (ensemble based on historical meteorology), (2) coupling hydrological models with post-processed outputs from ECMWF's general circulation model (System 4), and (3) a purely statistical approach based on the stable relationship (teleconnection) of global or regional oceanic, climate and hydrological data with river flows. The current results, still pre-operational, reveal the existence of a valuable predictability of water levels and streamflow also at monthly up to seasonal time-scales along the larger rivers used as waterways in Germany. Last but not least insight into the technical set-up of the aforementioned forecasting systems operated at the Federal Institute of Hydrology, which are based on a Delft-FEWS application, will be given focussing on the step-wise extension of the former system by integrating new components in order to meet the growing needs of the customers and to improve and extend the forecast portfolio for waterway users.

Forecasting Cause-Specific Mortality in Korea up to Year 2032

PubMed Central

2016-01-01

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

Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

NASA Astrophysics Data System (ADS)

Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

2017-06-01

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid over-forecasting could help improve both the quality and the value of forecasts.

Comparison of GEOS-5 AGCM planetary boundary layer depths computed with various definitions

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.

2014-07-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Köppen-Geiger climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number methods are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

Comparison of GEOS-5 AGCM Planetary Boundary Layer Depths Computed with Various Definitions

NASA Technical Reports Server (NTRS)

Mcgrath-Spangler, E. L.; Molod, A.

2014-01-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Koppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

Comparison of GEOS-5 AGCM planetary boundary layer depths computed with various definitions

NASA Astrophysics Data System (ADS)

McGrath-Spangler, E. L.; Molod, A.

2014-03-01

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Köppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior.

The climatic effect of explosive volcanic activity: Analysis of the historical data

NASA Technical Reports Server (NTRS)

Bryson, R. A.; Goodman, B. M.

1982-01-01

By using the most complete available records of direct beam radiation and volcanic eruptions, an historical analysis of the role of the latter in modulating the former was made. A very simple fallout and dispersion model was applied to the historical chronology of explosive eruptions. The resulting time series explains about 77 percent of the radiation variance, as well as suggests that tropical and subpolar eruptions are more important than mid-latitude eruptions in their impact on the stratospheric aerosol optical depth. The simpler climatic models indicate that past hemispheric temperature can be stimulated very well with volcanic and CO2 inputs and suggest that climate forecasting will also require volcano forecasting. There is some evidence that this is possible some years in advance.

Identified EM Earthquake Precursors

NASA Astrophysics Data System (ADS)

Jones, Kenneth, II; Saxton, Patrick

2014-05-01

Many attempts have been made to determine a sound forecasting method regarding earthquakes and warn the public in turn. Presently, the animal kingdom leads the precursor list alluding to a transmission related source. By applying the animal-based model to an electromagnetic (EM) wave model, various hypotheses were formed, but the most interesting one required the use of a magnetometer with a differing design and geometry. To date, numerous, high-end magnetometers have been in use in close proximity to fault zones for potential earthquake forecasting; however, something is still amiss. The problem still resides with what exactly is forecastable and the investigating direction of EM. After a number of custom rock experiments, two hypotheses were formed which could answer the EM wave model. The first hypothesis concerned a sufficient and continuous electron movement either by surface or penetrative flow, and the second regarded a novel approach to radio transmission. Electron flow along fracture surfaces was determined to be inadequate in creating strong EM fields, because rock has a very high electrical resistance making it a high quality insulator. Penetrative flow could not be corroborated as well, because it was discovered that rock was absorbing and confining electrons to a very thin skin depth. Radio wave transmission and detection worked with every single test administered. This hypothesis was reviewed for propagating, long-wave generation with sufficient amplitude, and the capability of penetrating solid rock. Additionally, fracture spaces, either air or ion-filled, can facilitate this concept from great depths and allow for surficial detection. A few propagating precursor signals have been detected in the field occurring with associated phases using custom-built loop antennae. Field testing was conducted in Southern California from 2006-2011, and outside the NE Texas town of Timpson in February, 2013. The antennae have mobility and observations were noted for recurrence, duration, and frequency response. At the Southern California field sites, one loop antenna was positioned for omni-directional reception and also detected a strong First Schumann Resonance; however, additional Schumann Resonances were absent. At the Timpson, TX field sites, loop antennae were positioned for directional reception, due to earthquake-induced, hydraulic fracturing activity currently conducted by the oil and gas industry. Two strong signals, one moderately strong signal, and approximately 6-8 weaker signals were detected in the immediate vicinity. The three stronger signals were mapped by a biangulation technique, followed by a triangulation technique for confirmation. This was the first antenna mapping technique ever performed for determining possible earthquake epicenters. Six and a half months later, Timpson experienced two M4 (M4.1 and M4.3) earthquakes on September 2, 2013 followed by a M2.4 earthquake three days later, all occurring at a depth of five kilometers. The Timpson earthquake activity now has a cyclical rate and a forecast was given to the proper authorities. As a result, the Southern California and Timpson, TX field results led to an improved design and construction of a third prototype antenna. With a loop antenna array, a viable communication system, and continuous monitoring, a full fracture cycle can be established and observed in real-time. In addition, field data could be reviewed quickly for assessment and lead to a much more improved earthquake forecasting capability. The EM precursors determined by this method appear to surpass all prior precursor claims, and the general public will finally receive long overdue forecasting.

Computer simulation of the coffee leaf miner using sexual Penna aging model

NASA Astrophysics Data System (ADS)

de Oliveira, A. C. S.; Martins, S. G. F.; Zacarias, M. S.

2008-01-01

Forecast models based on climatic conditions are of great interest in Integrated Pest Management (IPM) programs. The success of these models depends, among other factors, on the knowledge of the temperature effect on the pests’ population dynamics. In this direction, a computer simulation was made for the population dynamics of the coffee leaf miner, L. coffeella, at different temperatures, considering experimental data relative to the pest. The age structure was inserted into the dynamics through sexual Penna Model. The results obtained, such as life expectancy, growth rate and annual generations’ number, in agreement to those in laboratory and field conditions, show that the simulation can be used as a forecast model for controlling L. coffeella.

Fusion of real-time simulation, sensing, and geo-informatics in assessing tsunami impact

NASA Astrophysics Data System (ADS)

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

2015-12-01

Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we establish a method of real-time tsunami inundation forecasting, damage estimation and mapping to enhance disaster response. Right after a major (near field) earthquake is triggered, we perform a real-time tsunami inundation forecasting with use of high-performance computing platform (Koshimura et al., 2014). Using Tohoku University's vector supercomputer, we accomplished "10-10-10 challenge", to complete tsunami source determination in 10 minutes, tsunami inundation modeling in 10 minutes with 10 m grid resolution. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data and mobile phone data, and the numbers of potential death and damaged structures by applying tsunami fragility curve. After the potential tsunami-affected areas are estimated, the analysis gets focused and moves on to the "detection" phase using remote sensing. Recent advances of remote sensing technologies expand capabilities of detecting spatial extent of tsunami affected area and structural damage. Especially, a semi-automated method to estimate building damage in tsunami affected areas is developed using pre- and post-event high-resolution SAR (Synthetic Aperture Radar) data. The method is verified through the case studies in the 2011 Tohoku and other potential tsunami scenarios, and the prototype system development is now underway in Kochi prefecture, one of at-risk coastal city against Nankai trough earthquake. In the trial operation, we verify the capability of the method as a new tsunami early warning and response system for stakeholders and responders.

An operational global ocean forecast system and its applications

NASA Astrophysics Data System (ADS)

Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

2012-12-01

A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

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

USGS Publications Warehouse

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

2008-01-01

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

On-line applications of numerical models in the Black Sea GIS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Khaliulin, A.; Zodiatis, G.; Nikolaidis, A.; Nikolaidis, M.; Stylianou, Stavros

2017-09-01

The Black Sea Geographical Information System (GIS) is developed based on cutting edge information technologies, and provides automated data processing and visualization on-line. Mapserver is used as a mapping service; the data are stored in MySQL DBMS; PHP and Python modules are utilized for data access, processing, and exchange. New numerical models can be incorporated in the GIS environment as individual software modules, compiled for a server-based operational system, providing interaction with the GIS. A common interface allows setting the input parameters; then the model performs the calculation of the output data in specifically predefined files and format. The calculation results are then passed to the GIS for visualization. Initially, a test scenario of integration of a numerical model into the GIS was performed, using software, developed to describe a two-dimensional tsunami propagation in variable basin depth, based on a linear long surface wave model which is legitimate for more than 5 m depth. Furthermore, the well established oil spill and trajectory 3-D model MEDSLIK (http://www.oceanography.ucy.ac.cy/medslik/) was integrated into the GIS with more advanced GIS functionality and capabilities. MEDSLIK is able to forecast and hind cast the trajectories of oil pollution and floating objects, by using meteo-ocean data and the state of oil spill. The MEDSLIK module interface allows a user to enter all the necessary oil spill parameters, i.e. date and time, rate of spill or spill volume, forecasting time, coordinates, oil spill type, currents, wind, and waves, as well as the specification of the output parameters. The entered data are passed on to MEDSLIK; then the oil pollution characteristics are calculated for pre-defined time steps. The results of the forecast or hind cast are then visualized upon a map.

Optimizing measurements of cluster velocities and temperatures for CCAT-prime and future surveys

NASA Astrophysics Data System (ADS)

Mittal, Avirukt; de Bernardis, Francesco; Niemack, Michael D.

2018-02-01

Galaxy cluster velocity correlations and mass distributions are sensitive probes of cosmology and the growth of structure. Upcoming microwave surveys will enable extraction of velocities and temperatures from many individual clusters for the first time. We forecast constraints on peculiar velocities, electron temperatures, and optical depths of galaxy clusters obtainable with upcoming multi-frequency measurements of the kinematic, thermal, and relativistic Sunyaev-Zeldovich effects. The forecasted constraints are compared for different measurement configurations with frequency bands between 90 GHz and 1 THz, and for different survey strategies for the 6-meter CCAT-prime telescope. We study methods for improving cluster constraints by removing emission from dusty star forming galaxies, and by using X-ray temperature priors from eROSITA. Cluster constraints are forecast for several model cluster masses. A sensitivity optimization for seven frequency bands is presented for a CCAT-prime first light instrument and a next generation instrument that takes advantage of the large optical throughput of CCAT-prime. We find that CCAT-prime observations are expected to enable measurement and separation of the SZ effects to characterize the velocity, temperature, and optical depth of individual massive clusters (~1015 Msolar). Submillimeter measurements are shown to play an important role in separating these components from dusty galaxy contamination. Using a modular instrument configuration with similar optical throughput for each detector array, we develop a rule of thumb for the number of detector arrays desired at each frequency to optimize extraction of these signals. Our results are relevant for a future "Stage IV" cosmic microwave background survey, which could enable galaxy cluster measurements over a larger range of masses and redshifts than will be accessible by other experiments.

Long-Term Climate Forcing in Loggerhead Sea Turtle Nesting

PubMed Central

Van Houtan, Kyle S.; Halley, John M.

2011-01-01

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions—such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence. PMID:21589639

Long-term climate forcing in loggerhead sea turtle nesting.

PubMed

Van Houtan, Kyle S; Halley, John M

2011-04-27

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions--such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence.

Population projections for three counties in Zhejiang Province.

PubMed

Zhuang, B; Huang, X

1983-01-01

Using population numbers and deaths in each age group in Yayao, Jiangshan, and Huangyan counties, China, in 1978, the authors analyze current population dynamics and project population trends for the next 20-60 years. The total population of the 3 counties is 2,314,566, with 33.2% 0-14 years old and 5.7% over 65 years old. The dependency ratio is 63.7%. 24.16% of the women are of childbearing age, 15-49 years old. The birth rate averages 15.39% and the mortality rate is 5.91%. Life expectancy is 68.94 for males and 71.94 for females. Males account for 51.6% of the population and females 48.4%, primarily due to the preferential treatment given to male babies. 3 constrictions in the age pyramid reflect conditions caused by the Japanese invasion of 1941-1945, economic policy blunders during the Great Leap Forward and natural disasters, and, most recently, the family planning program. The recent 1 child family policy aims to limit China's total population to 1.2 billion by the year 2000. Achieving this goal requires careful population planning based on actual local conditions. 3 forecasts--based on different combinations of 1 and 2 child families--estimate total birth rates of 1.46, 1.184, and 1.925. These assumptions produce natural increase rates of 5.66%, 7.83%, and 10.64%. All 3 forecasts produce an aging population, but the dependency ratio decreases. China's population policy must be based on the fact that the current population is 1 billion, 800 million of whom are peasants, and that China has too little arable land and is economically undeveloped. The authors consider forecast 1--in which couples have 2 children each from 1981-1985 and half have 1 and half have 2 child families from 1986-2000--the most desirable because 1) it will be acceptable to the peasant population, 2) it maintains a large labor force, 3) it produces a stable age pyramid, and 4) it remains a reasonable possibility.

Forecasting effects of climate change on Great Lakes fisheries: models that link habitat supply to population dynamics can help

USGS Publications Warehouse

Jones, Michael L.; Shuter, Brian J.; Zhao, Yingming; Stockwell, Jason D.

2006-01-01

Future changes to climate in the Great Lakes may have important consequences for fisheries. Evidence suggests that Great Lakes air and water temperatures have risen and the duration of ice cover has lessened during the past century. Global circulation models (GCMs) suggest future warming and increases in precipitation in the region. We present new evidence that water temperatures have risen in Lake Erie, particularly during summer and winter in the period 1965–2000. GCM forecasts coupled with physical models suggest lower annual runoff, less ice cover, and lower lake levels in the future, but the certainty of these forecasts is low. Assessment of the likely effects of climate change on fish stocks will require an integrative approach that considers several components of habitat rather than water temperature alone. We recommend using mechanistic models that couple habitat conditions to population demographics to explore integrated effects of climate-caused habitat change and illustrate this approach with a model for Lake Erie walleye (Sander vitreum). We show that the combined effect on walleye populations of plausible changes in temperature, river hydrology, lake levels, and light penetration can be quite different from that which would be expected based on consideration of only a single factor.

Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

NASA Astrophysics Data System (ADS)

Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, Anctil

2010-05-01

Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrology and Earth System Sciences, 13(11), 2221-2231. Wilks, D.S. and Hamill, T.M. 1995: Potential economic value of ensemble-based surface weather forecasts, Monthly Weather Review, 123(12), 3565-3575.

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

Forecasting carbon dioxide emissions.

PubMed

Zhao, Xiaobing; Du, Ding

2015-09-01

This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

Understanding the Impact of Ground Water Treatment and Evapotranspiration Parameterizations in the NCEP Climate Forecast System (CFS) on Warm Season Predictions

NASA Astrophysics Data System (ADS)

Ek, M. B.; Yang, R.

2016-12-01

Skillful short-term weather forecasts, which rely heavily on quality atmospheric initial conditions, have a fundamental limit of about two weeks owing to the chaotic nature of the atmosphere. Useful forecasts at sub-seasonal to seasonal time scales, on the other hand, require well-simulated large-scale atmospheric response to slowly varying lower boundary forcings from both the ocean and land surface. The critical importance of ocean has been recognized, where the ocean indices have been used in a variety of climate applications. In contrast, the impact of land surface anomalies, especially soil moisture and associated evaporation, has been proven notably difficult to demonstrate. The Noah Land Surface Model (LSM) is the land component of NCEP CFS version 2 (CFSv2) used for seasonal predictions. The Noah LSM originates from the Oregon State University (OSU) LSM. The evaporation control in the Noah LSM is based on the Penman-Monteith equation, which takes into account the solar radiation, relative humidity, air temperature, and soil moisture effects. The Noah LSM is configured with four soil layers with a fixed depth of 2 meters and free drainage at the bottom soil layer. This treatment assumes that the soil water table depth is well within the specified range, and also potentially misrepresents the soil moisture memory effects at seasonal time scales. To overcome the limitation, an unconfined aquifer is attached to the bottom of the soil to allow the water table to move freely up and down. In addition, in conjunction with the water table, an alternative Ball-Berry photosynthesis-based evaporation parameterization is examined to evaluate the impact from using a different evaporation control methodology. Focusing on the 2011 and 2012 intense summer droughts in the central US, seasonal ensemble forecast experiments with early May initial conditions are carried out for the two years using an enhanced version of CFSv2, where the atmospheric component of the CFSv2 is coupled to the Noah Multiple-Parameterization (Noah-MP) land model. The Noah-MP has different options for ground water and evaporation control parameterizations. The differences will be presented and results will be discussed.

Large earthquake rates from geologic, geodetic, and seismological perspectives

NASA Astrophysics Data System (ADS)

Jackson, D. D.

2017-12-01

Earthquake rate and recurrence information comes primarily from geology, geodesy, and seismology. Geology gives the longest temporal perspective, but it reveals only surface deformation, relatable to earthquakes only with many assumptions. Geodesy is also limited to surface observations, but it detects evidence of the processes leading to earthquakes, again subject to important assumptions. Seismology reveals actual earthquakes, but its history is too short to capture important properties of very large ones. Unfortunately, the ranges of these observation types barely overlap, so that integrating them into a consistent picture adequate to infer future prospects requires a great deal of trust. Perhaps the most important boundary is the temporal one at the beginning of the instrumental seismic era, about a century ago. We have virtually no seismological or geodetic information on large earthquakes before then, and little geological information after. Virtually all-modern forecasts of large earthquakes assume some form of equivalence between tectonic- and seismic moment rates as functions of location, time, and magnitude threshold. That assumption links geology, geodesy, and seismology, but it invokes a host of other assumptions and incurs very significant uncertainties. Questions include temporal behavior of seismic and tectonic moment rates; shape of the earthquake magnitude distribution; upper magnitude limit; scaling between rupture length, width, and displacement; depth dependence of stress coupling; value of crustal rigidity; and relation between faults at depth and their surface fault traces, to name just a few. In this report I'll estimate the quantitative implications for estimating large earthquake rate. Global studies like the GEAR1 project suggest that surface deformation from geology and geodesy best show the geography of very large, rare earthquakes in the long term, while seismological observations of small earthquakes best forecasts moderate earthquakes up to about magnitude 7. Regional forecasts for a few decades, like those in UCERF3, could be improved by calibrating tectonic moment rate to past seismicity rates. Century-long forecasts must be speculative. Estimates of maximum magnitude and rate of giant earthquakes over geologic time scales require more than science.

DEA-I: A Globally Configurable Open Source Software Package in Support of Air Quality Forecasts

NASA Astrophysics Data System (ADS)

Davies, J.; Strabala, K.; Pierce, R.; Huang, H.; Schiffer, E.

2012-12-01

During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype for using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth retrievals in daily air quality forecasts; this became known as IDEA (Infusing satellite Data into Environmental Applications). IDEA was part of the NASA Applied Sciences Program strategy to demonstrate practical uses of NASA-sponsored observations from space and predictions. Following its successful demonstration an export version of IDEA, known as IDEA International (IDEA-I), has now been released. IDEA-I supports the Global Earth Observation Systems of Systems (GEOSS) Group on Earth Observations (GEO) Health Societal Benefit Area (SBA) and is being developed within the framework of the GEO Earth Observations in Decision Support Call for Proposals. The vehicle for IDEA-I release is the International MODIS and AIRS (Atmospheric Infrared Sounder) Processing Package (IMAPP), developed at the Space Science and Engineering Center, University of Wisconsin-Madison (SSEC/UW-Madison). IMAPP is a NASA-funded and freely-distributed software package which allows any ground station capable of receiving direct broadcast from Terra or Aqua to produce calibrated and geolocated radiances, and a suite of environmental products, of which the IDEA-I 48-hour forward trajectory prediction of high aerosol events is now a part. IDEA-I provides a tool for linking ground-based and satellite capabilities to support international air quality forecasting activities and is to be demonstrated internationally through user training and impact evaluation via a series of IMAPP workshops. This presentation describes the IMAPP implementation of IDEA-I in terms of its simple installation and configuration, and through examples of its operation in several regions known for periodic high aerosol events.; Screen capture of the University of Wisconsin implementation of the real-time direct broadcast IDEA-I Air Quality monitoring website. This example uses Terra MODIS Aerosol Optical Depth retrievals to identify regions of high aerosol concentrations. A trajectory model is then run that provide a forecast of the horizontal and vertical movement of the aerosols over the next 48 hours.

Prediction of the demographic situation in urban districts as a factor of sustainable social and economic development of the transport infrastructure

NASA Astrophysics Data System (ADS)

Davydova, Tatyana; Zhutaeva, Evgeniya; Dubrovskaya, Tatyana

2017-10-01

Article considers the significance of the demographic forecast for the effective operation of the providing system of social and economic development of the urban transport infrastructure. Analysis of the factors which influence on the population of the city of Voronezh was performed and the population forecast for the year 2020 is presented on the basis of the classification by year of birth. Calculation was performed in three variants (with consideration of the use of classification by year of birth) in connection with an impact of modern social and economic situation on the negative tendencies formed in demographic processes. In the basis of variants were grounded different approaches to the dynamics of demographic processes. The main demographic indicators are the number of permanent residents, birth rates, death rates, migration rates. According to the results of the study, population of the urban district of the city of Voronezh is expected to increase in the specified period and migration inflow of the population has a dominant role in the formation in the formation of the number of the city population.

Probabilistic population aging

PubMed Central

2017-01-01

We merge two methodologies, prospective measures of population aging and probabilistic population forecasts. We compare the speed of change and variability in forecasts of the old age dependency ratio and the prospective old age dependency ratio as well as the same comparison for the median age and the prospective median age. While conventional measures of population aging are computed on the basis of the number of years people have already lived, prospective measures are computed also taking account of the expected number of years they have left to live. Those remaining life expectancies change over time and differ from place to place. We compare the probabilistic distributions of the conventional and prospective measures using examples from China, Germany, Iran, and the United States. The changes over time and the variability of the prospective indicators are smaller than those that are observed in the conventional ones. A wide variety of new results emerge from the combination of methodologies. For example, for Germany, Iran, and the United States the likelihood that the prospective median age of the population in 2098 will be lower than it is today is close to 100 percent. PMID:28636675

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Exploration of Urban Spatial Planning Evaluation Based on Humanland Harmony

NASA Astrophysics Data System (ADS)

Hu, X. S.; Ma, Q. R.; Liang, W. Q.; Wang, C. X.; Xiong, X. Q.; Han, X. H.

2017-09-01

This study puts forward a new concept, "population urbanization level forecast - driving factor analysis - urban spatial planning analysis" for achieving efficient and intensive development of urbanization considering human-land harmony. We analyzed big data for national economic and social development, studied the development trends of population urbanization and its influencing factors using the grey system model in Chengmai county of Hainan province, China. In turn, we calculated the population of Chengmai coming years based on the forecasting urbanization rate and the corresponding amount of urban construction land, and evaluated the urban spatial planning with GIS spatial analysis method in the study area. The result shows that the proposed concept is feasible for evaluation of urban spatial planning, and is meaningful for guiding the rational distribution of urban space, controlling the scale of development, improving the quality of urbanization and thus promoting highly-efficient and intensive use of limited land resource.

Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

DOE PAGES

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; ...

2015-09-30

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. As a result, our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

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

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. As a result, our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections

PubMed Central

Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.

2015-01-01

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities. PMID:26421722

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning: Summary report

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This summary report discusses the results of each of the four major tasks of the study. Task 1 compared airline flight plans based on operational forecasts to plans based on the verifying analyses and found that average fuel savings of 1.2 to 2.5 percent are possible with improved forecasts. Task 2 consisted of similar comparisons but used a model developed for the FAA by SRI International that simulated the impact of ATc diversions on the flight plans. While parts of Task 2 confirm the Task I findings, inconsistency with other data and the known impact of ATC suggests that other Task 2 findings are the result of errors in the model. Task 3 compares segment weather data from operational flight plans with the weather actually observed by the aircraft and finds the average error could result in fuel burn penalties (or savings) of up to 3.6 percent for the average 8747 flight. In Task 4 an in-depth analysis of the weather forecast for the 33 days included in the study finds that significant errors exist on 15 days. Wind speeds in the area of maximum winds are underestimated by 20 to 50 kts., a finding confirmed in the other three tasks.

An American Laboratory: Population Growth and Environmental Quality in California.

ERIC Educational Resources Information Center

McConnell, Robert

1993-01-01

Describes the cumulative impact of rapid population growth, industrial and military activity, agriculture, and motor vehicles on California's environmental and social fabric. Discusses these problems in California as a forecast for the nation and test to consensus-based U.S. representative government. (Author/ MCO)

Forecasting disease risk for increased epidemic preparedness in public health

NASA Technical Reports Server (NTRS)

Myers, M. F.; Rogers, D. J.; Cox, J.; Flahault, A.; Hay, S. I.

2000-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Forecasting Disease Risk for Increased Epidemic Preparedness in Public Health

PubMed Central

Myers, M.F.; Rogers, D.J.; Cox, J.; Flahault, A.; Hay, S.I.

2011-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world’s epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector–environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development. PMID:10997211

Forecasting fluid milk and cheese demands for the next decade.

PubMed

Schmit, T M; Kaiser, H M

2006-12-01

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

Modeling sediment accumulation in North American playa wetlands in response to climate change, 1940-2100

USGS Publications Warehouse

Burris, Lucy; Skagen, Susan K.

2013-01-01

Playa wetlands on the west-central Great Plains of North America are vulnerable to sediment infilling from upland agriculture, putting at risk several important ecosystem services as well as essential habitats and food resources of diverse wetland-dependent biota. Climate predictions for this semi-arid area indicate reduced precipitation which may alter rates of erosion, runoff, and sedimentation of playas. We forecasted erosion rates, sediment depths, and resultant playa wetland depths across the west-central Great Plains and examined the relative roles of land use context and projected changes in precipitation in the sedimentation process. We estimated erosion with the Revised Universal Soil Loss Equation (RUSLE) using historic values and downscaled precipitation predictions from three general circulation models and three emissions scenarios. We calibrated RUSLE results using field sediment measurements. RUSLE is appealing for regional scale modeling because it uses climate forecasts with monthly resolution and other widely available values including soil texture, slope and land use. Sediment accumulation rates will continue near historic levels through 2070 and will be sufficient to cause most playas (if not already filled) to fill with sediment within the next 100 years in the absence of mitigation. Land use surrounding the playa, whether grassland or tilled cropland, is more influential in sediment accumulation than climate-driven precipitation change.

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.

PubMed

Torres, Rita; Pereira, Elisa; Vasconcelos, Vítor; Teles, Luís Oliva

2011-06-01

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

A numerical forecast model for road meteorology

NASA Astrophysics Data System (ADS)

Meng, Chunlei

2017-05-01

A fine-scale numerical model for road surface parameters prediction (BJ-ROME) is developed based on the Common Land Model. The model is validated using in situ observation data measured by the ROSA road weather stations of Vaisala Company, Finland. BJ-ROME not only takes into account road surface factors, such as imperviousness, relatively low albedo, high heat capacity, and high heat conductivity, but also considers the influence of urban anthropogenic heat, impervious surface evaporation, and urban land-use/land-cover changes. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 and 3 h, respectively. The validation results indicate that BJ-ROME can successfully simulate the diurnal variation of road surface temperature both under clear-sky and rainfall conditions. BJ-ROME can simulate road water and snow depth well if the artificial removing was considered. Road surface energy balance in rainy days is quite different from that in clear-sky conditions. Road evaporation could not be neglected in road surface water cycle research. The results of sensitivity analysis show solar radiation correction coefficient, asphalt depth, and asphalt heat conductivity are important parameters in road interface temperatures simulation. The prediction results could be used as a reference of maintenance decision support system to mitigate the traffic jam and urban water logging especially in large cities.

COP21 climate negotiators' responses to climate model forecasts

NASA Astrophysics Data System (ADS)

Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo

2017-02-01

Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.

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.

Future Trends in San Diego: Population, Income, Employment, Post College Wages and Enrollment.

ERIC Educational Resources Information Center

Barnes, Randy; Armstrong, William B.; Bersentes, Gina; Turingan, Maria

This report contains forecasted data for San Diego through the year 2015 and examines changes that have taken place over the past fifty years. Historically, San Diego population growth rates have been relatively high compared with the rest of the nation. Between 1998 and 2015, the population will not only become larger, it will become more…

Contrasting patterns of nest survival and postfledging survival in ovenbirds and Acadian flycatchers in Missouri forest fragments

Treesearch

Julianna M. A. Jenkins; Frank R. Thompson; John Faaborg

2016-01-01

We can improve our ability to assess population viability and forecast population growth under different scenarios by understanding factors that limit population parameters in each stage of the annual cycle. Postfledging mortality rates may be as variable as nest survival across regions and fragmentation gradients, although factors that negatively impact nest survival...

Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

NASA Astrophysics Data System (ADS)

Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

2010-05-01

Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

CSEP-Japan: The Japanese node of the collaboratory for the study of earthquake predictability

NASA Astrophysics Data System (ADS)

Yokoi, S.; Tsuruoka, H.; Nanjo, K.; Hirata, N.

2011-12-01

Collaboratory for the Study of Earthquake Predictability (CSEP) is a global project of earthquake predictability research. The final goal of this project is to have a look for the intrinsic predictability of the earthquake rupture process through forecast testing experiments. The Earthquake Research Institute, the University of Tokyo joined the CSEP and started the Japanese testing center called as CSEP-Japan. This testing center constitutes an open access to researchers contributing earthquake forecast models for applied to Japan. A total of 91 earthquake forecast models were submitted on the prospective experiment starting from 1 November 2009. The models are separated into 4 testing classes (1 day, 3 months, 1 year and 3 years) and 3 testing regions covering an area of Japan including sea area, Japanese mainland and Kanto district. We evaluate the performance of the models in the official suite of tests defined by the CSEP. The experiments of 1-day, 3-month, 1-year and 3-year forecasting classes were implemented for 92 rounds, 4 rounds, 1round and 0 round (now in progress), respectively. The results of the 3-month class gave us new knowledge concerning statistical forecasting models. All models showed a good performance for magnitude forecasting. On the other hand, observation is hardly consistent in space-distribution with most models in some cases where many earthquakes occurred at the same spot. Throughout the experiment, it has been clarified that some properties of the CSEP's evaluation tests such as the L-test show strong correlation with the N-test. We are now processing to own (cyber-) infrastructure to support the forecast experiment as follows. (1) Japanese seismicity has changed since the 2011 Tohoku earthquake. The 3rd call for forecasting models was announced in order to promote model improvement for forecasting earthquakes after this earthquake. So, we provide Japanese seismicity catalog maintained by JMA for modelers to study how seismicity changes in Japan. (2) Now we prepare the 3-D forecasting experiment with a depth range of 0 to 100 km in Kanto region. (3) The testing center improved an evaluation system for 1-day class experiment because this testing class required fast calculation ability to finish forecasting and testing results within one day. This development will make a real-time forecasting system come true. (4) The special issue of 1st part titled Earthquake Forecast Testing Experiment in Japan was published on the Earth, Planets and Space Vol. 63, No.3, 2011 on March, 2011. This issue includes papers of algorithm of statistical models participating our experiment and outline of the experiment in Japan. The 2nd part of this issue, which is now on line, will be published soon. In this presentation, we will overview CSEP-Japan and results of the experiments, and discuss direction of our activity. An outline of the experiment and activities of the Japanese Testing Center are published on our WEB site;

Lead contents in the surface enamel of deciduous teeth sampled in vivo from children in uncontaminated and in lead-contaminated areas

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

Almeida, Glauce Regina Costa de; Pereira Saraiva, Maria da Conceicao; Barbosa Jr, Fernando

2007-07-15

This study aimed to: (1) measure lead contents in the surface enamel of two populations consisting of 4-6-year-old children, one from an apparently uncontaminated area (Ribeirao Preto, Sao Paulo State, SP, Brazil, n=247) and the other from an area notoriously contaminated with lead (Bauru, Sao Paulo State, Brazil, n=26); (2) compare biopsy depths between the two populations; (3) correlate biopsy depth with lead content; (4) stratify samples according to biopsy depth to compare lead contents in samples from similar biopsy depths. A surface enamel acid-etch microbiopsy was performed in vivo on a single upper deciduous incisor for each sample. Leadmore » was measured by graphite furnace atomic absorption spectrometry (GFAAS) while phosphorus was measured colorimetrically to establish biopsy depth. Samples from both populations were classified into categories of similar biopsy depths based on biopsy depth quartiles. Median lead contents were statistically different between the Ribeirao Preto population (206 {mu}g/g, range: 5-1399 {mu}g/g) and the Bauru population (786 {mu}g/g, range: 320-4711 {mu}g/g) (p<0.001); however, biopsy depth did not differ between the Ribeirao Preto (3.9 {mu}m, Standard Deviation, SD=0.9) and Bauru (3.8 {mu}m, SD=0.9) populations (p=0.7940). Pearson's correlation coefficient for biopsy depths versus log{sub 10} lead values was -0.29 for Ribeirao Preto and -0.18 for Bauru. Lead contents were statistically different between the two populations for all quartiles of biopsy depth. These findings suggest that lead accumulated in the surface enamel of deciduous teeth is linked to the environment in which people reside, indicating that this tissue should be further explored as an accessible biomarker of lead exposure.« less

Evaluation of a wildfire smoke forecasting system as a tool for public health protection.

PubMed

Yao, Jiayun; Brauer, Michael; Henderson, Sarah B

2013-10-01

Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Geographic range and structure of cryptic genetic diversity among Pacific North American populations of the non-native amphipod Grandidierella japonica

EPA Science Inventory

Reconstructing the invasion history of aquatic invasive species can enhance understanding of invasion risks by recognizing areas most susceptible to invasion and forecasting future spread based on past patterns of population expansion. Here we reconstruct the invasion history of ...

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Daily Reservoir Inflow Forecasting using Deep Learning with Downscaled Multi-General Circulation Models (GCMs) Platform

NASA Astrophysics Data System (ADS)

Li, D.; Fang, N. Z.

2017-12-01

Dallas-Fort Worth Metroplex (DFW) has a population of over 7 million depending on many water supply reservoirs. The reservoir inflow plays a vital role in water supply decision making process and long-term strategic planning for the region. This paper demonstrates a method of utilizing deep learning algorithms and multi-general circulation model (GCM) platform to forecast reservoir inflow for three reservoirs within the DFW: Eagle Mountain Lake, Lake Benbrook and Lake Arlington. Ensemble empirical mode decomposition was firstly employed to extract the features, which were then represented by the deep belief networks (DBNs). The first 75 years of the historical data (1940 -2015) were used to train the model, while the last 2 years of the data (2016-2017) were used for the model validation. The weights of each DBN gained from the training process were then applied to establish a neural network (NN) that was able to forecast reservoir inflow. Feature predictors used for the forecasting model were generated from weather forecast results of the downscaled multi-GCM platform for the North Texas region. By comparing root mean square error (RMSE) and mean bias error (MBE) with the observed data, the authors found that the deep learning with downscaled multi-GCM platform is an effective approach in the reservoir inflow forecasting.

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Communicating Storm Surge Forecast Uncertainty

NASA Astrophysics Data System (ADS)

Troutman, J. A.; Rhome, J.

2015-12-01

When it comes to tropical cyclones, storm surge is often the greatest threat to life and property along the coastal United States. The coastal population density has dramatically increased over the past 20 years, putting more people at risk. Informing emergency managers, decision-makers and the public about the potential for wind driven storm surge, however, has been extremely difficult. Recently, the Storm Surge Unit at the National Hurricane Center in Miami, Florida has developed a prototype experimental storm surge watch/warning graphic to help communicate this threat more effectively by identifying areas most at risk for life-threatening storm surge. This prototype is the initial step in the transition toward a NWS storm surge watch/warning system and highlights the inundation levels that have a 10% chance of being exceeded. The guidance for this product is the Probabilistic Hurricane Storm Surge (P-Surge) model, which predicts the probability of various storm surge heights by statistically evaluating numerous SLOSH model simulations. Questions remain, however, if exceedance values in addition to the 10% may be of equal importance to forecasters. P-Surge data from 2014 Hurricane Arthur is used to ascertain the practicality of incorporating other exceedance data into storm surge forecasts. Extracting forecast uncertainty information through analyzing P-surge exceedances overlaid with track and wind intensity forecasts proves to be beneficial for forecasters and decision support.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

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

Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com; Abdul Salam, P., E-mail: salam@ait.ac.th; Kumar, S., E-mail: kumar@ait.ac.th

Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developingmore » countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.« less

Over two decades of Plasmodium knowlesi infections in Sarawak: Trend and forecast.

PubMed

Ooi, Choo Huck; Bujang, Mohamad Adam; Tg Abu Bakar Sidik, Tg Mohd Ikhwan; Ngui, Romano; Lim, Yvonne Ai-Lian

2017-12-01

Malaria is still of great public health concern, especially in Malaysian Borneo. The aim of this study was to determine the trends of P. knowlesi infection in Sarawak, Malaysia and to forecast the incidence of P. knowlesi until the year 2040. Data on P. knowlesi malaria cases from 1992 to the year 2014 were obtained from the Sarawak Health Department, Malaysia. ARIMA model was applied to forecast the future incidence of P. knowlesi infection. The data for the whole of Sarawak and subsequently the selected six districts which have high incidence rates of P. knowlesi infection were analyzed. Results of the analysis showed that there was an increasing trend of P. knowlesi cases from the year 1992-2014 (p<0.001). The trend in the incidence started to increase in the year 2008 (p=0.029). The incidence rate per 100,000 populations was between 4.15 in the year 1992 and 42.03 in the year 2014. High incidence of P. knowlesi infections has been detected in the districts adjacent to each other within the interior region of Sarawak. The forecasted incidence and incidence rate per 100,000 populations in the year 2020 were 1229 and 44.04, respectively, while those in the year 2040 were 2056 and 62.91, respectively. The forecasted incidence showed an upward trend highlighting an urgent need to draw up strategic and holistic prevention plans to limit further the increase in P. knowlesi morbidity and mortality in Sarawak. It is imperative that these measures are customized taking into consideration the challenges faced in the interior areas of Sarawak and the behavior of the main vector of P. knowlesi (i.e., An. latens) in Sarawak. Copyright © 2017 Elsevier B.V. All rights reserved.

Incidence Rate of Acute Encephalitis Syndrome without Specific Treatment in India and Nepal

PubMed Central

Potharaju, Nagabhushana Rao

2012-01-01

Background: A performance target (PT) for the incidence rate (IR) of acute encephalitis syndrome (AES) was not defined by the World Health Organization (WHO) due to lack of data. There is no specific treatment for ~90% of the AES cases. Objectives: (1) To determine the IR of AES not having specific treatment (AESn) in two countries, India and Nepal. (2) To suggest the PT. Subjects and Methods: This was a record-based study of the entire population of India and Nepal from 1978 to 2011. The WHO definition was used for inclusion of cases. Cases that had specific treatment were excluded. IR was calculated per 100,000 population per annum. Forecast IR was generated from 2010 to 2013 using time-series analysis. Results: There were 165,461 cases from 1978 to 2011, of which 125,030 cases were from India and 40,431 were from Nepal. The mean IR of India was 0.42 (s 0.24) and that of Nepal was 5.23 (σ 3.03). IRs of 2010 and 2011 of India and that of 2011 of Nepal were closer to the mean IR rather than the forecast IR. IR of 2010 of Nepal was closer to the forecast IR. The forecast IR for India for 2012 was 0.49 (0.19-1.06), for 2013 was 0.42 (0.15-0.97) and for Nepal for both 2012 and 2013 was 5.62 (1.53-15.05). Conclusions: IRs were considerably different for India and Nepal. Using the current mean IR as PT for the next year was simple and practical. Using forecasting was complex and, less frequently, useful. PMID:23293439

Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.

PubMed

Giovannelli, J; Loury, P; Lainé, M; Spaccaferri, G; Hubert, B; Chaud, P

2015-05-01

To describe and evaluate the forecasts of the load that pandemic A(H1N1)2009 influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France. Modelling study. The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region--of 4 million people. The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day. The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation--particularly the rate of ICU admission among the population that the authors recommend to use--may provide better forecasts. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Forecasting the 2013–2014 influenza season using Wikipedia

DOE PAGES

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; ...

2015-05-14

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

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

NASA Astrophysics Data System (ADS)

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

2014-08-01

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

Forecasting the 2013–2014 Influenza Season Using Wikipedia

PubMed Central

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.

2015-01-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed. PMID:25974758

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

PubMed

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

2014-08-01

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

Forecasting the 2013-2014 influenza season using Wikipedia.

PubMed

Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M; Deshpande, Alina; Del Valle, Sara Y

2015-05-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

Forecasting the 2013–2014 influenza season using Wikipedia

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

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

A 3-month long operational implementation of an ensemble prediction system of storm surge for the city of Venice

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-05-01

Advantages of an ensemble prediction forecast (EPF) technique that has been used for sea level (SL) prediction at the Northern Adriatic coast are investigated. The aims is to explore whether EPF is more precise than the traditional Deterministic Forecast (DF) and the value of the added information, mainly on forecast uncertainty. Improving the SL forecast for the city of Venice is of paramount importance for the management and maintenance of this historical city and for operating the movable barriers that are presently being built for its protection. The operational practice is simulated for three months from 1st October to 31st December 2010. The EPF is based on the HYPSE model, which is a standard single-layer nonlinear shallow water model, whose equations are derived from the depth averaged momentum equations and predicts the SL. A description of the model is available in the scientific literature. Forcing of HYPSE are provided by three different sets of 3-hourly ECMWF 10m-wind and MSLP fields: the high resolution meteorological forecast (which is used for the deterministic SL forecast, DF), the control run forecast (CRF, that differs from the DF forecast only for it lower meteorological fields resolution) and the 50 ensemble members of the ECMWF EPS (which are used for the SL-EPS. The resolution of DF fields is T1279 and resolution of both CRF and ECMWF EPS fields is T639 resolution. The 10m wind and MSLP fields have been downloaded at 0.125degs (DF) and 0.25degs(CRF and EPS) and linearly interpolated to the HYPSE grid (which is the same for all simulations). The version of HYPSE used in the SR EPS uses a rectangular mesh grid of variable size, which has the minimum grid step (0.03 degrees) in the northern part of the Adriatic Sea, from where grid step increases with a 1.01 factor in both latitude and longitude (In practice, resolution varies in the range from 3.3 to 7km). Results are analyzed considering the EPS spread, the rms of the simulations, the Brier Skill Score and are compared to observations at tide gauges distributed along the Croatian and Italian coast of the Adriatic Sea. It is shown that the ensemble spread is indeed a reliable indicator of the uncertainty of the storm surge prediction. Further, results show how uncertainty depends on the predicted value of sea level and how it increases with the forecast time range. The accuracy of the ensemble mean forecast is actually larger than that of the deterministic forecast, though the latter is produced by meteorological forcings at higher resolution

How Many Kentuckians: Population Forecasts, 1980-2020. The 1986 Edition.

ERIC Educational Resources Information Center

Price, Michael

A Kentucky population projection presents 1980 census counts and projections for 1985, 1990, 1995, 2000, 2010, and 2020 for the state, its 15 area development districts, and its 120 counties. Populations are broken down by gender and 5-year age groups through 85 years and over, with age summaries for 0-18 years, 19-64 years, and 65 years and over.…

Forecasting the need for physicians in the United States: the Health Resources and Services Administration's physician requirements model.

PubMed Central

Greenberg, L; Cultice, J M

1997-01-01

OBJECTIVE: The Health Resources and Services Administration's Bureau of Health Professions developed a demographic utilization-based model of physician specialty requirements to explore the consequences of a broad range of scenarios pertaining to the nation's health care delivery system on need for physicians. DATA SOURCE/STUDY SETTING: The model uses selected data primarily from the National Center for Health Statistics, the American Medical Association, and the U.S. Bureau of Census. Forecasts are national estimates. STUDY DESIGN: Current (1989) utilization rates for ambulatory and inpatient medical specialty services were obtained for the population according to age, gender, race/ethnicity, and insurance status. These rates are used to estimate specialty-specific total service utilization expressed in patient care minutes for future populations and converted to physician requirements by applying per-physician productivity estimates. DATA COLLECTION/EXTRACTION METHODS: Secondary data were analyzed and put into matrixes for use in the mainframe computer-based model. Several missing data points, e.g., for HMO-enrolled populations, were extrapolated from available data by the project's contractor. PRINCIPAL FINDINGS: The authors contend that the Bureau's demographic utilization model represents improvements over other data-driven methodologies that rely on staffing ratios and similar supply-determined bases for estimating requirements. The model's distinct utility rests in offering national-level physician specialty requirements forecasts. Images Figure 1 PMID:9018213

Operational use of VIIRS Multispectral Imagery and NUCAPS Soundings in Short-term Weather Forecasting

NASA Astrophysics Data System (ADS)

Molthan, A.; Fuell, K. K.; Berndt, E.; Schultz, L. A.

2016-12-01

The NASA/SPoRT Program supports the NOAA/JPSS program through the transition of S-NPP VIIRS and CrIS/ATMS products to prepare users for the upcoming JPSS-1/-2 missions. Several multispectral (i.e. RGB) imagery products can be created from VIIRS based on internationally-accepted recipes developed by EUMETSAT. Initial transition of a Nighttime Microphysics RGB to operations revealed improved distinction between low clouds and fog compared with legacy satellite imagery, and hence, improvement in short-term aviation and public forecasts. An increased number of S-NPP passes at high latitude combined with other instruments led to a series of "microphysical" RGBs to be introduced to NWS forecasters in Alaska at both local weather offices as well as regional aviation centers. Forecasters in Alaska also applied VIIRS microphysical RGBs to identify small scale features such as valley/coastal fog, volcanic ash, and convective precipitation. Further use of a "Dust" RGB in the U.S. southwest led to changes in NWS forecast products due to improvements in detection and monitoring of dust aloft. As multispectral imagery has gained operational acceptance, additional work has begun to develop quantitative products to assist users with their interpretation of RGB imagery. For example, National Center forecasters often use an "Air Mass" RGB to differentiate between possible stratospheric /tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. Research was done to demonstrate how the NUCAPS CrIS/ATMS infrared retrieved temperature, moisture, and ozone profiles can aid Air Mass RGB imagery interpretation as well as how these quantitative values are important for anticipating tropical to extratropical transition events. In addition, an enhanced stratospheric depth product was developed to identify the dynamic tropopause from the NUCAPS retrieved ozone profiles to aid identification of stratospheric air influence. Forecasters from National Centers evaluated the NUCAPS profiles as a tool for anticipating extratropical transition during the latter half of the 2016 hurricane season. Examples of multispectral and sounding product impacts in near-realtime operations from VIIRS and CrIS/ATMS are presented here.

Surface wave effect on the upper ocean in marine forecast

NASA Astrophysics Data System (ADS)

Wang, Guansuo; Qiao, Fangli; Xia, Changshui; Zhao, Chang

2015-04-01

An Operational Coupled Forecast System for the seas off China and adjacent (OCFS-C) is constructed based on the paralleled wave-circulation coupled model, which is tested with comprehensive experiments and operational since November 1st, 2007. The main feature of the system is that the wave-induced mixing is considered in circulation model. Daily analyses and three day forecasts of three-dimensional temperature, salinity, currents and wave height are produced. Coverage is global at 1/2 degreed resolution with nested models up to 1/24 degree resolution in China Sea. Daily remote sensing sea surface temperatures (SST) are taken to relax to an analytical product as hot restarting fields for OCFS-C by the Nudging techniques. Forecasting-data inter-comparisons are performed to measure the effectiveness of OCFS-C in predicting upper-ocean quantities including SST, mixed layer depth (MLD) and subsurface temperature. The variety of performance with lead time and real-time is discussed as well using the daily statistic results for SST between forecast and satellite data. Several buoy observations and many Argo profiles are used for this validation. Except the conventional statistical metrics, non-dimension skill scores (SS) is taken to estimate forecast skill. Model SST comparisons with more one year-long SST time series from 2 buoys given a large SS value (more than 0.90). And skill in predicting the seasonal variability of SST is confirmed. Model subsurface temperature comparisons with that from a lot of Argo profiles indicated that OCFS-C has low skill in predicting subsurface temperatures between 80m and 120m. Inter-comparisons of MLD reveal that MLD from model is shallower than that from Argo profiles by about 12m. QCFS-C is successful and steady in predicting MLD. The daily statistic results for SST between 1-d, 2-d and 3-d forecast and data is adopted to describe variability of Skill in predicting SST with lead time or real time. In a word QCFS-C shows reasonable accuracy over a series of studies designed to test ability to predict upper ocean conditions.

Review of Studies of Mechanoelectrical Transformations in Rocks in Russia and Abroad

NASA Astrophysics Data System (ADS)

Pomishin, E.; Yavorovich, L.

2016-06-01

The problem of monitoring and forecast of dynamic manifestations of rock masses becomes immediate in the mining industry because of the growth of mining work intensity and changeover to the mining operations in deeper levels. The article presents a short review of the scientific works of foreign researchers for more complete and in-depth study of geophysical methods of control of the stress-strain state and bump hazard of rock masses.

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2011 CFR

2011-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2012 CFR

2012-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2014 CFR

2014-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2010 CFR

2010-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

Research on water shortage risks and countermeasures in North China

NASA Astrophysics Data System (ADS)

Cheng, Yuxiang; Fang, Wenxuan; Wu, Ziqin

2017-05-01

In the paper, a grey forecasting model and a population growth model are established for forecasting water resources supply and demand situation in the region, and evaluating the scarcity of water resources thereof in order to solve the problem of water shortage in North China. A concrete plan for alleviating water resources pressure is proposed with AHP as basis, thereby discussing the feasibility of the plan. Firstly, water resources supply and demand in the future 15 years are predicted. There are four sources for the demand of water resources mainly: industry, agriculture, ecology and resident living. Main supply sources include surface water and underground water resources. A grey forecasting method is adopted for predicting in the paper aiming at water resources demands since industrial, agricultural and ecological water consumption data have excessive decision factors and the correlation is relatively fuzzy. Since residents' water consumption is determined by per capita water consumption and local population, a logistic growth model is adopted to forecast the population. The grey forecasting method is used for predicting per capita water consumption, and total water demand can be obtained finally. International calculation standards are adopted as reference aiming at water supply. The grey forecasting method is adopted for forecasting surface water quantity and underground water quantity, and water resources supply is obtained finally. Per capita water availability in the region is calculated by comparing the water resources supply and demand. Results show that per capita water availability in the region is only 283 cubic meters this year, people live in serious water shortage region, who will suffer from water shortage state for long time. Then, sensitivity analysis is applied for model test. The test result is excellent, and the prediction results are more accurate. In the paper, the following measures are proposed for improving water resources condition in the region according to prediction results, such as construction of reservoirs, sewage treatment, water diversion project and other measures. A detailed water supply plan is formulated. Water supply weights of all measures are determined according to the AHP model. Solution is sought after original models are improved. Results show that water resources quantity per capita will be up to 2170 cubic meters or so this year, people suffer from moderate water shortage in the region, which can meet people's life needs and economic development needs basically. In addition, water resources quantity per capita is increased year by year, and it can reach mild water shortage level after 2030. In a word, local water resources dilemma can be effectively solved by the plan actually, and thoughts can be provided for decision makers.

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

PubMed

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

2017-03-01

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

Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

NASA Astrophysics Data System (ADS)

Liang, Ping; Lin, Hai

2018-02-01

A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

Aging in America in the Twenty-first Century: Demographic Forecasts from the MacArthur Foundation Research Network on an Aging Society

PubMed Central

Olshansky, S Jay; Goldman, Dana P; Zheng, Yuhui; Rowe, John W

2009-01-01

Context: The aging of the baby boom generation, the extension of life, and progressive increases in disability-free life expectancy have generated a dramatic demographic transition in the United States. Official government forecasts may, however, have inadvertently underestimated life expectancy, which would have major policy implications, since small differences in forecasts of life expectancy produce very large differences in the number of people surviving to an older age. This article presents a new set of population and life expectancy forecasts for the United States, focusing on transitions that will take place by midcentury. Methods: Forecasts were made with a cohort-components methodology, based on the premise that the risk of death will be influenced in the coming decades by accelerated advances in biomedical technology that either delay the onset and age progression of major fatal diseases or that slow the aging process itself. Findings: Results indicate that the current forecasts of the U.S. Social Security Administration and U.S. Census Bureau may underestimate the rise in life expectancy at birth for men and women combined, by 2050, from 3.1 to 7.9 years. Conclusions: The cumulative outlays for Medicare and Social Security could be higher by $3.2 to $8.3 trillion relative to current government forecasts. This article discusses the implications of these results regarding the benefits and costs of an aging society and the prospect that health disparities could attenuate some of these changes. PMID:20021588

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

Evaluation of a Wildfire Smoke Forecasting System as a Tool for Public Health Protection

PubMed Central

Brauer, Michael; Henderson, Sarah B.

2013-01-01

Background: Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. Objectives: We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. Methods: We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. Results: We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. Conclusions: BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection. Citation: Yao J, Brauer M, Henderson SB. 2013. Evaluation of a wildfire smoke forecasting system as a tool for public health protection. Environ Health Perspect 121:1142–1147; http://dx.doi.org/10.1289/ehp.1306768 PMID:23906969

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

PubMed

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

2016-09-01

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.

Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.

Plant inputs, microbial carbon use in soil and decomposition under warming: effects of warming are depth dependent

NASA Astrophysics Data System (ADS)

Pendall, E.; Carrillo, Y.; Dijkstra, F. A.

2017-12-01

Future climate will include warmer conditions but impacts on soil C cycling remain uncertain and so are the potential warming-driven feedbacks. Net impacts will depend on the balance of effects on microbial activity and plant inputs. Soil depth is likely to be a critical factor driving this balance as it integrates gradients in belowground biomass, microbial activity and environmental variables. Most empirical studies focus on one soil layer and soil C forecasting relies on broad assumptions about effects of depth. Our limited understanding of the use of available C by soil microbes under climate change across depths is a critical source of uncertainty. Long-term labelling of plant biomass with C isotopic tracers in intact systems allows us to follow the dynamics of different soil C pools including the net accumulation of newly fixed C and the net loss of native C. These can be combined with concurrent observations of microbial use of C pools to explore the impacts of depth on the relationships between plant inputs and microbial C use. We evaluated belowground biomass, in-situ root decomposition and incorporation of plant-derived C into soil C and microbial C at 0-5 cm and 5-15 cmover 7 years at the Prairie Heating And CO2 Enrichment experiment. PHACE was a factorial manipulation of CO2 and warming in a native mixed grass prairie in Wyoming, USA. We used the continuous fumigation with labelled CO2 in the elevated CO2 treatments to study the C dynamics under unwarmed and warmed conditions. Shallower soils had three times the density of biomass as deeper soils. Warming increased biomass in both depths but this effect was weaker in deeper soils. Root litter mass loss in deeper soil was one third that of the shallow and was not affected by warming. Consistent with biomass distribution, incorporation of plant-derived C into soil and microbial C was lower in deeper soils and higher with warming. However, in contrast to the effect of warming on biomass, the effect of warming on incorporation of plant derived C into microbes was stronger in deeper soils. Thus, warming made microbes incorporate relatively more plant inputs in deeper soils, where biomass was less stimulated. This dependency on depth of impacts of warming on microbial C cycling should have important implications for forecasting potential feedbacks of soil C to climate change.

ENSO modulation of tropical Indian Ocean subseasonal variability

NASA Astrophysics Data System (ADS)

Jung, Eunsil; Kirtman, Ben P.

2016-12-01

In this study, we use 30 years of retrospective climate model forecasts and observational estimates to show that El Niño/Southern Oscillation (ENSO) affects the amplitude of subseasonal variability of sea surface temperature (SST) in the southwest Indian Ocean, an important Tropical Intraseasonal Oscillation (TISO) onset region. The analysis shows that deeper background mixed-layer depths and warmer upper ocean conditions during El Niño reduce the amplitude of the subseasonal SST variability over Seychelles-Chagos Thermocline Ridge (SCTR), which may reduce SST-wind coupling and the amplitude of TISO variability. The opposite holds for La Niña where the shallower mixed-layer depth enhances SST variability over SCTR, which may increase SST-wind coupling and the amplitude of TISO variability.

Evaluation of traps used to monitor southern pine beetle aerial populations and sex ratios

Treesearch

James T. Cronin; Jane L. Hayes; Peter Turchin

2000-01-01

Various kinds of traps have been employed to monitor and forecast population trends of the southern pine beetle (Dendroctonus frontalis Zimmermann; Coleoptera: Scolytidae), but their accuracy in assessing pine-beetle abundance and sex ratio in the field has not been evaluated directly.In trus study, we...

Recruitment dynamics in complex life cycles. [of organisms living in marine rocky zone

NASA Technical Reports Server (NTRS)

Roughgarden, Jonathan; Possingham, Hugh; Gaines, Steven

1988-01-01

Factors affecting marine population fluctuations are discussed with particular attention given to a common barnacle species of the Pacific coast of North America. It is shown how models combining larval circulation with adult interactions can potentially forecast population fluctuations. These findings demonstrate how processes in different ecological habitats are coupled.

Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece.

PubMed

Moustris, Kostas P; Douros, Konstantinos; Nastos, Panagiotis T; Larissi, Ioanna K; Anthracopoulos, Michael B; Paliatsos, Athanasios G; Priftis, Kostas N

2012-01-01

Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

2018-02-01

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

Predicting climate effects on Pacific sardine

PubMed Central

Deyle, Ethan R.; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D.; Munch, Stephan B.; Perretti, Charles T.; Ye, Hao; Sugihara, George

2013-01-01

For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine. PMID:23536299

Decadal-scale variation in diet forecasts persistently poor breeding under ocean warming in a tropical seabird

PubMed Central

Tompkins, Emily M.; Townsend, Howard M.

2017-01-01

Climate change effects on population dynamics of natural populations are well documented at higher latitudes, where relatively rapid warming illuminates cause-effect relationships, but not in the tropics and especially the marine tropics, where warming has been slow. Here we forecast the indirect effect of ocean warming on a top predator, Nazca boobies in the equatorial Galápagos Islands, where rising water temperature is expected to exceed the upper thermal tolerance of a key prey item in the future, severely reducing its availability within the boobies’ foraging envelope. From 1983 to 1997 boobies ate mostly sardines, a densely aggregated, highly nutritious food. From 1997 until the present, flying fish, a lower quality food, replaced sardines. Breeding success under the poor diet fell dramatically, causing the population growth rate to fall below 1, indicating a shrinking population. Population growth may not recover: rapid future warming is predicted around Galápagos, usually exceeding the upper lethal temperature and maximum spawning temperature of sardines within 100 years, displacing them permanently from the boobies’ island-constrained foraging range. This provides rare evidence of the effect of ocean warming on a tropical marine vertebrate. PMID:28832597

Decadal-scale variation in diet forecasts persistently poor breeding under ocean warming in a tropical seabird.

PubMed

Tompkins, Emily M; Townsend, Howard M; Anderson, David J

2017-01-01

Climate change effects on population dynamics of natural populations are well documented at higher latitudes, where relatively rapid warming illuminates cause-effect relationships, but not in the tropics and especially the marine tropics, where warming has been slow. Here we forecast the indirect effect of ocean warming on a top predator, Nazca boobies in the equatorial Galápagos Islands, where rising water temperature is expected to exceed the upper thermal tolerance of a key prey item in the future, severely reducing its availability within the boobies' foraging envelope. From 1983 to 1997 boobies ate mostly sardines, a densely aggregated, highly nutritious food. From 1997 until the present, flying fish, a lower quality food, replaced sardines. Breeding success under the poor diet fell dramatically, causing the population growth rate to fall below 1, indicating a shrinking population. Population growth may not recover: rapid future warming is predicted around Galápagos, usually exceeding the upper lethal temperature and maximum spawning temperature of sardines within 100 years, displacing them permanently from the boobies' island-constrained foraging range. This provides rare evidence of the effect of ocean warming on a tropical marine vertebrate.

Forecasting climate change impacts on plant populations over large spatial extents

DOE PAGES

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; ...

2016-10-24

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

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

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Understanding climate impacts on recruitment and spatial dynamics of Atlantic cod in the Gulf of Maine: Integration of observations and modeling

NASA Astrophysics Data System (ADS)

Runge, Jeffrey A.; Kovach, Adrienne I.; Churchill, James H.; Kerr, Lisa A.; Morrison, John R.; Beardsley, Robert C.; Berlinsky, David L.; Chen, Changsheng; Cadrin, Steven X.; Davis, Cabell S.; Ford, Kathryn H.; Grabowski, Jonathan H.; Howell, W. Huntting; Ji, Rubao; Jones, Rebecca J.; Pershing, Andrew J.; Record, Nicholas R.; Thomas, Andrew C.; Sherwood, Graham D.; Tallack, Shelly M. L.; Townsend, David W.

2010-10-01

We put forward a combined observing and modeling strategy for evaluating effects of environmental forcing on the dynamics of spatially structured cod populations spawning in the western Gulf of Maine. Recent work indicates at least two genetically differentiated complexes in this region: a late spring spawning, coastal population centered in Ipswich Bay, and a population that spawns in winter inshore and on nearshore banks in the Gulf of Maine and off southern New England. The two populations likely differ in trophic interactions and in physiological and behavioral responses to different winter and spring environments. Coupled physical-biological modeling has advanced to the point where within-decade forecasting of environmental conditions for recruitment to each of the two populations is feasible. However, the modeling needs to be supported by hydrographic, primary production and zooplankton data collected by buoys, and by data from remote sensing and fixed station sampling. Forecasts of environmentally driven dispersal and growth of planktonic early life stages, combined with an understanding of possible population-specific predator fields, usage of coastal habitat by juveniles and adult resident and migratory patterns, can be used to develop scenarios for spatially explicit population responses to multiple forcings, including climate change, anthropogenic impacts on nearshore juvenile habitat, connectivity among populations and management interventions such as regional fisheries closures.

The psychology of intelligence analysis: drivers of prediction accuracy in world politics.

PubMed

Mellers, Barbara; Stone, Eric; Atanasov, Pavel; Rohrbaugh, Nick; Metz, S Emlen; Ungar, Lyle; Bishop, Michael M; Horowitz, Michael; Merkle, Ed; Tetlock, Philip

2015-03-01

This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and open-mindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

NASA Astrophysics Data System (ADS)

Van Steenbergen, N.; Willems, P.

2012-04-01

Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the probability of flooding of a certain area, based on the uncertainty assessment of the flood forecasts. By using this type of maps, water managers can focus their attention on the areas with the highest flood probability. Also the larger public can consult these maps for information on the probability of flooding for their specific location, such that they can take pro-active measures to reduce the personal damage. The method of quantifying the uncertainty was implemented in the operational flood forecasting system for the navigable rivers in the Flanders region of Belgium. The method has shown clear benefits during the floods of the last two years.

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

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

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

Building regional early flood warning systems by AI techniques

NASA Astrophysics Data System (ADS)

Chang, F. J.; Chang, L. C.; Amin, M. Z. B. M.

2017-12-01

Building early flood warning system is essential for the protection of the residents against flood hazards and make actions to mitigate the losses. This study implements AI technology for forecasting multi-step-ahead regional flood inundation maps during storm events. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building dynamic neural networks to forecast multi-step-ahead average inundated depths (AID); and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted AID to obtain real-time regional inundation maps. The proposed models are trained, and tested based on a large number of inundation data sets collected in regions with the most frequent and serious flooding in the river basin. The results appear that the SOM topological relationships between individual neurons and their neighbouring neurons are visible and clearly distinguishable, and the hybrid model can continuously provide multistep-ahead visible regional inundation maps with high resolution during storm events, which have relatively small RMSE values and high R2 as compared with numerical simulation data sets. The computing time is only few seconds, and thereby leads to real-time regional flood inundation forecasting and make early flood inundation warning system. We demonstrate that the proposed hybrid ANN-based model has a robust and reliable predictive ability and can be used for early warning to mitigate flood disasters.

Real-time localization of mobile device by filtering method for sensor fusion

NASA Astrophysics Data System (ADS)

Fuse, Takashi; Nagara, Keita

2017-06-01

Most of the applications with mobile devices require self-localization of the devices. GPS cannot be used in indoor environment, the positions of mobile devices are estimated autonomously by using IMU. Since the self-localization is based on IMU of low accuracy, and then the self-localization in indoor environment is still challenging. The selflocalization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method without GPS in indoor environment by integrating sensors, such as IMU and cameras, on mobile devices simultaneously. The proposed method consists of observations, forecasting and filtering. The position and velocity of the mobile device are defined as a state vector. In the self-localization, observations correspond to observation data from IMU and camera (observation vector), forecasting to mobile device moving model (system model) and filtering to tracking method by inertial surveying and coplanarity condition and inverse depth model (observation model). Positions of a mobile device being tracked are estimated by system model (forecasting step), which are assumed as linearly moving model. Then estimated positions are optimized referring to the new observation data based on likelihood (filtering step). The optimization at filtering step corresponds to estimation of the maximum a posterior probability. Particle filter are utilized for the calculation through forecasting and filtering steps. The proposed method is applied to data acquired by mobile devices in indoor environment. Through the experiments, the high performance of the method is confirmed.

Simulation Based Earthquake Forecasting with RSQSim

NASA Astrophysics Data System (ADS)

Gilchrist, J. J.; Jordan, T. H.; Dieterich, J. H.; Richards-Dinger, K. B.

2016-12-01

We are developing a physics-based forecasting model for earthquake ruptures in California. We employ the 3D boundary element code RSQSim to generate synthetic catalogs with millions of events that span up to a million years. The simulations incorporate rate-state fault constitutive properties in complex, fully interacting fault systems. The Unified California Earthquake Rupture Forecast Version 3 (UCERF3) model and data sets are used for calibration of the catalogs and specification of fault geometry. Fault slip rates match the UCERF3 geologic slip rates and catalogs are tuned such that earthquake recurrence matches the UCERF3 model. Utilizing the Blue Waters Supercomputer, we produce a suite of million-year catalogs to investigate the epistemic uncertainty in the physical parameters used in the simulations. In particular, values of the rate- and state-friction parameters a and b, the initial shear and normal stress, as well as the earthquake slip speed, are varied over several simulations. In addition to testing multiple models with homogeneous values of the physical parameters, the parameters a, b, and the normal stress are varied with depth as well as in heterogeneous patterns across the faults. Cross validation of UCERF3 and RSQSim is performed within the SCEC Collaboratory for Interseismic Simulation and Modeling (CISM) to determine the affect of the uncertainties in physical parameters observed in the field and measured in the lab, on the uncertainties in probabilistic forecasting. We are particularly interested in the short-term hazards of multi-event sequences due to complex faulting and multi-fault ruptures.

Impact of Assimilated and Interactive Aerosol on Tropical Cyclogenesis

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, K. M.; daSilva, A.; Matsui, T.

2014-01-01

This article investigates the impact 3 of Saharan dust on the development of tropical cyclones in the Atlantic. A global data assimilation and forecast system, the NASA GEOS-5, is used to assimilate all satellite and conventional data sets used operationally for numerical weather prediction. In addition, this new GEOS-5 version includes assimilation of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS). The analysis so obtained comprises atmospheric quantities and a realistic 3-d aerosol and cloud distribution, consistent with the meteorology and validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data. These improved analyses are used to initialize GEOS-5 forecasts, explicitly accounting for aerosol direct radiative effects and their impact on the atmospheric dynamics. Parallel simulations with/without aerosol radiative effects show that effects of dust on static stability increase with time, becoming highly significant after day 5 and producing an environment less favorable to tropical cyclogenesis.

New challenges in solar energy resource and forecasting in Greece

NASA Astrophysics Data System (ADS)

Kazantzidis, A.; Nikitidou, E.; Salamalikis, V.; Tzoumanikas, P.; Zagouras, A.

2018-05-01

Aerosols and clouds are the most important constituents in the atmosphere that affect the incoming solar radiation, either directly through absorbing and scattering processes or indirectly by changing the optical properties and lifetime of clouds. Under clear skies, aerosols become the dominant factor that affect the intensity of solar irradiance reaching the ground. Under cloudy skies, the high temporal and spatial variability of cloudiness is the key factor for the estimation of solar irradiance. In this study, recent research activities related to the climatology and the prediction of solar energy in Greece are presented with emphasis on new challenges in the climatology of global horizontal irradiance (GHI) and direct normal irradiance (DNI), the changes of DNI due to the decreasing aerosol optical depth and the short-term (15-240 min) forecasts of solar irradiance with the collaborative use of neural networks and satellite images.

Interrelations of green oak leaf roller population and common oak: results of 30-year monitoring and mathematical modeling

Treesearch

V. V. Rubtsov; I. A. Utkina

2003-01-01

Long-term monitoring followed by mathematical modeling was used to describe the population dynamics of the green oak leaf roller Tortrix viridana L. over a period of 30 years and to study reactions of oak stands to different levels of defoliation. The mathematical model allows us to forecast the population dynamics of the green oak leaf roller and...

The Eruption Forecasting Information System: Volcanic Eruption Forecasting Using Databases

NASA Astrophysics Data System (ADS)

Ogburn, S. E.; Harpel, C. J.; Pesicek, J. D.; Wellik, J.

2016-12-01

Forecasting eruptions, including the onset size, duration, location, and impacts, is vital for hazard assessment and risk mitigation. The Eruption Forecasting Information System (EFIS) project is a new initiative of the US Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) and will advance VDAP's ability to forecast the outcome of volcanic unrest. The project supports probability estimation for eruption forecasting by creating databases useful for pattern recognition, identifying monitoring data thresholds beyond which eruptive probabilities increase, and for answering common forecasting questions. A major component of the project is a global relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest. This module allows us to query eruption chronologies, monitoring data, descriptive information, operational data, and eruptive phases alongside other global databases, such as WOVOdat and the Global Volcanism Program. The EFIS database is in the early stages of development and population; thus, this contribution also is a request for feedback from the community. Preliminary data are already benefitting several research areas. For example, VDAP provided a forecast of the likely remaining eruption duration for Sinabung volcano, Indonesia, using global data taken from similar volcanoes in the DomeHaz database module, in combination with local monitoring time-series data. In addition, EFIS seismologists used a beta-statistic test and empirically-derived thresholds to identify distal volcano-tectonic earthquake anomalies preceding Alaska volcanic eruptions during 1990-2015 to retrospectively evaluate Alaska Volcano Observatory eruption precursors. This has identified important considerations for selecting analog volcanoes for global data analysis, such as differences between closed and open system volcanoes.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

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.

Past, Current, and Future Incidence Rates and Burden of Metastatic Prostate Cancer in the United States.

PubMed

Kelly, Scott P; Anderson, William F; Rosenberg, Philip S; Cook, Michael B

2017-11-18

Metastatic prostate cancer (PCA) remains a highly lethal malignancy in the USA. As prostate-specific antigen testing declines nationally, detailed assessment of current age- and race-specific incidence trends and quantitative forecasts are needed. To evaluate the current trends of metastatic PCA by age and race, and forecast the number of new cases (annual burden) and future trends. We derived incidence data for men aged ≥45 yr who were diagnosed with metastatic PCA from the population-based Surveillance, Epidemiology, and End Results registries. We examined the current trends of metastatic PCA from 2004 to 2014, and forecast the annual burden and incidence rates by age and race for 2015-2025, using age-period-cohort models and population projections. We also examined alternative forecasts (2012-2025) using trends prior to the revised screening guidelines issued in 2012. Metastatic PCA, steadily declining from 2004 to 2007 by 1.45%/yr, began to increase by 0.58%/yr after 2008, which accelerated to 2.74%/yr following the 2012 United States Preventive Services Task Force recommendations-a pattern that was magnified among men aged ≤69 yr and white men. Forecasts project the incidence to increase by 1.03%/yr through 2025, with men aged 45-54 yr (2.29%/yr) and 55-69 yr (1.53%/yr) increasing more rapidly. Meanwhile, the annual burden is expected to increase 42% by 2025. Our forecasts estimated an additional 15 891 metastatic cases from 2015 to 2025 compared with alternative forecasts using trends prior to 2012. The recent uptick in metastatic PCA rates has resulted in forecasts that project increasing rates through 2025, particularly among men aged ≤69 yr. Moreover, racial disparities are expected to persist and the annual burden will increase considerably. The impact of the prior and current PCA screening recommendations on metastatic PCA rates requires continued examination. In this report, we assessed how the incidence of metastatic prostate cancer has changed over recent years, and forecast future incidence trends and the number of new cases expected each year. We found that the incidence of metastatic prostate cancer has been increasing more rapidly since 2012, resulting in a rise in both future incidence and the number of new cases by 2025. Future incidence rates and the number of new cases were reduced in alternative forecasts using data prior to the 2012 United States Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA) testing for prostate cancer. There is a need for additional research that examines whether national declines in PSA testing contributed to increases in rates of metastatic disease. The incidence of metastatic disease in black men is still expected to occur at considerably higher rates compared with that in white men. Published by Elsevier B.V.

Ocean Nowcast/Forecast Systems for Naval Undersea Capability

DTIC Science & Technology

2007-01-01

Tonkin to the Taiwan Strait is consistently nearly 70 m deep, averaging 150 km in width; the central deep basin is 1900 km along its major axis...shaped basin in the center, and numerous reef islands 5 and underwater plateaus scattered throughout. The shelf that extends from the Gulf of...connection between southeastern Asia, Malaysia, Sumatra , Java, and Borneo and reaches 100 m depth in the middle; the center of the Gulf of Thailand is about

Forecast of Remote Underwater Sensing Technology.

DTIC Science & Technology

1980-07-01

hr T. MAGNETICS (2 Replies) Q. What will be sensitivities of fluxgate , proton, optical pump, SQUID (superconducting) magnetometers ? A. Fluxgate 0.1...ft Oujtpuit Analog, digital and B3CD Cost $65.K 227 Manu factu rer EG&G Geometric Unit G-806M System Marine Search Proton Magnetometer Sensitivity...optional) Depth Range 0 to 100 m or 6000 m Precision +0.15% FS Time Constant 60 ms Output Digital display, analog and digital BCD output Cost $13.K 243

The population biology and genetics of the deep-sea spider crab, Encephaloides armstrongi Wood-Mason 1891 (Decapoda: Majidae)

PubMed Central

Creasey, S.; Rogers, A. D.; Tyler, P.; Young, C.; Gage, J.

1997-01-01

Numerous specimens of the majid spider crab, Encephaloides armstrongi, were sampled from six stations (populations) between 150 and 650 m depth, on the continental slope off the coast of Oman. This extended the known geographic and bathymetric range of E. armstrongi, which is now known to occur along the continental margins of the northern Indian Ocean from the western coast of Burma to the coast of Oman. This band-like distribution is contiguous to the oxygen minimum zone in this region. The biology and genetics of populations of Encephaloides armstrongi separated by depth were studied. The overall sex ratio of the E. armstrongi sampled was male-biased (p less than 0.01; 3.3 males: 1 female; So = 0.538). However, sex ratio varied both between populations (p less than 0.01) and between size classes of crabs. Size frequency analysis indicated that the male and female crabs consisted of at least two instars, one between 6 and 16mm carapace length and one between 16 and 29 mm carapace length, which probably represented the terminal (pubertal) moult for most individuals. Accumulation of female crabs in the terminal instar probably caused the variation of sex ratio with size classes. Some male crabs grew to a larger size (up to 38 mm carapace length), possibly as a result of maturity at later instars. Length frequency distribution was significantly different between sexes (one-way ANOVA p less than 0.001). Within sexes, length frequency distributions varied between different populations. In both male and female Encephaloides armstrongi the individuals from a single population located at 150 m depth were significantly smaller than individuals at all other stations and were considered to represent a juvenile cohort. For female crabs no other significant differences were detected in length frequency between populations from 300 m to 650 m depth. Significant differences in length frequency were detected between male crabs from populations between 300 and 650 m depth. Horizontal starch gel electrophoresis was used to detect six enzyme systems coding for eight loci for individuals sampled from each population of Encephaloides armstrongi. Genetic identity (I) values between populations of E. armstrongi (I = 0.98-1.00) were within the normal range for conspecific populations. Observed heterozygosity (Ho = 0.080-0.146) was lower than expected heterozygosity (He = 0.111-0.160), but in the normal range detected for eukaryotic organisms. F-statistics were used to analyse between population (FST) and within population (F ) genetic structure. For both male and female E. armstrongi significant genetic differentiation was detected between the population located at 150 m depth and all other populations. Analyses of FIS and FST, excluding the 150 m population indicated that for female E. armstrongi there was no significant structuring within or between populations. For male E. armstrongi significant heterozygote deficiencies were detected within populations and significant genetic differentiation between populations. The most likely explanations for the observations of the present study are: the population of Encephaloides armstrongi located at 150 m depth represented a juvenile cohort that is genetically distinct from deeper populations; female E. armstrongi formed a single population between 300 m and 650 m depth in the sampling area; male E. armstrongi were from two or more genetically distinct populations which are represented by different numbers of individuals at stations between 300 m and 650 m depth. This caused the observed significant differences in morphology (size distribition) and allele frequencies of male populations. It is likely that E. armstrongi exhibits gender-biased dispersal and that the crabs collected between 300 m and 650 m depth formed spawning aggressions. This also explains the bias in sex ratio of individuals sampled in the present study.

Development and application of a spatial IBM to forecast greater prairie-chicken population responses to land use in the Flint Hills region of Kansas

EPA Science Inventory

Greater prairie-chicken (Tympanachus cupido) populations have been on the decline for decades. Recent efforts to reverse this trend are focusing on two specific disturbance regimes, cattle grazing and field burning, both prevalent in the Flint Hill region of Kansas -- an area of...

The cumulative effect of consecutive winters' snow depth on moose and deer populations: a defence

USGS Publications Warehouse

McRoberts, R.E.; Mech, L.D.; Peterson, R.O.

1995-01-01

1. L. D. Mech et al. presented evidence that moose Alces alces and deer Odocoileus virginianus population parameters re influenced by a cumulative effect of three winters' snow depth. They postulated that snow depth affects adult ungulates cumulatively from winter to winter and results in measurable offspring effects after the third winter. 2. F. Messier challenged those findings and claimed that the population parameters studied were instead affected by ungulate density and wolf indexes. 3. This paper refutes Messier's claims by demonstrating that his results were an artifact of two methodological errors. The first was that, in his main analyses, Messier used only the first previous winter's snow depth rather than the sum of the previous three winters' snow depth, which was the primary point of Mech et al. Secondly, Messier smoothed the ungulate population data, which removed 22-51% of the variability from the raw data. 4. When we repeated Messier's analyses on the raw data and using the sum of the previous three winter's snow depth, his findings did not hold up.

The large area crop inventory experiment: A major demonstration of space remote sensing

NASA Technical Reports Server (NTRS)

Macdonald, R. B.; Hall, F. G.

1977-01-01

Strategies are presented in agricultural technology to increase the resistance of crops to a wider range of meteorological conditions in order to reduce year-to-year variations in crop production. Uncertainties in agricultral production, together with the consumer demands of an increasing world population, have greatly intensified the need for early and accurate annual global crop production forecasts. These forecasts must predict fluctuation with an accuracy, timeliness and known reliability sufficient to permit necessary social and economic adjustments, with as much advance warning as possible.

System development over the monitoring for the purpose of early warning of population from the threat of landslides. (Invited)

NASA Astrophysics Data System (ADS)

Zakhidova, D. V.; Kadyrhodjaev, A.; Scientific Team Of Hydroengeo Institute On Natural Hazards

2010-12-01

Well-timed warning of the population about possible landslide threat is one of the main positions in order to provide safe and stable country development. The system of monitoring over dangerous geological processes includes such components, as observation, forecast, control and management. Aspects of forecasting take special place. Having wide row of observations there can be possible to reveal some regularity of the phenomena, basing on which, it is possible to proceed forecasting. We looked through many approaches of forecasting that are used in different countries. The analysis of the available work has allowed to draw up a conclusion that while referring to the question of landslide forecasting, it is necessary to approach in system form, taking into account interacting components of the nature. The study of landslide processes has shown that these processes lies within the framework of engineering-geological directions of the science and also interacts with tectonics, geomorphology, hydrogeology, hydrology, climate change, technogenesis and etc. Thereby, the necessity of system approach, achievements of modern science and technology the most expedient approach to make a decision at landslide forecasting is probabilistic-statistical method with complex use of geological and satellite data, specific images processed through geoinformation systems. In this connection, probabilistic-statistical approach, reflecting natural characteristics of interacting natural system, allows to take into account multi-factored processes of landslide activations. Among the many factors, influencing on landslide activation, there exist ones that are not amenable to numerical feature. The parameters of these factors have descriptive, qualitative, rather than quantitative nature. Leaving these factors with lack of attention is absolutely not reasonable. Proposed approach has one more advantage, which allows taking into account not only numerical, but also non-numeric parameters. Combination of multidisciplinary, systematic feature, multifactorness of the account, probabilistic and statistical methods of the calculation, complex use of geological and satellite data, using modern technology processing and analysis of information - all these aspects were collected in one at proposed by authors approach to solve the question of defining the area of possible landslide activation. Proposed by authors method could be a part of the monitoring system for early warning of landslide activation. Thus, the authors propose to initialize the project “System development over the monitoring for the purpose of early warning of population from the threat of landslides”. In the process of project implementation there to be revealed such results like: 1. System of Geo-indicators in order to early warn quick-running landslide processes. 2. United interconnected system for remote, surface and underground types of observations over Geo-indicators. 3. Notification system of population about forthcoming threat by means of alerts, light signals, mobilization of municipalities and related ministries. In the result of project implementation there considered to reveal economic, technical, and social outputs.

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

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

PubMed

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

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the need for effective synthesis and visualization of forecasts, and the importance of linking forecasts to specific public health responses.

Catastrophic shifts in vegetation-soil systems may unfold rapidly or slowly independent of the rate of change in the system driver

NASA Astrophysics Data System (ADS)

Karssenberg, Derek; Bierkens, Marc

2014-05-01

Complex systems may switch between contrasting stable states under gradual change of a driver. Such critical transitions often result in considerable long-term damage because strong hysteresis impedes reversion, and the transition becomes catastrophic. Critical transitions largely reduce our capability of forecasting future system states because it is hard to predict the timing of their occurrence [2]. Moreover, for many systems it is unknown how rapidly the critical transition unfolds when the tipping point has been reached. The rate of change during collapse, however, is important information because it determines the time available to take action to reverse a shift [1]. In this study we explore the rate of change during the degradation of a vegetation-soil system on a hillslope from a state with considerable vegetation cover and large soil depths, to a state with sparse vegetation and a bare rock or negligible soil depths. Using a distributed, stochastic model coupling hydrology, vegetation, weathering and water erosion, we derive two differential equations describing the vegetation and the soil system, and their interaction. Two stable states - vegetated and bare - are identified by means of analytical investigation, and it is shown that the change between these two states is a critical transition as indicated by hysteresis. Surprisingly, when the tipping point is reached under a very slow increase of grazing pressure, the transition between the vegetated and the bare state can either unfold rapidly, over a few years, or gradually, occurring over decennia up to millennia. These differences in the rate of change during the transient state are explained by differences in bedrock weathering rates. This finding emphasizes the considerable uncertainty associated with forecasting catastrophic shifts in ecosystems, which is due to both difficulties in forecasting the timing of the tipping point and the rate of change when the transition unfolds. References [1] Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. a, & van Nes, E. H. (2013). Living dangerously on borrowed time during slow, unrecognized regime shifts. Trends in ecology & evolution, 28(3), 149-55. [2] Karssenberg, D., & Bierkens, M. F. P. (2012). Early-warning signals (potentially) reduce uncertainty in forecasted timing of critical shifts. Ecosphere, 3(2).

Simulation and analysis of synoptic scale dust storms over the Arabian Peninsula

NASA Astrophysics Data System (ADS)

Beegum, S. Naseema; Gherboudj, Imen; Chaouch, Naira; Temimi, Marouane; Ghedira, Hosni

2018-01-01

Dust storms are among the most severe environmental problems in arid and semi-arid regions of the world. The predictability of seven dust events, viz. D1: April 2-4, 2014; D2: February 23-24, 2015; D3: April 1-3, 2015; D4: March 26-28, 2016; D5: August 3-5, 2016; D6: March 13-14, 2017 and D7:March 19-21, 2017, are investigated over the Arabian Peninsula using a regionally adapted chemistry transport model CHIMERE coupled with the Weather Research and Forecast (WRF) model. The hourly forecast products of particulate matter concentrations (PM10) and aerosol optical depths (AOD) are compared against both satellite-based (MSG/SEVRI RGB dust, MODIS Deep Blue Aerosol Optical Depth: DB-AOD, Ozone Monitoring Instrument observed UV Aerosol Absorption Index: OMI-AI) and ground-based (AERONET AOD) remote sensing products. The spatial pattern and the time series of the simulations show good agreement with the observations in terms of the dust intensity as well as the spatiotemporal distribution. The causative mechanisms of these dust events are identified by the concurrent analyses of the meteorological data. From these seven storms, five are associated with synoptic scale meteorological processes, such as prefrontal storms (D1 and D7), postfrontal storms of short (D2), and long (D3) duration types, and a summer shamal storm (D6). However, the storms D4 and D6 are partly associated with mesoscale convective type dust episodes known as haboobs. The socio-economic impacts of the dust events have been assessed by estimating the horizontal visibility, air quality index (AQI), and the dust deposition flux (DDF) from the forecasted dust concentrations. During the extreme dust events, the horizontal visibility drops to near-zero values co-occurred withhazardous levels of AQI and extremely high dust deposition flux (250 μg cm- 2 day- 1).

A low-altitude mountain range as an important refugium for two narrow endemics in the Southwest Australian Floristic Region biodiversity hotspot

PubMed Central

Robinson, Todd P.; Wardell-Johnson, Grant W.; Yates, Colin J.; Van Niel, Kimberly P.; Byrne, Margaret; Schut, Antonius G. T.

2017-01-01

Background and Aims Low-altitude mountains constitute important centres of diversity in landscapes with little topographic variation, such as the Southwest Australian Floristic Region (SWAFR). They also provide unique climatic and edaphic conditions that may allow them to function as refugia. We investigate whether the Porongurups (altitude 655 m) in the SWAFR will provide a refugium for the endemic Ornduffia calthifolia and O. marchantii under forecast climate change. Methods We used species distribution modelling based on WorldClim climatic data, 30-m elevation data and a 2-m-resolution LiDAR-derived digital elevation model (DEM) to predict current and future distributions of the Ornduffia species at local and regional scales based on 605 field-based abundance estimates. Future distributions were forecast using RCP2.6 and RCP4.5 projections. To determine whether local edaphic and biotic factors impact these forecasts, we tested whether soil depth and vegetation height were significant predictors of abundance using generalized additive models (GAMs). Key Results Species distribution modelling revealed the importance of elevation and topographic variables at the local scale for determining distributions of both species, which also preferred shadier locations and higher slopes. However, O. calthifolia occurred at higher (cooler) elevations with rugged, concave topography, while O. marchantii occurred in disturbed sites at lower locations with less rugged, convex topography. Under future climates both species are likely to severely contract under the milder RCP2.6 projection (approx. 2 °C of global warming), but are unlikely to persist if warming is more severe (RCP4.5). GAMs showed that soil depth and vegetation height are important predictors of O. calthifolia and O. marchantii distributions, respectively. Conclusions The Porongurups constitute an important refugium for O. calthifolia and O. marchantii, but limits to this capacity may be reached if global warming exceeds 2 °C. This capacity is moderated at local scales by biotic and edaphic factors. PMID:27634576

Application of a three-dimensional hydrodynamic model to the Himmerfjärden, Baltic Sea

NASA Astrophysics Data System (ADS)

Sokolov, Alexander

2014-05-01

Himmerfjärden is a coastal fjord-like bay situated in the north-western part of the Baltic Sea. The fjord has a mean depth of 17 m and a maximum depth of 52 m. The water is brackish (6 psu) with small salinity fluctuation (±2 psu). A sewage treatment plant, which serves about 300 000 people, discharges into the inner part of Himmerfjärden. This area is the subject of a long-term monitoring program. We are planning to develop a publicly available modelling system for this area, which will perform short-term forecast predictions of pertinent parameters (e.g., water-levels, currents, salinity, temperature) and disseminate them to users. A key component of the system is a three-dimensional hydrodynamic model. The open source Delft3D Flow system (http://www.deltaressystems.com/hydro) has been applied to model the Himmerfjärden area. Two different curvilinear grids were used to approximate the modelling domain (25 km × 50 km × 60 m). One grid has low horizontal resolution (cell size varies from 250 to 450 m) to perform long-term numerical experiments (modelling period of several months), while another grid has higher resolution (cell size varies from 120 to 250 m) to model short-term situations. In vertical direction both z-level (50 layers) and sigma coordinate (20 layers) were used. Modelling results obtained with different horizontal resolution and vertical discretisation will be presented. This model will be a part of the operational system which provides automated integration of data streams from several information sources: meteorological forecast based on the HIRLAM model from the Finnish Meteorological Institute (https://en.ilmatieteenlaitos.fi/open-data), oceanographic forecast based on the HIROMB-BOOS Model developed within the Baltic community and provided by the MyOcean Project (http://www.myocean.eu), riverine discharge from the HYPE model provided by the Swedish Meteorological Hydrological Institute (http://vattenwebb.smhi.se/modelarea/).

Forecasting and evaluating patterns of energy development in southwestern Wyoming

USGS Publications Warehouse

Garman, Steven L.

2015-01-01

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

Projecting surgeon supply using a dynamic model.

PubMed

Fraher, Erin P; Knapton, Andy; Sheldon, George F; Meyer, Anthony; Ricketts, Thomas C

2013-05-01

To develop a projection model to forecast the head count and full-time equivalent supply of surgeons by age, sex, and specialty in the United States from 2009 to 2028. The search for the optimal number and specialty mix of surgeons to care for the United States population has taken on increased urgency under health care reform. Expanded insurance coverage and an aging population will increase demand for surgical and other medical services. Accurate forecasts of surgical service capacity are crucial to inform the federal government, training institutions, professional associations, and others charged with improving access to health care. The study uses a dynamic stock and flow model that simulates future changes in numbers and specialty type by factoring in changes in surgeon demographics and policy factors. : Forecasts show that overall surgeon supply will decrease 18% during the period form 2009 to 2028 with declines in all specialties except colorectal, pediatric, neurological surgery, and vascular surgery. Model simulations suggest that none of the proposed changes to increase graduate medical education currently under consideration will be sufficient to offset declines. The length of time it takes to train surgeons, the anticipated decrease in hours worked by surgeons in younger generations, and the potential decreases in graduate medical education funding suggest that there may be an insufficient surgeon workforce to meet population needs. Existing maldistribution patterns are likely to be exacerbated, leading to delayed or lost access to time-sensitive surgical procedures, particularly in rural areas.

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.

PubMed

Mao, Qiang; Zhang, Kai; Yan, Wu; Cheng, Chaonan

2018-05-02

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources. Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months. During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1) 12 , which demonstrated adequate information extraction (white noise test, p>0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively. According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Forecasting Ontario's blood supply and demand.

PubMed

Drackley, Adam; Newbold, K Bruce; Paez, Antonio; Heddle, Nancy

2012-02-01

Given an aging population that requires increased medical care, an increasing number of deferrals from the donor pool, and a growing immigrant population that typically has lower donation rates, the purpose of this article is to forecast Ontario's blood supply and demand. We calculate age- and sex-specific donation and demand rates for blood supply based on 2008 data and project demand between 2008 and 2036 based on these rates and using population data from the Ontario Ministry of Finance. Results indicate that blood demand will outpace supply as early as 2012. For instance, while the total number of donations made by older cohorts is expected to increase in the coming years, the number of red blood cell (RBC) transfusions in the 70+ age group is forecasted grow from approximately 53% of all RBC transfusions in 2008 (209,515) in 2008 to 68% (546,996) by 2036. A series of alternate scenarios, including projections based on a 2% increase in supply per year and increased use of apheresis technology, delays supply shortfalls, but does not eliminate them without active management and/or multiple methods to increase supply and decrease demand. Predictions show that demand for blood products will outpace supply in the near future given current age- and sex-specific supply and demand rates. However, we note that the careful management of the blood supply by Canadian Blood Services, along with new medical techniques and the recruitment of new donors to the system, will remove future concerns. © 2012 American Association of Blood Banks.

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

PubMed

LaDeau, Shannon L; Glass, Gregory E; Hobbs, N Thompson; Latimer, Andrew; Ostfeld, Richard S

2011-07-01

Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.

Remote sensing: Snow monitoring tool for today and tomorrow

NASA Technical Reports Server (NTRS)

Rango, A.

1977-01-01

Various types of remote sensing are now available or will be in the future for snowpack monitoring. Aircraft reconnaissance is now used in a conventional manner by various water resources agencies to obtain information on snowlines, depth, and melting of the snowpack for forecasting purposes. The use of earth resources satellites for mapping snowcovered area, snowlines, and changes in snowcover during the spring has increased during the last five years. Gamma ray aircraft flights, although confined to an extremely low altitude, provide a means for obtaining valuable information on snow water equivalent. The most recently developed remote sensing technology for snow, namely, microwave monitoring, has provided initial results that may eventually allow us to infer snow water equivalent or depth, snow wetness, and the hydrologic condition of the underlying soil.

An Operational Coastal Forecasting System in Galicia (NW Spain)

NASA Astrophysics Data System (ADS)

Balseiro, C. F.; Carracedo, P.; Pérez, E.; Pérez, V.; Taboada, J.; Venacio, A.; Vilasa, L.

2009-09-01

The Galician coast (NW Iberian Peninsula coast) and mainly the Rias Baixas (southern Galician rias) are one of the most productive ecosystems in the world, supporting a very active fishing and aquiculture industry. This high productivity lives together with a high human pressure and an intense maritime traffic, which means an important environmental risk. Besides that, Harmful Algae Blooms (HAB) are common in this area, producing important economical losses in aquiculture. In this context, the development of an Operational Hydrodynamic Ocean Forecast System is the first step to the development of a more sophisticated Ocean Integrated Decision Support Tool. A regional oceanographic forecasting system in the Galician Coast has been developed by MeteoGalicia (the Galician regional meteorological agency) inside ESEOO project to provide forecasts on currents, sea level, water temperature and salinity. This system is based on hydrodynamic model MOHID, forced with the operational meteorological model WRF, supported daily at MeteoGalicia . Two grid meshes are running nested at different scales, one of ~2km at the shelf scale and the other one with a resolution of 500 m at the rias scale. ESEOAT (Puertos del Estado) model provide salinity and temperature fields which are relaxed at all depth along the open boundary of the regional model (~6km). Temperature and salinity initial fields are also obtained from this application. Freshwater input from main rivers are included as forcing in MOHID model. Monthly mean discharge data from gauge station have been provided by Aguas de Galicia. Nowadays a coupling between an hydrological model (SWAT) and the hydrodynamic one are in development with the aim to verify the impact of the rivers discharges. The system runs operationally daily, providing two days of forecast. First model verifications had been performed against Puertos del Estado buoys and Xunta de Galicia buoys network along the Galician coast. High resolution model results were validated against a CTDs profiles campaign carried out during an oil spill exercise in the Ria de Vigo in April 2007. During EROCIPS INTERREG IIIB and EASY INTERREG IVB projects, a Galician oceanographic observation network were built. Three stations located inside the Rias Baixas allow to collect meteorological and oceanographic data at different depths to calibrate and validate the modelization of the rias. To complete this network and to create a common data platform a new project emerged (RAIA INTERREG IVA). It will provide MeteoGalicia more scientific data to improve the study of the rias. Furthermore, MeteoGalicia is also involved in DRIFTER AMPERA project which allows to improve the capability of modelling and monitoring the trajectory of hazardous substances and inerts.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

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

PubMed Central

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

2015-01-01

Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981 PMID:26662617

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Zappa, M.

2011-01-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This models chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that COSMO-LEPS-based hydrological forecasts overall outperform their COSMO-7 based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts and used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

NASA Technical Reports Server (NTRS)

Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

2009-01-01

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Application of Jason-2/3 Altimetry for Virtual Gauging and Flood Forecasting in Mekong Basin

NASA Astrophysics Data System (ADS)

Lee, H.; Hossain, F.; Okeowo, M. A.; Nguyen, L. D.; Bui, D. D.; Chang, C. H.

2016-12-01

Vietnam suffers from both flood and drought during the rainy and dry seasons, respectively, due to its highly varying surface water resources. However, the National Center for Water Resources Planning and Investigation (NAWAPI) states that only 7 surface water monitoring stations have been constructed in Central and Highland Central regions with 100 station planned to be constructed by 2030 throughout Vietnam. For the Mekong Delta (MD), the Mekong River Commission (MRC) provides 7-day river level forecasting, but only at the two gauge stations located near the border between Cambodia and Vietnam (http://ffw.mrcmekong.org/south.htm). In order to help stakeholder agencies monitor upstream processes in the rivers and manage their impacts on the agricultural sector and densely populated delta cities, we, first of all, construct the so-called virtual stations throughout the entire Mekong River using the fully automated river level extraction tool with Jason-2/3 Geophysical Research Record (GDR) data. Then, we discuss the potentials and challenges of river level forecasting using Jason-2/3 Interim GDR (IGDR) data, which has 1 - 2 days of latency, over the Mekong River. Finally, based on our analyses, we propose a forecasting system for the Mekong River by drawing from our experience in operationalizing Jason-2 altimetry for Bangladesh flood forecasting.

Optimal Scaling of Aftershock Zones using Ground Motion Forecasts

NASA Astrophysics Data System (ADS)

Wilson, John Max; Yoder, Mark R.; Rundle, John B.

2018-02-01

The spatial distribution of aftershocks following major earthquakes has received significant attention due to the shaking hazard these events pose for structures and populations in the affected region. Forecasting the spatial distribution of aftershock events is an important part of the estimation of future seismic hazard. A simple spatial shape for the zone of activity has often been assumed in the form of an ellipse having semimajor axis to semiminor axis ratio of 2.0. However, since an important application of these calculations is the estimation of ground shaking hazard, an effective criterion for forecasting future aftershock impacts is to use ground motion prediction equations (GMPEs) in addition to the more usual approach of using epicentral or hypocentral locations. Based on these ideas, we present an aftershock model that uses self-similarity and scaling relations to constrain parameters as an option for such hazard assessment. We fit the spatial aspect ratio to previous earthquake sequences in the studied regions, and demonstrate the effect of the fitting on the likelihood of post-disaster ground motion forecasts for eighteen recent large earthquakes. We find that the forecasts in most geographic regions studied benefit from this optimization technique, while some are better suited to the use of the a priori aspect ratio.

Investigation of geomagnetic field forecasting and fluid dynamics of the core

NASA Technical Reports Server (NTRS)

Benton, E. R. (Principal Investigator)

1981-01-01

The magnetic determination of the depth of the core-mantle boundary using MAGSAT data is discussed. Refinements to the approach of using the pole-strength of Earth to evaluate the radius of the Earth's core-mantle boundary are reported. The downward extrapolation through the electrically conducting mantle was reviewed. Estimates of an upper bound for the time required for Earth's liquid core to overturn completely are presented. High order analytic approximations to the unsigned magnetic flux crossing the Earth's surface are also presented.

Comparison of Two Global Ocean Reanalyses, NRL Global Ocean Forecast System (GOFS) and U. Maryland Simple Ocean Data Assimilation (SODA)

NASA Astrophysics Data System (ADS)

Richman, J. G.; Shriver, J. F.; Metzger, E. J.; Hogan, P. J.; Smedstad, O. M.

2017-12-01

The Oceanography Division of the Naval Research Laboratory recently completed a 23-year (1993-2015) coupled ocean-sea ice reanalysis forced by NCEP CFS reanalysis fluxes. The reanalysis uses the Global Ocean Forecast System (GOFS) framework of the HYbrid Coordinate Ocean Model (HYCOM) and the Los Alamos Community Ice CodE (CICE) and the Navy Coupled Ocean Data Assimilation 3D Var system (NCODA). The ocean model has 41 layers and an equatorial resolution of 0.08° (8.8 km) on a tri-polar grid with the sea ice model on the same grid that reduces to 3.5 km at the North Pole. Sea surface temperature (SST), sea surface height (SSH) and temperature-salinity profile data are assimilated into the ocean every day. The SSH anomalies are converted into synthetic profiles of temperature and salinity prior to assimilation. Incremental analysis updating of geostrophically balanced increments is performed over a 6-hour insertion window. Sea ice concentration is assimilated into the sea ice model every day. Following the lead of the Ocean Reanalysis Intercomparison Project (ORA-IP), the monthly mean upper ocean heat and salt content from the surface to 300 m, 700m and 1500 m, the mixed layer depth, the depth of the 20°C isotherm, the steric sea surface height and the Atlantic Meridional Overturning Circulation for the GOFS reanalysis and the Simple Ocean Data Assimilation (SODA 3.3.1) eddy-permitting reanalysis have been compared on a global uniform 0.5° grid. The differences between the two ocean reanalyses in heat and salt content increase with increasing integration depth. Globally, GOFS trends to be colder than SODA at all depth. Warming trends are observed at all depths over the 23 year period. The correlation of the upper ocean heat content is significant above 700 m. Prior to 2004, differences in the data assimilated lead to larger biases. The GOFS reanalysis assimilates SSH as profile data, while SODA doesn't. Large differences are found in the Western Boundary Currents, Southern Ocean and equatorial regions. In the Indian Ocean, the Equatorial Counter Current extends to far to the east and the subsurface flow in the thermocline is too weak in GOFS. The 20°C isotherm is biased 2 m shallow in SODA compared to GOFS, but the monthly anomalies in the depth are highly correlated.

Updating the transportation plans in Virginia's small urban areas.

DOT National Transportation Integrated Search

1987-01-01

The Transportation Planning Division (TPD) of the Virginia Department of Transportation is responsible for developing transportation plans for areas in the state having a population greater than 3,500. Although transportation forecasting procedures f...

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...

Disturbance frequency and vertical distribution of seeds affect long-term population dynamics: a mechanistic seed bank model.

PubMed

Eager, Eric Alan; Haridas, Chirakkal V; Pilson, Diana; Rebarber, Richard; Tenhumberg, Brigitte

2013-08-01

Seed banks are critically important for disturbance specialist plants because seeds of these species germinate only in disturbed soil. Disturbance and seed depth affect the survival and germination probability of seeds in the seed bank, which in turn affect population dynamics. We develop a density-dependent stochastic integral projection model to evaluate the effect of stochastic soil disturbances on plant population dynamics with an emphasis on mimicking how disturbances vertically redistribute seeds within the seed bank. We perform a simulation analysis of the effect of the frequency and mean depth of disturbances on the population's quasi-extinction probability, as well as the long-term mean and variance of the total density of seeds in the seed bank. We show that increasing the frequency of disturbances increases the long-term viability of the population, but the relationship between the mean depth of disturbance and the long-term viability of the population are not necessarily monotonic for all parameter combinations. Specifically, an increase in the probability of disturbance increases the long-term viability of the total seed bank population. However, if the probability of disturbance is too low, a shallower mean depth of disturbance can increase long-term viability, a relationship that switches as the probability of disturbance increases. However, a shallow disturbance depth is beneficial only in scenarios with low survival in the seed bank.

Physician supply forecast: better than peering in a crystal ball?

PubMed Central

Roberfroid, Dominique; Leonard, Christian; Stordeur, Sabine

2009-01-01

Background Anticipating physician supply to tackle future health challenges is a crucial but complex task for policy planners. A number of forecasting tools are available, but the methods, advantages and shortcomings of such tools are not straightforward and not always well appraised. Therefore this paper had two objectives: to present a typology of existing forecasting approaches and to analyse the methodology-related issues. Methods A literature review was carried out in electronic databases Medline-Ovid, Embase and ERIC. Concrete examples of planning experiences in various countries were analysed. Results Four main forecasting approaches were identified. The supply projection approach defines the necessary inflow to maintain or to reach in the future an arbitrary predefined level of service offer. The demand-based approach estimates the quantity of health care services used by the population in the future to project physician requirements. The needs-based approach involves defining and predicting health care deficits so that they can be addressed by an adequate workforce. Benchmarking health systems with similar populations and health profiles is the last approach. These different methods can be combined to perform a gap analysis. The methodological challenges of such projections are numerous: most often static models are used and their uncertainty is not assessed; valid and comprehensive data to feed into the models are often lacking; and a rapidly evolving environment affects the likelihood of projection scenarios. As a result, the internal and external validity of the projections included in our review appeared limited. Conclusion There is no single accepted approach to forecasting physician requirements. The value of projections lies in their utility in identifying the current and emerging trends to which policy-makers need to respond. A genuine gap analysis, an effective monitoring of key parameters and comprehensive workforce planning are key elements to improving the usefulness of physician supply projections. PMID:19216772

Transforming Atmospheric and Remotely-Sensed Information to Hydrologic Predictability in South Asia

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Riddle, E. E.; Broman, D.; Brakenridge, G. R.; Birkett, C. M.; Kettner, A.; Sampson, K. M.; Boehnert, J.; Priya, S.; Collins, D. C.; Rostkier-Edelstein, D.; Young, W.; Singh, D.; Islam, A. S.

2017-12-01

South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally. Although close to 40% of the world's population depends on the Greater Himalaya's great rivers, $20 Billion of GDP is affected by river floods each year. The frequent occurrence of floods, combined with large and rapidly growing populations with high levels of poverty, make South Asia highly susceptible to humanitarian disasters. The challenges of mitigating such devastating disasters are exacerbated by the limited availability of real-time rain and stream gauge measuring stations and transboundary data sharing, and by constrained institutional commitments to overcome these challenges. To overcome such limitations, India and the World Bank have committed resources to the National Hydrology Project III, with the development objective to improve the extent, quality, and accessibility of water resources information and to strengthen the capacity of targeted water resources management institutions in India. The availability and application of remote sensing products and weather forecasts from ensemble prediction systems (EPS) have transformed river forecasting capability over the last decade, and is of interest to India. In this talk, we review the potential predictability of river flow contributed by some of the freely-available remotely-sensed and weather forecasting products within the framework of the physics of water migration through a watershed. Our specific geographical context is the Ganges, Brahmaputra, and Meghna river basin and a newly-available set of stream gauge measurements located over the region. We focus on satellite rainfall estimation, river height and width estimation, and EPS weather forecasts. For the later, we utilize the THORPEX-TIGGE dataset of global forecasts, and discuss how atmospheric predictability, as measured by an EPS, is transformed into hydrometeorological predictability. We provide an overview of the strengths and weaknesses of each of these data sets to the river flow prediction problem, generalizing their utility across spatial- and temporal-scales, and highlight the benefits of joint utilization and multi-modeling to minimize uncertainty and enhance operational robustness.

Snow: A New Model Diagnostic and Seasonal Forecast Influences

NASA Astrophysics Data System (ADS)

Slater, A. G.; Lawrence, D. M.; Koven, C.

2015-12-01

Snow is the most variable of terrestrial surface condition on the planet with the seasonal extent of snow cover varying by about 48% of land area in the Northern Hemisphere. Physical properties of snow such as high albedo, high insulation along with its ability to store moisture make it an integral component of mid- and high-latitude climates and it is therefore important that models capture these properties and associated processes. In this work we explore two items associated with snow and their role in the climate system. Firstly, a diagnostic measure of snow insulation that is rooted in the physics of heat transfer is introduced. Insulation of the ground during cold Arctic winters heavily influences the rate and depth of ground freezing (or thawing), which can then influence hydrologic and biogeochemical fluxes. The ability of models to simulate snow insulation varies widely. Secondly, the role of snow upon seasonal forecasts is demonstrated within a currently operational modeling system. Due to model system biases, mass and longevity of snow can vary with forecasts. In turn, a longer lasting and greater moisture store can have impacts upon the surface temperature. These impacts can linger for over two months after all snow has melted. The cause of the biases is identified and a solution posed.

Development and assessment of 30-meter pine density maps for landscape-level modeling of mountain pine beetle dynamics

Treesearch

Benjamin A. Crabb; James A. Powell; Barbara J. Bentz

2012-01-01

Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population...

Real-Time Population Health Detector

DTIC Science & Technology

2004-11-01

military and civilian populations. General Dynamics (then Veridian Systems Division), in cooperation with Stanford University, won a competitive DARPA...via the sequence of one-step ahead forecast errors from the Kalman recursions: 1| −−= tttt Hye µ The log-likelihood then follows by treating the... parking in the transient parking structure. Norfolk Area Military Treatment Facility Patient Files GDAIS received historic CHCS data from all

Development and application of a spatial IBM to forecast greater prairie-chicken population responses to land use in the Flint Hills region of Kansas - SCB meeting

EPA Science Inventory

Greater prairie-chicken (Tympanachus cupido) populations have been on the decline for decades. Recent efforts to reverse this trend are focusing on two specific disturbance regimes, cattle grazing and field burning, both prevalent in the Flint Hill region of Kansas -- an area of...

Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

NASA Astrophysics Data System (ADS)

Löptien, U.; Axell, L.

2014-12-01

The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62-67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian Sea during the severe winter 2011 and employs 15 to 25 min averages of ship speed.

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

Forecasting Social Unrest Using Activity Cascades

PubMed Central

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J.; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach. PMID:26091012

Forecasting Social Unrest Using Activity Cascades.

PubMed

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.

Obesity and severe obesity forecasts through 2030.

PubMed

Finkelstein, Eric A; Khavjou, Olga A; Thompson, Hope; Trogdon, Justin G; Pan, Liping; Sherry, Bettylou; Dietz, William

2012-06-01

Previous efforts to forecast future trends in obesity applied linear forecasts assuming that the rise in obesity would continue unabated. However, evidence suggests that obesity prevalence may be leveling off. This study presents estimates of adult obesity and severe obesity prevalence through 2030 based on nonlinear regression models. The forecasted results are then used to simulate the savings that could be achieved through modestly successful obesity prevention efforts. The study was conducted in 2009-2010 and used data from the 1990 through 2008 Behavioral Risk Factor Surveillance System (BRFSS). The analysis sample included nonpregnant adults aged ≥ 18 years. The individual-level BRFSS variables were supplemented with state-level variables from the U.S. Bureau of Labor Statistics, the American Chamber of Commerce Research Association, and the Census of Retail Trade. Future obesity and severe obesity prevalence were estimated through regression modeling by projecting trends in explanatory variables expected to influence obesity prevalence. Linear time trend forecasts suggest that by 2030, 51% of the population will be obese. The model estimates a much lower obesity prevalence of 42% and severe obesity prevalence of 11%. If obesity were to remain at 2010 levels, the combined savings in medical expenditures over the next 2 decades would be $549.5 billion. The study estimates a 33% increase in obesity prevalence and a 130% increase in severe obesity prevalence over the next 2 decades. If these forecasts prove accurate, this will further hinder efforts for healthcare cost containment. Copyright © 2012 Elsevier Inc. All rights reserved.

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.

PubMed

Moss, Robert; Zarebski, Alexander; Dawson, Peter; McCaw, James M

2016-07-01

Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system. Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia). We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14. Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support. © 2016 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019.

PubMed

Hendrie, Gilly A; Ullah, Shahid; Scott, Jane A; Gray, John; Berry, Narelle; Booth, Sue; Carter, Patricia; Cobiac, Lynne; Coveney, John

2015-12-01

Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019. Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function. The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females). Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development. © 2015 Public Health Association of Australia.

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

Changes in the relation between snow station observations and basin scale snow water resources

NASA Astrophysics Data System (ADS)

Sexstone, G. A.; Penn, C. A.; Clow, D. W.; Moeser, D.; Liston, G. E.

2017-12-01

Snow monitoring stations that measure snow water equivalent or snow depth provide fundamental observations used for predicting water availability and flood risk in mountainous regions. In the western United States, snow station observations provided by the Natural Resources Conservation Service Snow Telemetry (SNOTEL) network are relied upon for forecasting spring and summer streamflow volume. Streamflow forecast accuracy has declined for many regions over the last several decades. Changes in snow accumulation and melt related to climate, land use, and forest cover are not accounted for in current forecasts, and are likely sources of error. Therefore, understanding and updating relations between snow station observations and basin scale snow water resources is crucial to improve accuracy of streamflow prediction. In this study, we investigated the representativeness of snow station observations when compared to simulated basin-wide snow water resources within the Rio Grande headwaters of Colorado. We used the combination of a process-based snow model (SnowModel), field-based measurements, and remote sensing observations to compare the spatiotemporal variability of simulated basin-wide snow accumulation and melt with that of SNOTEL station observations. Results indicated that observations are comparable to simulated basin-average winter precipitation but overestimate both the simulated basin-average snow water equivalent and snowmelt rate. Changes in the representation of snow station observations over time in the Rio Grande headwaters were also investigated and compared to observed streamflow and streamflow forecasting errors. Results from this study provide important insight in the context of non-stationarity for future water availability assessments and streamflow predictions.

Evaluation of W Phase CMT Based PTWC Real-Time Tsunami Forecast Model Using DART Observations: Events of the Last Decade

NASA Astrophysics Data System (ADS)

Wang, D.; Becker, N. C.; Weinstein, S.; Duputel, Z.; Rivera, L. A.; Hayes, G. P.; Hirshorn, B. F.; Bouchard, R. H.; Mungov, G.

2017-12-01

The Pacific Tsunami Warning Center (PTWC) began forecasting tsunamis in real-time using source parameters derived from real-time Centroid Moment Tensor (CMT) solutions in 2009. Both the USGS and PTWC typically obtain W-Phase CMT solutions for large earthquakes less than 30 minutes after earthquake origin time. Within seconds, and often before waves reach the nearest deep ocean bottom pressure sensor (DARTs), PTWC then generates a regional tsunami propagation forecast using its linear shallow water model. The model is initialized by the sea surface deformation that mimics the seafloor deformation based on Okada's (1985) dislocation model of a rectangular fault with a uniform slip. The fault length and width are empirical functions of the seismic moment. How well did this simple model perform? The DART records provide a very valuable dataset for model validation. We examine tsunami events of the last decade with earthquake magnitudes ranging from 6.5 to 9.0 including some deep events for which tsunamis were not expected. Most of the forecast results were obtained during the events. We also include events from before the implementation of the WCMT method at USGS and PTWC, 2006-2009. For these events, WCMTs were computed retrospectively (Duputel et al. 2012). We also re-ran the model with a larger domain for some events to include far-field DARTs that recorded a tsunami with identical source parameters used during the events. We conclude that our model results, in terms of maximum wave amplitude, are mostly within a factor of two of the observed at DART stations, with an average error of less than 40% for most events, including the 2010 Maule and the 2011 Tohoku tsunamis. However, the simple fault model with a uniform slip is too simplistic for the Tohoku tsunami. We note model results are sensitive to centroid location and depth, especially if the earthquake is close to land or inland. For the 2016 M7.8 New Zealand earthquake the initial forecast underestimated the tsunami because the initial WCMT centroid was on land (the epicenter was inland but most of the slips occurred offshore). Later WCMTs did provide better forecast. The model also failed to reproduce the observed tsunamis from earthquake-generated landslides. Sea level observations during the events are crucial in determining whether or not a forecast needs to be adjusted.

New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product was developed for routine near-real time verification of these predictions. The footprint of the predicted smoke from identified fires is verified with satellite observations of the spatial extent of smoke aerosols (5km resolution). Based on geostationary aerosol optical depth measurements that provide good time resolution of the horizontal spatial extent of the plumes, these observations do not yield quantitative concentrations of smoke particles at the surface. Predicted surface smoke concentrations are consistent with the limited number of in situ observations of total fine particle mass from all sources; however they are much higher than predicted for most CONUS fires. To assess uncertainty associated with fire emissions estimates, sensitivity analyses are in progress.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

NASA Astrophysics Data System (ADS)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by itself.

Development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia

NASA Astrophysics Data System (ADS)

Lee, Sojin; Song, Chul-han; Park, Rae Seol; Park, Mi Eun; Han, Kyung man; Kim, Jhoon; Choi, Myungje; Ghim, Young Sung; Woo, Jung-Hun

2016-04-01

To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers a part of Northeast Asia (113-146° E; 25-47° N), were used. Although GOCI can provide a higher number of AOD data in a semicontinuous manner than low Earth orbit (LEO) satellite sensors, it still has a serious limitation in that the AOD data are not available at cloud pixels and over high-reflectance areas, such as desert and snow-covered regions. To overcome this limitation, a spatiotemporal-kriging (STK) method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages in using the STK method in this study is that more observed AOD data can be used to prepare the best initial AOD fields compared with other methods that use single frame of observation data around the time of initialization. It is demonstrated in this study that the short-term PM forecast system developed with the application of the STK method can greatly improve PM10 predictions in the Seoul metropolitan area (SMA) when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ˜ 60 and ˜ 70{%}, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, the influences of several factors on the performances of the short-term PM forecast were explored in this study. The influences of the choices of the control variables on the PM chemical composition were also investigated with the composition data measured via PILS-IC (particle-into-liquid sampler coupled with ion chromatography) and low air-volume sample instruments at a site near Seoul. To improve the overall performances of the short-term PM forecast system, several future research directions were also discussed and suggested.

Near Real{time Data Assimilation for the HYSPLIT Aerosol Dispersion Model

NASA Astrophysics Data System (ADS)

Kalpakis, K.; Yang, S.; Yesha, Y.

2010-12-01

Konstantinos Kalpakis, Shiming Yang, and Yaacov Yesha Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle, Baltimore, MD, U.S.A. {kalpakis, shiming1, yayeshag}@csee.umbc.edu ABSTRACT We are working on an IBM-funded project seeking to develop a prototype system for real-time plume dispersion and fire and smoke detection and monitoring. Our prototype system utilizes HYSPLIT and observation data from various sources. HYSPLIT is a model developed by NOAA's Air Resources Laboratory for forecasting aerosol trajectories, dispersion, and concentration from emission sources. It is used extensively by NOAA to routinely provide a number of data products. We develop a data assimilation system for assimilating observational data into the forecasting model in order to improve its forecasting accuracy. Our system is based on the Local Ensemble Transform Kalman Filter (LETKF) algorithm and it is computationally efficient. We evaluate our data assimilation system with real in-situ observational data, and find that our system improves upon HYSPLIT's forecast by reducing the normalized mean squared error and the bias. We are also experimenting with assimilating MODIS data with HYSPLIT model forecasts. To this end, we extrapolate ground concentrations from MODIS Aerosol Optical Depth (AOD) data. Our extrapolation approach relies on spatially localized linear regressions of aerosol concentrations from ground stations in the Air Quality System (AQS) network and MODIS AOD data. We expect that assimilating the extrapolated concentrations leads into further improvements of HYSPLIT forecasts. Furthermore, we are investigating using additional sources of in-situ and remotely sensed observations, such as GOES AOD 30-minute data, and UAV data from the Ikhana AMS fire missions. These sources provide higher spatial resolution and more frequent temporal coverage. Moreover, GOES and UAVs provide near-real time data which should be useful in improving HYSPLIT forecasts of smoke from wildfires. Currently, the Ikhana AMS fire missions team provides L1B data which are very useful in themselves, but no level 2 to the best of our knowledge. For our application, it would very useful to have an AOD data product for these datasets. A possible path for deriving AOD data the AMS sensor onboard UAVs would be to utilize the DRL code for deriving the MODIS AOD from MODIS L1B data, due to the sensor similarities. Developing such code would be very useful for wildfire smoke prediction applications. Our near real-time data assimilation system helps in bridging the gap between predictions and real-time observations, for more accurate and timely aerosol dispersion forecasts. Keywords: data assimilation, HYSPLIT, forecast model performance, real-time, ensemble Kalman filter, aerosol dispersion and concentration.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2011-07-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

A novel visualisation tool for climate services: a case study of temperature extremes and human mortality in Europe

NASA Astrophysics Data System (ADS)

Lowe, R.; Ballester, J.; Robine, J.; Herrmann, F. R.; Jupp, T. E.; Stephenson, D.; Rodó, X.

2013-12-01

Users of climate information often require probabilistic information on which to base their decisions. However, communicating information contained within a probabilistic forecast presents a challenge. In this paper we demonstrate a novel visualisation technique to display ternary probabilistic forecasts on a map in order to inform decision making. In this method, ternary probabilistic forecasts, which assign probabilities to a set of three outcomes (e.g. low, medium, and high risk), are considered as a point in a triangle of barycentric coordinates. This allows a unique colour to be assigned to each forecast from a continuum of colours defined on the triangle. Colour saturation increases with information gain relative to the reference forecast (i.e. the long term average). This provides additional information to decision makers compared with conventional methods used in seasonal climate forecasting, where one colour is used to represent one forecast category on a forecast map (e.g. red = ';dry'). We use the tool to present climate-related mortality projections across Europe. Temperature and humidity are related to human mortality via location-specific transfer functions, calculated using historical data. Daily mortality data at the NUTS2 level for 16 countries in Europe were obtain from 1998-2005. Transfer functions were calculated for 54 aggregations in Europe, defined using criteria related to population and climatological similarities. Aggregations are restricted to fall within political boundaries to avoid problems related to varying adaptation policies between countries. A statistical model is fit to cold and warm tails to estimate future mortality using forecast temperatures, in a Bayesian probabilistic framework. Using predefined categories of temperature-related mortality risk, we present maps of probabilistic projections for human mortality at seasonal to decadal time scales. We demonstrate the information gained from using this technique compared to more traditional methods to display ternary probabilistic forecasts. This technique allows decision makers to identify areas where the model predicts with certainty area-specific heat waves or cold snaps, in order to effectively target resources to those areas most at risk, for a given season or year. It is hoped that this visualisation tool will facilitate the interpretation of the probabilistic forecasts not only for public health decision makers but also within a multi-sectoral climate service framework.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

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

PubMed Central

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

2014-01-01

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

Comparison of Climatological Planetary Boundary Layer Depth Estimates Using the GEOS-5 AGCM

NASA Technical Reports Server (NTRS)

Mcgrath-Spangler, Erica Lynn; Molod, Andrea M.

2014-01-01

Planetary boundary layer (PBL) processes, including those influencing the PBL depth, control many aspects of weather and climate and accurate models of these processes are important for forecasting changes in the future. However, evaluation of model estimates of PBL depth are difficult because no consensus on PBL depth definition currently exists and various methods for estimating this parameter can give results that differ by hundreds of meters or more. In order to facilitate comparisons between the Goddard Earth Observation System (GEOS-5) and other modeling and observational systems, seven PBL depth estimation methods are used to produce PBL depth climatologies and are evaluated and compared here. All seven methods evaluate the same atmosphere so all differences are related solely to the definition chosen. These methods depend on the scalar diffusivity, bulk and local Richardson numbers, and the diagnosed horizontal turbulent kinetic energy (TKE). Results are aggregated by climate class in order to allow broad generalizations. The various PBL depth estimations give similar midday results with some exceptions. One method based on horizontal turbulent kinetic energy produces deeper PBL depths in the winter associated with winter storms. In warm, moist conditions, the method based on a bulk Richardson number gives results that are shallower than those given by the methods based on the scalar diffusivity. The impact of turbulence driven by radiative cooling at cloud top is most significant during the evening transition and along several regions across the oceans and methods sensitive to this cooling produce deeper PBL depths where it is most active. Additionally, Richardson number-based methods collapse better at night than methods that depend on the scalar diffusivity. This feature potentially affects tracer transport.

Population Differentiation and Species Formation in the Deep Sea: The Potential Role of Environmental Gradients and Depth

PubMed Central

Jennings, Robert M.; Etter, Ron J.; Ficarra, Lynn

2013-01-01

Ecological speciation probably plays a more prominent role in diversification than previously thought, particularly in marine ecosystems where dispersal potential is great and where few obvious barriers to gene flow exist. This may be especially true in the deep sea where allopatric speciation seems insufficient to account for the rich and largely endemic fauna. Ecologically driven population differentiation and speciation are likely to be most prevalent along environmental gradients, such as those attending changes in depth. We quantified patterns of genetic variation along a depth gradient (1600-3800m) in the western North Atlantic for a protobranch bivalve ( Nuculaatacellana ) to test for population divergence. Multilocus analyses indicated a sharp discontinuity across a narrow depth range, with extremely low gene flow inferred between shallow and deep populations for thousands of generations. Phylogeographical discordance occurred between nuclear and mitochondrial loci as might be expected during the early stages of species formation. Because the geographic distance between divergent populations is small and no obvious dispersal barriers exist in this region, we suggest the divergence might reflect ecologically driven selection mediated by environmental correlates of the depth gradient. As inferred for numerous shallow-water species, environmental gradients that parallel changes in depth may play a key role in the genesis and adaptive radiation of the deep-water fauna. PMID:24098590

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Linked population and economic models: some methodological issues in forecasting, analysis, and policy optimization.

PubMed

Madden, M; Batey Pwj

1983-05-01

Some problems associated with demographic-economic forecasting include finding models appropriate for a declining economy with unemployment, using a multiregional approach in an interregional model, finding a way to show differential consumption while endogenizing unemployment, and avoiding unemployment inconsistencies. The solution to these problems involves the construction of an activity-commodity framework, locating it within a group of forecasting models, and indicating possible ratios towards dynamization of the framework. The authors demonstrate the range of impact multipliers that can be derived from the framework and show how these multipliers relate to Leontief input-output multipliers. It is shown that desired population distribution may be obtained by selecting instruments from the economic sphere to produce, through the constraints vector of an activity-commodity framework, targets selected from demographic activities. The next step in this process, empirical exploitation, was carried out by the authors in the United Kingdom, linking an input-output model with a wide selection of demographic and demographic-economic variables. The generally tenuous control which government has over any variables in systems of this type, especially in market economies, makes application in the policy field of the optimization approach a partly conjectural exercise, although the analytic capacity of the approach can provide clear indications of policy directions.

Improved management of small pelagic fisheries through seasonal climate prediction.

PubMed

Tommasi, Désirée; Stock, Charles A; Pegion, Kathleen; Vecchi, Gabriel A; Methot, Richard D; Alexander, Michael A; Checkley, David M

2017-03-01

Populations of small pelagic fish are strongly influenced by climate. The inability of managers to anticipate environment-driven fluctuations in stock productivity or distribution can lead to overfishing and stock collapses, inflexible management regulations inducing shifts in the functional response to human predators, lost opportunities to harvest populations, bankruptcies in the fishing industry, and loss of resilience in the human food supply. Recent advances in dynamical global climate prediction systems allow for sea surface temperature (SST) anomaly predictions at a seasonal scale over many shelf ecosystems. Here we assess the utility of SST predictions at this "fishery relevant" scale to inform management, using Pacific sardine as a case study. The value of SST anomaly predictions to management was quantified under four harvest guidelines (HGs) differing in their level of integration of SST data and predictions. The HG that incorporated stock biomass forecasts informed by skillful SST predictions led to increases in stock biomass and yield, and reductions in the probability of yield and biomass falling below socioeconomic or ecologically acceptable levels. However, to mitigate the risk of collapse in the event of an erroneous forecast, it was important to combine such forecast-informed harvest controls with additional harvest restrictions at low biomass. © 2016 by the Ecological Society of America.

National Transportation Noise Mapping Tool

DOT National Transportation Integrated Search

2017-03-01

By most forecasts, the U.S. population is projected to grow by over 100 million by 2050. As demand for transportation increases, transportation-related noise will also change. The Bureau of Transportation Statistics (BTS) has started a national, mult...

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

NASA Technical Reports Server (NTRS)

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

1973-01-01

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

Benefits of volcano monitoring far outweigh costs - the case of Mount Pinatubo

USGS Publications Warehouse

Newhall, Chris G.; Hendley, James W.; Stauffer, Peter H.

1997-01-01

The climactic June 1991 eruption of Mount Pinatubo, Philippines, was the largest volcanic eruption in this century to affect a heavily populated area. Because it was forecast by scientists from the Philippine Institute of Volcanology and Seismology and the U.S. Geological Survey, civil and military leaders were able to order massive evacuations and take measures to protect property before the eruption. Thousands of lives were saved and hundreds of millions of dollars in property losses averted. The savings in property alone were many times the total costs of the forecasting and evacuations.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

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

PubMed Central

Reschovsky, Andrew; Zahner, Susan J.

2015-01-01

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

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

PubMed

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

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

Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus

PubMed Central

Smith, Jack W.; Everhart, J.E.; Dickson, W.C.; Knowler, W.C.; Johannes, R.S.

1988-01-01

Neural networks or connectionist models for parallel processing are not new. However, a resurgence of interest in the past half decade has occurred. In part, this is related to a better understanding of what are now referred to as hidden nodes. These algorithms are considered to be of marked value in pattern recognition problems. Because of that, we tested the ability of an early neural network model, ADAP, to forecast the onset of diabetes mellitus in a high risk population of Pima Indians. The algorithm's performance was analyzed using standard measures for clinical tests: sensitivity, specificity, and a receiver operating characteristic curve. The crossover point for sensitivity and specificity is 0.76. We are currently further examining these methods by comparing the ADAP results with those obtained from logistic regression and linear perceptron models using precisely the same training and forecasting sets. A description of the algorithm is included.

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Cuffey, R.J.; Pachut, J.F.

The Holocene reef-building coral Favia pallida was sampled at 4.5 m depth increments (to 40 m) from two reefs on Enewetak Atoll to examine intraspecific environmental effects. An exposed outer reef was massive and wall-like, whereas a sheltered lagoonal reef grew as a slender pinnacle. Corallite diameter and growth rate, two attributes retrievable in fossil corals, were measured with data partitioned into shallow (<20 m), intermediate (20 to 29 m), and deep-water (>29 m) subsets. Highly significant differences between depth zone populations were found for both corallite diameters and growth rates in analyses of individual and combined reef data sets.more » Canonical variates analyses (CVA) separated populations from depth zones along single, highly significant, functions. Centroids and 95% confidence intervals, calculated from CVA scores of colonies in each population, are widely separated for the lagoon reef and combined data sets. Conversely, populations from shallow and intermediate depths on the outer reef display overlapping confidence bars indicative of more gradational morphologic changes. When CV's were used to classify specimens to groups, misassignments of intermediate depth specimens to shallow or deep-water populations underscored the gradational nature of the environment. Completely intergrading populations of Favia pallida collected from different depths can be morphologically separated into statistically distinct groupings. A stratigraphic succession of such morphotypes might be interpreted as abruptly appearing separate species if sampling were not as uniform, systematic, and detailed as was possible on modern reefs. Analyses of evolutionary patterns must carefully assess potential effects of clinal variation if past evolutionary patterns are to be interpreted correctly.« less

State-space modeling to support management of brucellosis in the Yellowstone bison population

USGS Publications Warehouse

Hobbs, N. Thompson; Geremia, Chris; Treanor, John; Wallen, Rick; White, P.J.; Hooten, Mevin B.; Rhyan, Jack C.

2015-01-01

The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency-dependent model, brucellosis median transmission rate was 1.87 yr−1. The median of the posterior distribution of the basic reproductive ratio (R0) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate λ averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of achieving goals using interventions relative to no action. Because the width of the posterior predictive distributions of future population states expands rapidly with increases in the forecast horizon, managers must accept high levels of uncertainty. These findings emphasize the necessity of iterative, adaptive management with relatively short-term commitment to action and frequent reevaluation in response to new data and model forecasts. We believe our approach has broad applications.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2

NASA Astrophysics Data System (ADS)

Saha, Subodh Kumar; Sujith, K.; Pokhrel, Samir; Chaudhari, Hemantkumar S.; Hazra, Anupam

2017-03-01

The Noah version 2.7.1 is a moderately complex land surface model (LSM), with a single layer snowpack, combined with vegetation and underlying soil layer. Many previous studies have pointed out biases in the simulation of snow, which may hinder the skill of a forecasting system coupled with the Noah. In order to improve the simulation of snow by the Noah, a multilayer snow scheme (up to a maximum of six layers) is introduced. As Noah is the land surface component of the Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP), the modified Noah is also coupled with the CFSv2. The offline LSM shows large improvements in the simulation of snow depth, snow water equivalent (SWE), and snow cover area during snow season (October to June). CFSv2 with the modified Noah reveals a dramatic improvements in the simulation of snow depth and 2 m air temperature and moderate improvements in SWE. As suggested in the previous diagnostic and sensitivity study, improvements in the simulation of snow by CFSv2 have lead to the reduction in dry bias over the Indian subcontinent (by a maximum of 2 mm d-1). The multilayer snow scheme shows promising results in the simulation of snow as well as Indian summer monsoon rainfall and hence this development may be the part of the future version of the CFS.

Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic

NASA Astrophysics Data System (ADS)

Shevnina, Elena; Kourzeneva, Ekaterina; Kovalenko, Viktor; Vihma, Timo

2017-05-01

Climate warming has been more acute in the Arctic than at lower latitudes and this tendency is expected to continue. This generates major challenges for economic activity in the region. Among other issues is the long-term planning and development of socio-economic infrastructure (dams, bridges, roads, etc.), which require climate-based forecasts of the frequency and magnitude of detrimental flood events. To estimate the cost of the infrastructure and operational risk, a probabilistic form of long-term forecasting is preferable. In this study, a probabilistic model to simulate the parameters of the probability density function (PDF) for multi-year runoff based on a projected climatology is applied to evaluate changes in extreme floods for the territory of the Russian Arctic. The model is validated by cross-comparison of the modelled and empirical PDFs using observations from 23 sites located in northern Russia. The mean values and coefficients of variation (CVs) of the spring flood depth of runoff are evaluated under four climate scenarios, using simulations of six climate models for the period 2010-2039. Regions with substantial expected changes in the means and CVs of spring flood depth of runoff are outlined. For the sites located within such regions, it is suggested to account for the future climate change in calculating the maximal discharges of rare occurrence. An example of engineering calculations for maximal discharges with 1 % exceedance probability is provided for the Nadym River at Nadym.

Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

PubMed Central

Chan, Emily H.; Scales, David A.; Brewer, Timothy F.; Madoff, Lawrence C.; Pollack, Marjorie P.; Hoen, Anne G.; Choden, Tenzin; Brownstein, John S.

2013-01-01

Background. Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. Methods. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Results. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996–2008. Conclusions. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions. PMID:23118271

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

NASA Astrophysics Data System (ADS)

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

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

PACIFIC NORTHWEST SALMON: FORECASTING THEIR STATUS IN 2100

EPA Science Inventory

Throughout the Pacific Northwest (northern California, Oregon, Idaho, Washington, and the Columbia Basin portion of British Columbia), many wild salmon stocks (a group of interbreeding individuals that is roughly equivalent to a "population") have declined and some have disappear...

The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV

NASA Astrophysics Data System (ADS)

Ho, Y.; Weber, J.

2017-12-01

WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.

Depth-area-duration characteristics of storm rainfall in Texas using Multi-Sensor Precipitation Estimates

NASA Astrophysics Data System (ADS)

McEnery, J. A.; Jitkajornwanich, K.

2012-12-01

This presentation will describe the methodology and overall system development by which a benchmark dataset of precipitation information has been used to characterize the depth-area-duration relations in heavy rain storms occurring over regions of Texas. Over the past two years project investigators along with the National Weather Service (NWS) West Gulf River Forecast Center (WGRFC) have developed and operated a gateway data system to ingest, store, and disseminate NWS multi-sensor precipitation estimates (MPE). As a pilot project of the Integrated Water Resources Science and Services (IWRSS) initiative, this testbed uses a Standard Query Language (SQL) server to maintain a full archive of current and historic MPE values within the WGRFC service area. These time series values are made available for public access as web services in the standard WaterML format. Having this volume of information maintained in a comprehensive database now allows the use of relational analysis capabilities within SQL to leverage these multi-sensor precipitation values and produce a valuable derivative product. The area of focus for this study is North Texas and will utilize values that originated from the West Gulf River Forecast Center (WGRFC); one of three River Forecast Centers currently represented in the holdings of this data system. Over the past two decades, NEXRAD radar has dramatically improved the ability to record rainfall. The resulting hourly MPE values, distributed over an approximate 4 km by 4 km grid, are considered by the NWS to be the "best estimate" of rainfall. The data server provides an accepted standard interface for internet access to the largest time-series dataset of NEXRAD based MPE values ever assembled. An automated script has been written to search and extract storms over the 18 year period of record from the contents of this massive historical precipitation database. Not only can it extract site-specific storms, but also duration-specific storms and storms separated by user defined inter-event periods. A separate storm database has been created to store the selected output. By storing output within tables in a separate database, we can make use of powerful SQL capabilities to perform flexible pattern analysis. Previous efforts have made use of historic data from limited clusters of irregularly spaced physical gauges. Spatial extent of the observational network has been a limiting factor. The relatively dense distribution of MPE provides a virtual mesh of observations stretched over the landscape. This work combines a unique hydrologic data resource with programming and database analysis to characterize storm depth-area-duration relationships.

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

USGS Publications Warehouse

Klett, T.R.

2005-01-01

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

Definition of perspective scheme of organization of traffic using methods of forecasting and modeling

NASA Astrophysics Data System (ADS)

Vlasov, V. M.; Novikov, A. N.; Novikov, I. A.; Shevtsova, A. G.

2018-03-01

In the environment of highly developed urban agglomerations, one of the main problems arises - inability of the road network to reach a high level of motorization. The introduction of intelligent transport systems allows solving this problem, but the main issue in their implementation remains open: to what extent this or that method of improving the transport network will be effective and whether it is able to solve the problem of vehicle growth especially for the long-term period. The main goal of this work was the development of an approach to forecasting the increase in the intensity of traffic flow for a long-term period using the population and the level of motorization. The developed approach made it possible to determine the projected population and, taking into account the level of motorization, to determine the growth factor of the traffic flow intensity, which allows calculating the intensity value for a long-term period with high accuracy. The analysis of the main methods for predicting the characteristics of the transport stream is performed. The basic values and parameters necessary for their use are established. The analysis of the urban settlement is carried out and the level of motorization characteristic for the given locality is determined. A new approach to predicting the intensity of the traffic flow has been developed, which makes it possible to predict the change in the transport situation in the long term in high accuracy. Calculations of the magnitude of the intensity increase on the basis of the developed forecasting method are made and the errors in the data obtained are determined. The main recommendations on the use of the developed forecasting approach for the long-term functioning of the road network are formulated.

Coupling Recruitment Forecasts with Economics in the Gulf of Maine's American Lobster Fishery

NASA Astrophysics Data System (ADS)

Wahle, R.; Oppenheim, N.; Brady, D. C.; Dayton, A.; Sun, C. H. J.

2016-02-01

Accurate predictions of fishery recruitment and landings represent an important goal of fisheries science and management, but linking environmental drivers of fish population dynamics to financial markets remains a challenge. A fundamental step in that process is understanding the environmental drivers of fishery recruitment. American lobster (Homarus americanus) populations of the northwest Atlantic have been undergoing a dramatic surge, mostly driven by increases the Gulf of Maine. Settler-recruit models that track cohorts after larvae settle to the sea bed are proving useful in predicting subsequent fishery recruitment some 5-7 years later. Here we describe new recruitment forecasting models for the lobster fishery at 11 management areas from Southern New England to Atlantic Canada. We use an annual survey of juvenile year-class strength and environmental indicators to parameterize growth and mortality terms in the model. As a consequence of a recent widespread multi-year downturn in larval settlement, our models suggest that the peak in lobster abundance in the Gulf of Maine will be passed in the near future. We also present initial steps in the coupling of forecast data with economic models for the fishery. We anticipate that these models will give stakeholders and policy makers time to consider their management choices for this most valuable of the region's fisheries. Our vision is to couple our forecast model outputs to an economic model that captures the dynamics of market forces in the New England and Canadian Maritime lobster fisheries. It will then be possible to estimate the financial status of the fishery several years in advance. This early warning system could mitigate the adverse effects of a fluctuating fishery on the coastal communities that are perilously dependent upon it.

Process-based modeling of species' responses to climate change - a proof of concept using western North American trees

NASA Astrophysics Data System (ADS)

Evans, M. E.; Merow, C.; Record, S.; Menlove, J.; Gray, A.; Cundiff, J.; McMahon, S.; Enquist, B. J.

2013-12-01

Current attempts to forecast how species' distributions will change in response to climate change suffer under a fundamental trade-off: between modeling many species superficially vs. few species in detail (between correlative vs. mechanistic models). The goals of this talk are two-fold: first, we present a Bayesian multilevel modeling framework, dynamic range modeling (DRM), for building process-based forecasts of many species' distributions at a time, designed to address the trade-off between detail and number of distribution forecasts. In contrast to 'species distribution modeling' or 'niche modeling', which uses only species' occurrence data and environmental data, DRMs draw upon demographic data, abundance data, trait data, occurrence data, and GIS layers of climate in a single framework to account for two processes known to influence range dynamics - demography and dispersal. The vision is to use extensive databases on plant demography, distributions, and traits - in the Botanical Information and Ecology Network, the Forest Inventory and Analysis database (FIA), and the International Tree Ring Data Bank - to develop DRMs for North American trees. Second, we present preliminary results from building the core submodel of a DRM - an integral projection model (IPM) - for a sample of dominant tree species in western North America. IPMs are used to infer demographic niches - i.e., the set of environmental conditions under which population growth rate is positive - and project population dynamics through time. Based on >550,000 data points derived from FIA for nine tree species in western North America, we show IPM-based models of their current and future distributions, and discuss how IPMs can be used to forecast future forest productivity, mortality patterns, and inform efforts at assisted migration.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

PubMed

Watson, Stella C; Liu, Yan; Lund, Robert B; Gettings, Jenna R; Nordone, Shila K; McMahan, Christopher S; Yabsley, Michael J

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

PubMed Central

Watson, Stella C.; Liu, Yan; Lund, Robert B.; Gettings, Jenna R.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge. PMID:28472096

Time lapse photography as an approach to understanding glide avalanche activity

USGS Publications Warehouse

Hendrikx, Jordy; Peitzsch, Erich H.; Fagre, Daniel B.

2012-01-01

Avalanches resulting from glide cracks are notoriously difficult to forecast, but are a recurring problem for numerous avalanche forecasting programs. In some cases glide cracks are observed to open and then melt away in situ. In other cases, they open and then fail catastrophically as large, full-depth avalanches. Our understanding and management of these phenomena are currently limited. It is thought that an increase in the rate of snow gliding occurs prior to full-depth avalanche activity so frequent observation of glide crack movement can provide an index of instability. During spring 2011 in Glacier National Park, Montana, USA, we began an approach to track glide crack avalanche activity using a time-lapse camera focused on a southwest facing glide crack. This crack melted in-situ without failing as a glide avalanche, while other nearby glide cracks on north through southeast aspects failed. In spring 2012, a camera was aimed at a large and productive glide crack adjacent to the Going to the Sun Road. We captured three unique glide events in the field of view. Unfortunately, all of them either failed very quickly, or during periods of obscured view, so measurements of glide rate could not be obtained. However, we compared the hourly meteorological variables during the period of glide activity to the same variables prior to glide activity. The variables air temperature, relative humidity, air pressure, incoming and reflected long wave radiation, SWE, total precipitation, and snow depth were found to be statistically different for our cases examined. We propose that these are some of the potential precursors for glide avalanche activity, but do urge caution in their use, due to the simple approach and small data set size. It is hoped that by introducing a workable method to easily record glide crack movement, combined with ongoing analysis of the associated meteorological data, we will improve our understanding of when, or if, glide avalanche activity will ensue.

Using Seismic Signals to Forecast Volcanic Processes

NASA Astrophysics Data System (ADS)

Salvage, R.; Neuberg, J. W.

2012-04-01

Understanding seismic signals generated during volcanic unrest have the ability to allow scientists to more accurately predict and understand active volcanoes since they are intrinsically linked to rock failure at depth (Voight, 1988). In particular, low frequency long period signals (LP events) have been related to the movement of fluid and the brittle failure of magma at depth due to high strain rates (Hammer and Neuberg, 2009). This fundamentally relates to surface processes. However, there is currently no physical quantitative model for determining the likelihood of an eruption following precursory seismic signals, or the timing or type of eruption that will ensue (Benson et al., 2010). Since the beginning of its current eruptive phase, accelerating LP swarms (< 10 events per hour) have been a common feature at Soufriere Hills volcano, Montserrat prior to surface expressions such as dome collapse or eruptions (Miller et al., 1998). The dynamical behaviour of such swarms can be related to accelerated magma ascent rates since the seismicity is thought to be a consequence of magma deformation as it rises to the surface. In particular, acceleration rates can be successfully used in collaboration with the inverse material failure law; a linear relationship against time (Voight, 1988); in the accurate prediction of volcanic eruption timings. Currently, this has only been investigated for retrospective events (Hammer and Neuberg, 2009). The identification of LP swarms on Montserrat and analysis of their dynamical characteristics allows a better understanding of the nature of the seismic signals themselves, as well as their relationship to surface processes such as magma extrusion rates. Acceleration and deceleration rates of seismic swarms provide insights into the plumbing system of the volcano at depth. The application of the material failure law to multiple LP swarms of data allows a critical evaluation of the accuracy of the method which further refines current understanding of the relationship between seismic signals and volcanic eruptions. It is hoped that such analysis will assist the development of real time forecasting models.

USING ECO-EVOLUTIONARY INDIVIDUAL-BASED MODELS TO INVESTIGATE SPATIALLY-DEPENDENT PROCESSES IN CONSERVATION GENETICS

EPA Science Inventory

Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...

Validation of new satellite aerosol optical depth retrieval algorithm using Raman lidar observations at radiative transfer laboratory in Warsaw

NASA Astrophysics Data System (ADS)

Zawadzka, Olga; Stachlewska, Iwona S.; Markowicz, Krzysztof M.; Nemuc, Anca; Stebel, Kerstin

2018-04-01

During an exceptionally warm September of 2016, the unique, stable weather conditions over Poland allowed for an extensive testing of the new algorithm developed to improve the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) aerosol optical depth (AOD) retrieval. The development was conducted in the frame of the ESA-ESRIN SAMIRA project. The new AOD algorithm aims at providing the aerosol optical depth maps over the territory of Poland with a high temporal resolution of 15 minutes. It was tested on the data set obtained between 11-16 September 2016, during which a day of relatively clean atmospheric background related to an Arctic airmass inflow was surrounded by a few days with well increased aerosol load of different origin. On the clean reference day, for estimating surface reflectance the AOD forecast available on-line via the Copernicus Atmosphere Monitoring Service (CAMS) was used. The obtained AOD maps were validated against AODs available within the Poland-AOD and AERONET networks, and with AOD values obtained from the PollyXT-UW lidar. of the University of Warsaw (UW).

Rate of recovery from perturbations as a means to forecast future stability of living systems.

PubMed

Ghadami, Amin; Gourgou, Eleni; Epureanu, Bogdan I

2018-06-18

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

Bayesian Probabilistic Projections of Life Expectancy for All Countries

PubMed Central

Raftery, Adrian E.; Chunn, Jennifer L.; Gerland, Patrick; Ševčíková, Hana

2014-01-01

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world from the present to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we did an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995, and using the estimated model to forecast for the subsequent ten years. The ten-year predictions had a mean absolute error of about 1 year, about 40% less than the current UN methodology. The probabilistic forecasts were calibrated, in the sense that (for example) the 80% prediction intervals contained the truth about 80% of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free publicly available R software packages called bayesLife and bayesDem are available to implement the method. PMID:23494599

Flood-inundation maps for the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, New Hampshire

USGS Publications Warehouse

Flynn, Robert H.; Johnston, Craig M.; Hays, Laura

2012-01-01

Digital flood-inundation maps for a 16.5-mile reach of the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, N.H., from the confluence with the Merrimack River to U.S. Geological Survey (USGS) Suncook River streamgage 01089500 at Depot Road in North Chichester, N.H., were created by the USGS in cooperation with the New Hampshire Department of Homeland Security and Emergency Management. The inundation maps presented in this report depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Suncook River at North Chichester, N.H. (station 01089500). The current conditions at the USGS streamgage may be obtained on the Internet (http://waterdata.usgs.gov/nh/nwis/uv/?site_no=01089500&PARAmeter_cd=00065,00060). The National Weather Service forecasts flood hydrographs at many places that are often collocated with USGS streamgages. Forecasted peak-stage information is available on the Internet at the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) flood-warning system site (http://water.weather.gov/ahps/) and may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. These maps along with real-time stream stage data from the USGS Suncook River streamgage (station 01089500) and forecasted stream stage from the NWS will provide emergency management personnel and residents with information that is critical for flood-response activities, such as evacuations, road closures, disaster declarations, and post-flood recovery. The maps, along with current stream-stage data from the USGS Suncook River streamgage and forecasted stream-stage data from the NWS, can be accessed at the USGS Flood Inundation Mapping Science Web site http://water.usgs.gov/osw/flood_inundation/.

Oviposition traps to survey eggs of Lambdina fiscellaria (Lepidoptera: Geometridae).

PubMed

Hébert, Christian; Jobin, Luc; Auger, Michel; Dupont, Alain

2003-06-01

Outbreaks of the hemlock looper, Lambdina fiscellaria (Gueneé), are characterized by rapid increase and patchy distribution over widespread areas, which make it difficult to detect impending outbreaks. This is a major problem with this insect. Population forecasting is based on tedious and expensive egg surveys in which eggs are extracted from 1-m branches; careful observation is needed to avoid counting old unhatched eggs of previous year populations. The efficacy of artificial substrates as oviposition traps to sample hemlock looper eggs was tested as a means of improving outbreak detection and population forecasting. A white polyurethane foam substrate (1,095 lb/ft3) used with the Luminoc insect trap, a portable light trap, was highly efficient in sampling eggs of the hemlock looper. Foam strips placed on tree trunks at breast height were less efficient but easier and less expensive to use for the establishment of extensive survey networks. Estimates based on oviposition traps were highly correlated with those obtained from the 1-m branch extraction method. The oviposition trap is a standard, inexpensive, easy, and robust method that can be used by nonspecialists. This technique makes it possible to sample higher numbers of plots in widespread monitoring networks, which is crucial for improving the management of hemlock looper populations.

Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast

NASA Astrophysics Data System (ADS)

Gordin, Vladimir; Bykov, Philipp

2015-04-01

We use the known statistics of the calls for the current and previous days to predict them for tomorrow and for the following days. We assume that this algorithm will work operatively, will cyclically update the available information and will move the horizon of the forecast. Sure, the accuracy of such forecasts depends on their lead time, and from a choice of some group of diagnoses. For comparison we used the error of the inertial forecast (tomorrow there will be the same number of calls as today). Our technology has demonstrated accuracy that is approximately two times better compared to the inertial forecast. We obtained the following result: the number of calls depends on the actual weather in the city as well as on its rate of change. We were interested in the accuracy of the forecast for 12-hour sum of the calls in real situations. We evaluate the impact of the meteorological errors [1] on the forecast errors of the number of Ambulance calls. The weather and the Ambulance calls number both have seasonal tendencies. Therefore, if we have medical information from one city only, we should separate the impacts of such predictors as "annual variations in the number of calls" and "weather". We need to consider the seasonal tendencies (associated, e. g. with the seasonal migration of the population) and the impact of the air temperature simultaneously, rather than sequentially. We forecasted separately the number of calls with diagnoses of cardiovascular group, where it was demonstrated the advantage of the forecasting method, when we use the maximum daily air temperature as a predictor. We have a chance to evaluate statistically the influence of meteorological factors on the dynamics of medical problems. In some cases it may be useful for understanding of the physiology of disease and possible treatment options. We can assimilate some personal archives of medical parameters for the individuals with concrete diseases and the relative meteorological archive. As a result we hope to evaluate how weather can influence the intensity of the disease. Thus, the knowledge of the weather forecast for several days will help us to predict a state of health. The person will be able to take some proactive actions to avoid the anticipated worsening of his health. Literature 1. A. N. Bagrov, F. L. Bykov, V. A. Gordin. Complex Forecast of Surface Meteorological Parameters. Meteorology and Hydrology, 2014, N 5, 5-16 (Russian), 283-291 (English). 2. Bykov, Ph.L., Gordin, V.A., Objective Analysis of the Structure of Three-Dimensional Atmospheric Fronts. Izvestia of Russian Academy of Sciences. Ser. The Physics of Atmosphere and Ocean, 48 (2) (2012), 172-188 (Russian), 152-168 (English), http://dx.doi.org/10.1134/S0001433812020053 3. V.A.Gordin. Mathematical Problems and Methods in Hydrodynamical Weather Forecasting. Amsterdam etc.: Gordon & Breach Publ. House, 2000. 4. V.A.Gordin. Mathematics, Computer, Weather Forecasting, and Other Mathematical Physics' Scenarios. Moscow, Fizmatlit, 2010, 2012 (Russian).

Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

NASA Astrophysics Data System (ADS)

Zhang, Yang; Hong, Chaopeng; Yahya, Khairunnisa; Li, Qi; Zhang, Qiang; He, Kebin

2016-08-01

An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance statistics. The model's forecasting skills for air quality can be further enhanced through improving model inputs (e.g., anthropogenic emissions for urban areas and upper boundary conditions of chemical species), meteorological forecasts (e.g., WS10, Precip) and meteorologically-dependent emissions (e.g., biogenic and wildfire emissions), and model physics and chemical treatments (e.g., gas-phase chemistry in winter conditions, cloud processes and their interactions with radiation and aerosol).

Sponge distribution across Davies Reef, Great Barrier Reef, relative to location, depth, and water movement

NASA Astrophysics Data System (ADS)

Wilkinson, Clive R.; Evans, Elizabeth

1989-06-01

Sponge populations were surveyed at different depths in three zones of Davies Reef, a large platform reef of the central Great Barrier Reef. Depth is the major discriminatory factor as few sponges are found within the first 10 m depth and maximal populations occur between 15 m and 30 m on fore-reef, lagoon and back-reef slopes. Reef location is another major factor, with the lagoon containing a significantly different sponge population to either the fore-reef or the back-reef slopes. Physical factors are considered to be the major influences behind these patterns. Physical turbulence is strongest within the first 10 m and apparently limits sponge growth within these shallow zones. Insufficient photosynthetic radiation limits the growth of the sponge population below 30 m depth as many of the species are phototrophic with a dependence on cyanobacterial symbionts for nutrition. Sponge populations on the outer (fore- and back-) reef slopes are comparable with each other but different from those on lagoon slopes where currents are reduced and fine sediment loads are higher. The largest populations occur on the back-reef slope where currents are stronger and there are possibly higher concentrations of organic nutrients originating from the more productive shallow parts of the reef. While there are correlations between sponge populations and environmental parameters, data are insufficient to enable more definitive conclusions to be drawn. Most sponge species are distributed widely over the reef, however, some are restricted to a few habitats and, hence, may be used to characterize those habitats.

The Relationship of Physical Property Indicators and Clay Soil Structural Strength of Tomsk Oblast Territory

NASA Astrophysics Data System (ADS)

Kramarenko, V. V.; Nikitenkov, A. N.; Molokov, V. Y.; Matveenko, I. A.; Shramok, A. V.

2015-11-01

The article deals with the characteristic of initial condition in fine-grained soils - its structural strength - pstr. Estimation and measurement of this factor at soil testing are of primary importance for defining its physical and mechanical properties as well as for subsequent calculation of foundation settlements that is insufficiently covered in Code of practice, national standard and inefficiently applicable in practice of engineering geological investigations. The article reveals the relationship between soil physical property, its occurrence depth, which will make possible to forecast pstr over the given territory.

Existing generating assets squeezed as new project starts slow

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

Jones, R.B.; Tiffany, E.D.

Most forecasting reports concentrate on political or regulatory events to predict future industry trends. Frequently overlooked are the more empirical performance trends of the principal power generation technologies. Solomon and Associates queried its many power plant performance databases and crunched some numbers to identify those trends. Areas of investigation included reliability, utilization (net output factor and net capacity factor) and cost (operating costs). An in-depth analysis for North America and Europe is presented in this article, by region and by regeneration technology. 4 figs., 2 tabs.

World helicopter market study

NASA Technical Reports Server (NTRS)

Cleary, B.; Pearson, R. W.; Greenwood, S. W.; Kaplan, L.

1978-01-01

The extent of the threat to the US helicopter industry posed by a determined effort by foreign manufacturers, European companies in particular, to supply their own domestic markets and also to penetrate export markets, including the USA is assessed. Available data on US and world markets for civil and military uses are collated and presented in both graphic and tabular form showing the past history of production and markets and, where forecasts are available, anticipated future trends. The data are discussed on an item-by-item basis and inferences are drawn in as much depth as appears justified.

Proposed concept and preliminary design for the sentinel-5 UVNs spectrometer

NASA Astrophysics Data System (ADS)

Windpassinger, R.; Schubert, J.; Kampf, D.

2017-11-01

Sentinel-5 is an atmospheric monitoring mission within the European Copernicus programme, formerly GMES (Global Monitoring for Environment and Security). Its main objective is trace-gas and aerosol optical depth measurements for air quality and climate monitoring and forecast with daily global coverage. Constituents of interest are O3, SO2, HCHO (formaldehyde), BrO, NO2, CHCHO (glyoxal), O2, CH4 (methane), and CO. Sentinel-5 will complement the Sentinel-4 GEO data over Europe. Both Sentinel-4 and -5 are intended to start operation in 2020.

An Examination of a Multi-Scale Three-Dimensional Variational Data Assimilation Scheme in the Kuroshio Extension Using the Naval Coastal Ocean Model

DTIC Science & Technology

2014-01-01

forecasts. Oceanic applications of the MS3DVAR have been Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ csr ...intervals of 12 h. The model grid location and depth can be seen in Fig. 2. In this region the Kuroshio appears as a narrow large magni- tude current in...1997. Introduction to high-frequency radar: reality and myth. Oceanography 10, 36–39. Qu, T., Mitsudera, H., Qui, B., 2001. A climatological view

Investigations using data from LANDSAT-2. [earth resources program maps forecasting

NASA Technical Reports Server (NTRS)

Hossain, A. (Principal Investigator)

1976-01-01

The author has identified the following significant results. LANDSAT imageries have given positive indication of new land formation in the Bay of Bengal. A map of the bay region showing depth of new formations south of Patherghata test site was prepared. Winter crop estimation of the Sylhet-Mymensingh districts was made. This estimate shows an agreement of about 93% with 1973 data of the Agriculture Department. A preliminary land use map of the Sylhet-Mymensingh area using LANDSAT imageries in conjunction with aerial photographs and ground survey was also prepared.

Assimilation of MODIS and VIIRS AOD to improve aerosols forecasts with FV3-GOCART

NASA Astrophysics Data System (ADS)

Pagowski, M.

2017-12-01

In 2016 NOAA chose the FV3 dynamical core as a basis for its future global modeling system. We present an implementation of aerosol module in the FV3 model and its assimilation framework. The parameterization of aerosols is based on the GOCART scheme. The assimilation methodology relies on hybrid 3D-Var and EnKF methods. Aerosol observations include aerosol optical depth at 550 nm from VIIRS satellite. Results and evaluation of the system against independent observations and NASA's MERRA-2 is shown.

Impact of inherent meteorology uncertainty on air quality ...

EPA Pesticide Factsheets

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb

Meteorological variables associated with deep slab avalanches on persistent weak layers

USGS Publications Warehouse

Marienthal, Alex; Hendrikx, Jordy; Birkeland, Karl; Irvine, Kathryn M.

2014-01-01

Deep slab avalanches are a particularly challenging avalanche forecasting problem. These avalanches are typically difficult to trigger, yet when they are triggered they tend to propagate far and result in large and destructive avalanches. For this work we define deep slab avalanches as those that fail on persistent weak layers deeper than 0.9m (3 feet), and that occur after February 1st. We utilized a 44-year record of avalanche control and meteorological data from Bridger Bowl Ski Area to test the usefulness of meteorological variables for predicting deep slab avalanches. As in previous studies, we used data from the days preceding deep slab cycles, but we also considered meteorological metrics over the early months of the season. We utilized classification trees for our analyses. Our results showed warmer temperatures in the prior twenty-four hours and more loading over the seven days before days with deep slab avalanches on persistent weak layers. In line with previous research, extended periods of above freezing temperatures led to days with deep wet slab avalanches on persistent weak layers. Seasons with either dry or wet avalanches on deep persistent weak layers typically had drier early months, and often had some significant snow depth prior to those dry months. This paper provides insights for ski patrollers, guides, and avalanche forecasters who struggle to forecast deep slab avalanches on persistent weak layers late in the season.

Physics-based and statistical earthquake forecasting in a continental rift zone: the case study of Corinth Gulf (Greece)

NASA Astrophysics Data System (ADS)

Segou, Margarita

2016-01-01

I perform a retrospective forecast experiment in the most rapid extensive continental rift worldwide, the western Corinth Gulf (wCG, Greece), aiming to predict shallow seismicity (depth <15 km) with magnitude M ≥ 3.0 for the time period between 1995 and 2013. I compare two short-term earthquake clustering models, based on epidemic-type aftershock sequence (ETAS) statistics, four physics-based (CRS) models, combining static stress change estimations and the rate-and-state laboratory law and one hybrid model. For the latter models, I incorporate the stress changes imparted from 31 earthquakes with magnitude M ≥ 4.5 at the extended area of wCG. Special attention is given on the 3-D representation of active faults, acting as potential receiver planes for the estimation of static stress changes. I use reference seismicity between 1990 and 1995, corresponding to the learning phase of physics-based models, and I evaluate the forecasts for six months following the 1995 M = 6.4 Aigio earthquake using log-likelihood performance metrics. For the ETAS realizations, I use seismic events with magnitude M ≥ 2.5 within daily update intervals to enhance their predictive power. For assessing the role of background seismicity, I implement a stochastic reconstruction (aka declustering) aiming to answer whether M > 4.5 earthquakes correspond to spontaneous events and identify, if possible, different triggering characteristics between aftershock sequences and swarm-type seismicity periods. I find that: (1) ETAS models outperform CRS models in most time intervals achieving very low rejection ratio RN = 6 per cent, when I test their efficiency to forecast the total number of events inside the study area, (2) the best rejection ratio for CRS models reaches RN = 17 per cent, when I use varying target depths and receiver plane geometry, (3) 75 per cent of the 1995 Aigio aftershocks that occurred within the first month can be explained by static stress changes, (4) highly variable performance on behalf of both statistical and physical models is suggested by large confidence intervals of information gain per earthquake and (5) generic ETAS models can adequately predict the temporal evolution of seismicity during swarms. Furthermore, stochastic reconstruction of seismicity makes possible the identification of different triggering processes between specific seismic crises (2001, 2003-04, 2006-07) and the 1995 aftershock sequence. I find that: (1) seismic events with M ≥ 5.0 are not a part of a preceding earthquake cascade, since they are characterized by high probability being a background event (average Pback > 0.8) and (2) triggered seismicity within swarms is characterized by lower event productivity when compared with the corresponding value during aftershock sequences. I conclude that physics-based models contribute on the determination of the `new-normal' seismicity rate at longer time intervals and that their joint implementation with statistical models is beneficial for future operational forecast systems.

New Techniques for Real-Time Stage Forecasting for Tributaries in the Nashville Area

NASA Astrophysics Data System (ADS)

Charley, W.; Moran, B.; LaRosa, J.

2011-12-01

On Saturday, May 1, 2010, heavy rain began falling in the Cumberland River Valley, Tennessee, and continued through the following day. 13.5 inches was measured at Nashville, an unprecedented amount that doubled the previous 2-day record, and exceeded the May monthly total record of 11 inches. Elsewhere in the valley, amounts of over 19 inches were measured. This intensity of rainfall quickly overwhelmed tributaries to the Cumberland in the Nashville area, causing wide-spread and serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. As a result of the flood, agencies in the Nashville area want better capabilities to forecast stages for the local tributaries. Better stage forecasting will help local agencies close roads, evacuate homes and businesses and similar actions. An interagency group, consisting of Metro Nashville Water Services and Office of Emergency Management, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers, has been established to seek ways to better forecast short-term events in the region. It should be noted that the National Weather Service has the official responsibility of forecasting stages. This paper examines techniques and algorithms that are being developed to meet this need and the practical aspects of integrating them into a usable product that can quickly and accurately forecast stages in the short-time frame of the tributaries. This includes not only the forecasting procedure, but also the procedure to acquire the latest precipitation and stage data to make the forecasts. These procedures are integrated into the program HEC-RTS, the US Army Corps of Engineers Real-Time Simulation program. HEC-RTS is a Java-based integration tool that has been derived from the Corps Water Management System (CWMS). The modeling component takes observed and forecasted rainfall to compute river flow with the program HEC-HMS. The river hydraulics program, HEC-RAS, computes river stages and water surface profiles. An inundation boundary and depth map of water in the flood plain is computed from HEC-RAS Mapper. The user-configurable sequence of modeling software allows engineers to evaluate and compare hydraulic impacts for various "what if?" scenarios. The implementation of these techniques and HEC-RTS is examined for the Mill Creek basin, the 108 square mile tributary basin south east of Nashville. Mill Creek has an average annual flow of 150 CFS and a short response time. It has suffered major damage from the 2010 and other events. The accuracy and effectiveness of the techniques in the integrated tool HEC-RTS is evaluated.

Population differentiation decreases with depth in deep-sea gastropods

NASA Astrophysics Data System (ADS)

Etter, Ron J.; Rex, Michael A.

1990-08-01

The evolutionary processes that have generated the rich and highly endemic deep-sea benthic invertebrate fauna remain largely unstudied. Patterns of geographic variation constitute the most basic and essential information for understanding speciation and adaptive radiation. Here we present a multivariate analysis of phenotypic distance to quantify geographic variation in shell form among populations of eight deep-sea gastropod species distributed along a depth gradient in the western North Atlantic. Our primary aim is to identify regions within the deep sea that may promote population differentiation. The degree of interpopulation divergence is highest at the shelf-slope transition (200 m), and then decreases dramatically with increasing depth across the bathyal region (200-4000 m) to the abyssal plain (>4000 m). Phenotypic change parallels faunal change along the depth gradient, suggesting that the selective regime becomes more uniform with increasing depth. Results indicate that the potential for gastropod radiation within the deep-sea environment (>200 m) varies geographically and is highest in the narrow upper bathyal region.

Seed germination ecology of Echinochloa glabrescens and its implication for management in rice (Oryza sativa L.).

PubMed

Opeña, Jhoana L; Chauhan, Bhagirath S; Baltazar, Aurora M

2014-01-01

Echinochloa glabrescens is a C4 grass weed that is very competitive with rice when left uncontrolled. The competitive ability of weeds is intensified in direct-seeded rice production systems. A better understanding is needed of factors affecting weed seed germination, which can be used as a component of integrated weed management in direct-seeded rice. This study was conducted to determine the effects of temperature, light, salt and osmotic stress, burial depth, crop residue, time and depth of flooding, and herbicide application on the emergence, survival, and growth of two populations [Nueva Ecija (NE) and Los Baños (IR)] of E. glabrescens. Seeds from both populations germinated at all temperatures. The NE population had a higher germination rate (88%) from light stimulation than did the IR population (34%). The salt concentration and osmotic potential required to inhibit 50% of germination were 313 mM and -0.24 MPa, respectively, for the NE population and 254 mM and -0.33 MPa, respectively, for the IR population. Emergence in the NE population was totally inhibited at 4-cm burial depth in the soil, whereas that of the IR population was inhibited at 8 cm. Compared with zero residue, the addition of 5 t ha(-1) of rice residue reduced emergence in the NE and IR populations by 38% and 9%, respectively. Early flooding (within 2 days after sowing) at 2-cm depth reduced shoot growth by 50% compared with non-flooded conditions. Pretilachlor applied at 0.075 kg ai ha(-1) followed by shallow flooding (2-cm depth) reduced seedling emergence by 94-96% compared with the nontreated flooded treatment. Application of postemergence herbicides at 4-leaf stage provided 85-100% control in both populations. Results suggest that integration of different strategies may enable sustainable management of this weed and of weeds with similar germination responses.

Seed Germination Ecology of Echinochloa glabrescens and Its Implication for Management in Rice (Oryza sativa L.)

PubMed Central

Opeña, Jhoana L.; Chauhan, Bhagirath S.; Baltazar, Aurora M.

2014-01-01

Echinochloa glabrescens is a C4 grass weed that is very competitive with rice when left uncontrolled. The competitive ability of weeds is intensified in direct-seeded rice production systems. A better understanding is needed of factors affecting weed seed germination, which can be used as a component of integrated weed management in direct-seeded rice. This study was conducted to determine the effects of temperature, light, salt and osmotic stress, burial depth, crop residue, time and depth of flooding, and herbicide application on the emergence, survival, and growth of two populations [Nueva Ecija (NE) and Los Baños (IR)] of E. glabrescens. Seeds from both populations germinated at all temperatures. The NE population had a higher germination rate (88%) from light stimulation than did the IR population (34%). The salt concentration and osmotic potential required to inhibit 50% of germination were 313 mM and −0.24 MPa, respectively, for the NE population and 254 mM and −0.33 MPa, respectively, for the IR population. Emergence in the NE population was totally inhibited at 4-cm burial depth in the soil, whereas that of the IR population was inhibited at 8 cm. Compared with zero residue, the addition of 5 t ha−1 of rice residue reduced emergence in the NE and IR populations by 38% and 9%, respectively. Early flooding (within 2 days after sowing) at 2-cm depth reduced shoot growth by 50% compared with non-flooded conditions. Pretilachlor applied at 0.075 kg ai ha−1 followed by shallow flooding (2-cm depth) reduced seedling emergence by 94−96% compared with the nontreated flooded treatment. Application of postemergence herbicides at 4-leaf stage provided 85−100% control in both populations. Results suggest that integration of different strategies may enable sustainable management of this weed and of weeds with similar germination responses. PMID:24642568

Fisheries conservation on the high seas: linking conservation physiology and fisheries ecology for the management of large pelagic fishes

PubMed Central

Horodysky, Andrij Z.; Cooke, Steven J.; Graves, John E.; Brill, Richard W.

2016-01-01

Populations of tunas, billfishes and pelagic sharks are fished at or over capacity in many regions of the world. They are captured by directed commercial and recreational fisheries (the latter of which often promote catch and release) or as incidental catch or bycatch in commercial fisheries. Population assessments of pelagic fishes typically incorporate catch-per-unit-effort time-series data from commercial and recreational fisheries; however, there have been notable changes in target species, areas fished and depth-specific gear deployments over the years that may have affected catchability. Some regional fisheries management organizations take into account the effects of time- and area-specific changes in the behaviours of fish and fishers, as well as fishing gear, to standardize catch-per-unit-effort indices and refine population estimates. However, estimates of changes in stock size over time may be very sensitive to underlying assumptions of the effects of oceanographic conditions and prey distribution on the horizontal and vertical movement patterns and distribution of pelagic fishes. Effective management and successful conservation of pelagic fishes requires a mechanistic understanding of their physiological and behavioural responses to environmental variability, potential for interaction with commercial and recreational fishing gear, and the capture process. The interdisciplinary field of conservation physiology can provide insights into pelagic fish demography and ecology (including environmental relationships and interspecific interactions) by uniting the complementary expertise and skills of fish physiologists and fisheries scientists. The iterative testing by one discipline of hypotheses generated by the other can span the fundamental–applied science continuum, leading to the development of robust insights supporting informed management. The resulting species-specific understanding of physiological abilities and tolerances can help to improve stock assessments, develop effective bycatch-reduction strategies, predict rates of post-release mortality, and forecast the population effects of environmental change. In this synthesis, we review several examples of these interdisciplinary collaborations that currently benefit pelagic fisheries management. PMID:27382467

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...

7 CFR 3700.3 - Functions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... and related farm and trade developments and short to long-term forecasts of domestic and world... world agricultural markets. (3) Conducting special analyses of U.S. and world agricultural markets for... trends in the non-metropolitan and farm populations, the number, location and characteristics of such...

Development of a flood early warning system and communication with end-users: the Vipava/Vipacco case study in the KULTURisk FP7 project

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Caronna, Paolo; Ranzi, Roberto

2014-05-01

Within the framework of risk communication, the goal of an early warning system is to support the interaction between technicians and authorities (and subsequently population) as a prevention measure. The methodology proposed in the KULTURisk FP7 project aimed to build a closer collaboration between these actors, in the perspective of promoting pro-active actions to mitigate the effects of flood hazards. The transnational (Slovenia/ Italy) Soča/Isonzo case study focused on this concept of cooperation between stakeholders and hydrological forecasters. The DIMOSHONG_VIP hydrological model was calibrated for the Vipava/Vipacco River (650 km2), a tributary of the Soča/Isonzo River, on the basis of flood events occurred between 1998 and 2012. The European Centre for Medium-Range Weather Forecasts (ECMWF) provided the past meteorological forecasts, both deterministic (1 forecast) and probabilistic (51 ensemble members). The resolution of the ECMWF grid is currently about 15 km (Deterministic-DET) and 30 km (Ensemble Prediction System-EPS). A verification was conducted to validate the flood-forecast outputs of the DIMOSHONG_VIP+ECMWF early warning system. Basic descriptive statistics, like event probability, probability of a forecast occurrence and frequency bias were determined. Some performance measures were calculated, such as hit rate (probability of detection) and false alarm rate (probability of false detection). Relative Opening Characteristic (ROC) curves were generated both for deterministic and probabilistic forecasts. These analysis showed a good performance of the early warning system, in respect of the small size of the sample. A particular attention was spent to the design of flood-forecasting output charts, involving and inquiring stakeholders (Alto Adriatico River Basin Authority), hydrology specialists in the field, and common people. Graph types for both forecasted precipitation and discharge were set. Three different risk thresholds were identified ("attention", "pre-alarm" or "alert", "alarm"), with an "icon-style" representation, suitable for communication to civil protection stakeholders or the public. Aiming at showing probabilistic representations in a "user-friendly" way, we opted for the visualization of the single deterministic forecasted hydrograph together with the 5%, 25%, 50%, 75% and 95% percentiles bands of the Hydrological Ensemble Prediction System (HEPS). HEPS is generally used for 3-5 days hydrological forecasts, while the error due to incorrect initial data is comparable to the error due to the lower resolution with respect to the deterministic forecast. In the short term forecasting (12-48 hours) the HEPS-members show obviously a similar tendency; in this case, considering its higher resolution, the deterministic forecast is expected to be more effective. The plot of different forecasts in the same chart allows the use of model outputs from 4/5 days to few hours before a potential flood event. This framework was built to help a stakeholder, like a mayor, a civil protection authority, etc, in the flood control and management operations, and was designed to be included in a wider decision support system.

Forecasting need and demand for home health care: a selective review

PubMed Central

Sharma, Rabinder K.

1980-01-01

Three models for forecasting home health care (HHC) needs are analyzed: HSA/SP model (Health Systems Agency of Southwestern Pennsylvania); Florida model (Florida State Department of Health and Rehabilitative Services); and Rhode Island model (Rhode Island Department of Community Affairs). A utilization approach to forecasting is also presented. In the HSA/SP and Florida models, need for HHC is based on a certain proportion of (a) hospital admissions and (b) patients entering HHC from other sources. The major advantage of these models is that they are relatively easy to use and explain; their major weaknesses are an imprecise definition of need and an incomplete model specification. The Rhode Island approach defines need for HHC in terms of the health status of the population as measured by chronic activity limitations. The major strengths of this approach are its explicit assumptions and its emphasis on consumer needs. The major drawback is that it requires considerable local area data. The utilization approach is based on extrapolation from observed utilization experience of the target population. Its main limitation is that it is based on current market imperfections; its major advantage is that it exposes existing deficiencies in HHC. The author concludes that each approach should be tested empirically in order to refine it, and that need and demand approaches be used jointly in the planning process. PMID:6893631

Historical view and future demand for knee arthroplasty in Sweden

PubMed Central

Rolfson, Ola; W-Dahl, Annette; Garellick, Göran; Sundberg, Martin; Kärrholm, Johan; Robertsson, Otto

2015-01-01

Background and purpose The incidence of knee osteoarthritis will most likely increase. We analyzed historical trends in the incidence of knee arthroplasty in Sweden between 1975 and 2013, in order to be able to provide projections of future demand. Patients and methods We obtained information on all knee arthroplasties in Sweden in the period 1975–2013 from the Swedish Knee Arthroplasty Register, and used public domain data from Statistics Sweden on the evolution of and forecasts for the Swedish population. We forecast the incidence, presuming the existence of a maximum incidence. Results We found that the incidence of knee arthroplasty will continue to increase until a projected upper incidence level of about 469 total knee replacements per 105 Swedish residents aged 40 years and older is reached around the year 2130. In 2020, the estimated incidence of total knee arthroplasties per 105 Swedish residents aged 40 years and older will be 334 (95% prediction interval (PI): 281–374) and in 2030 it will be 382 (PI: 308–441). Using officially forecast population growth data, around 17,500 operations would be expected to be performed in 2020 and around 21,700 would be expected to be performed in 2030. Interpretation Today’s levels of knee arthroplasty are well below the expected maximum incidence, and we expect a continued annual increase in the total number of knee arthroplasties performed. PMID:25806653

Using multiple data sets to populate probabilistic volcanic event trees

USGS Publications Warehouse

Newhall, C.G.; Pallister, John S.

2014-01-01

The key parameters one needs to forecast outcomes of volcanic unrest are hidden kilometers beneath the Earth’s surface, and volcanic systems are so complex that there will invariably be stochastic elements in the evolution of any unrest. Fortunately, there is sufficient regularity in behaviour that some, perhaps many, eruptions can be forecast with enough certainty for populations to be evacuated and kept safe. Volcanologists charged with forecasting eruptions must try to understand each volcanic system well enough that unrest can be interpreted in terms of pre-eruptive process, but must simultaneously recognize and convey uncertainties in their assessment. We have found that use of event trees helps to focus discussion, integrate data from multiple sources, reach consensus among scientists about both pre-eruptive process and uncertainties and, in some cases, to explain all of this to officials. Figure 1 shows a generic volcanic event tree from Newhall and Hoblitt (2002) that can be modified as needed for each specific volcano. This paper reviews how we and our colleagues have used such trees during a number of volcanic crises worldwide, for rapid hazard assessments in situations in which more formal expert elicitations could not be conducted. We describe how Multiple Data Sets can be used to estimate probabilities at each node and branch. We also present case histories of probability estimation during crises, how the estimates were used by public officials, and some suggestions for future improvements.

Hydraulic geometry of river cross sections; theory of minimum variance

USGS Publications Warehouse

Williams, Garnett P.

1978-01-01

This study deals with the rates at which mean velocity, mean depth, and water-surface width increase with water discharge at a cross section on an alluvial stream. Such relations often follow power laws, the exponents in which are called hydraulic exponents. The Langbein (1964) minimum-variance theory is examined in regard to its validity and its ability to predict observed hydraulic exponents. The variables used with the theory were velocity, depth, width, bed shear stress, friction factor, slope (energy gradient), and stream power. Slope is often constant, in which case only velocity, depth, width, shear and friction factor need be considered. The theory was tested against a wide range of field data from various geographic areas of the United States. The original theory was intended to produce only the average hydraulic exponents for a group of cross sections in a similar type of geologic or hydraulic environment. The theory does predict these average exponents with a reasonable degree of accuracy. An attempt to forecast the exponents at any selected cross section was moderately successful. Empirical equations are more accurate than the minimum variance, Gauckler-Manning, or Chezy methods. Predictions of the exponent of width are most reliable, the exponent of depth fair, and the exponent of mean velocity poor. (Woodard-USGS)

Real-time Assimilation of Altimeter Derived Synthetic Profiles Into a Global version of the Naval Research Laboratory's Coastal Ocean Model (NCOM)

NASA Astrophysics Data System (ADS)

Rhodes, R. C.; Barron, C. N.; Fox, D. N.; Smedstad, L. F.

2001-12-01

A global implementation of the Navy Coastal Ocean Model (NCOM), developed by the Naval Research Laboratory (NRL) at Stennis Space Center is currently running in real-time and is planned for transition to the Naval Oceanographic Office (NAVOCEANO) in 2002. The model encompasses the open ocean to 5 m depth on a curvilinear global model grid with 1/8 degree grid spacing at 45N, extending from 80 S to a complete arctic cap with grid singularities mapped into Canada and Russia. Vertically, the model employs 41 sigma-z levels with sigma in the upper-ocean and coastal regions and z in the deeper ocean. The Navy Operational Global Atmospheric Prediction System (NOGAPS) provides 6-hourly wind stresses and heat fluxes for forcing, while the operational Modular Ocean Data Assimilation System (MODAS) provides the background climatology and tools for data pre-processing. Operationally available sea surface temperature (SST) and altimetry (SSH) data are assimilated into the NAVOCEANO global 1/8 degree MODAS 2-D analysis and the 1/16 degree Navy Layered Ocean Model (NLOM) to provide analyses and forecasts of SSH and SST. The 2-D SSH and SST nowcast fields are used as input to the MODAS synthetic climatology database to yield three-dimensional fields of synthetic temperature and salinity for assimilation into global NCOM. The synthetic profiles are weighted higher at depth in the assimilation process to allow the numerical model to properly develop the mixed-layer structure driven by the real-time atmospheric forcing. Global NCOM nowcasts and forecasts provide a valuable resource for rapid response to the varied and often unpredictable operational requests for 3-dimensional fields of ocean temperature, salinity, and currents. In some cases, the resolution of the global product is sufficient for guidance. In cases requiring higher resolution, the global product offers a quick overview of local circulation and provides initial and boundary conditions for higher resolution coastal models that may be more specialized for a particular task or domain. Nowcast and forecast results are presented globally and in selected areas of interest and model results are compared with historical and concurrent observations and analyses.

A low-altitude mountain range as an important refugium for two narrow endemics in the Southwest Australian Floristic Region biodiversity hotspot.

PubMed

Keppel, Gunnar; Robinson, Todd P; Wardell-Johnson, Grant W; Yates, Colin J; Van Niel, Kimberly P; Byrne, Margaret; Schut, Antonius G T

2017-01-01

Low-altitude mountains constitute important centres of diversity in landscapes with little topographic variation, such as the Southwest Australian Floristic Region (SWAFR). They also provide unique climatic and edaphic conditions that may allow them to function as refugia. We investigate whether the Porongurups (altitude 655 m) in the SWAFR will provide a refugium for the endemic Ornduffia calthifolia and O. marchantii under forecast climate change. We used species distribution modelling based on WorldClim climatic data, 30-m elevation data and a 2-m-resolution LiDAR-derived digital elevation model (DEM) to predict current and future distributions of the Ornduffia species at local and regional scales based on 605 field-based abundance estimates. Future distributions were forecast using RCP2.6 and RCP4.5 projections. To determine whether local edaphic and biotic factors impact these forecasts, we tested whether soil depth and vegetation height were significant predictors of abundance using generalized additive models (GAMs). Species distribution modelling revealed the importance of elevation and topographic variables at the local scale for determining distributions of both species, which also preferred shadier locations and higher slopes. However, O. calthifolia occurred at higher (cooler) elevations with rugged, concave topography, while O. marchantii occurred in disturbed sites at lower locations with less rugged, convex topography. Under future climates both species are likely to severely contract under the milder RCP2.6 projection (approx. 2 °C of global warming), but are unlikely to persist if warming is more severe (RCP4.5). GAMs showed that soil depth and vegetation height are important predictors of O. calthifolia and O. marchantii distributions, respectively. The Porongurups constitute an important refugium for O. calthifolia and O. marchantii, but limits to this capacity may be reached if global warming exceeds 2 °C. This capacity is moderated at local scales by biotic and edaphic factors. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Historic and forecasted population and land-cover change in eastern North Carolina, 1992-2030

USGS Publications Warehouse

Claggett, Peter; Hearn,, Paul P.; Donato, David I.

2015-01-01

The Southeast Regional Partnership for Planning and Sustainability (SERPPAS) was formed in 2005 as a partnership between the Department of Defense (DOD) and State and Federal agencies to promote better collaboration in making resource-use decisions. In support of this goal, the U.S. Geological Survey (USGS) conducted a study to evaluate historic population growth and land-cover change, and to model future change, for the 13-county SERPPAS study area in southeastern North Carolina (fig. 1). Improved understanding of trends in land-cover change and the ability to forecast land-cover change that is consistent with these trends will be a key component of efforts to accommodate local military-mission imperatives while also promoting sustainable economic growth throughout the 13-county study area. The study had three principal objectives:    1.  Evaluate historic changes in population and land cover for the period 1992–2006 using both previously existing as well as newly generated land-cover data.    2.  Develop models to forecast future change in land cover using the data gathered in objective 1 in conjunction with ancillary data on the suitability of the various sub-areas within the study area for low- and high-intensity urban development.    3.  Deliver these results—including an executive-level briefing and a USGS technical report—to DOD, other project cooperators, and local counties in hard-copy and digital formats and via the Web through a map-based data viewer. This report provides a general overview of the study and is intended for general distribution to non-technical audiences.

How wild is your model fire? Constraining WRF-Chem wildfire smoke simulations with satellite observations

NASA Astrophysics Data System (ADS)

Fischer, E. V.; Ford, B.; Lassman, W.; Pierce, J. R.; Pfister, G.; Volckens, J.; Magzamen, S.; Gan, R.

2015-12-01

Exposure to high concentrations of particulate matter (PM) present during acute pollution events is associated with adverse health effects. While many anthropogenic pollution sources are regulated in the United States, emissions from wildfires are difficult to characterize and control. With wildfire frequency and intensity in the western U.S. projected to increase, it is important to more precisely determine the effect that wildfire emissions have on human health, and whether improved forecasts of these air pollution events can mitigate the health risks associated with wildfires. One of the challenges associated with determining health risks associated with wildfire emissions is that the low spatial resolution of surface monitors means that surface measurements may not be representative of a population's exposure, due to steep concentration gradients. To obtain better estimates of ambient exposure levels for health studies, a chemical transport model (CTM) can be used to simulate the evolution of a wildfire plume as it travels over populated regions downwind. Improving the performance of a CTM would allow the development of a new forecasting framework that could better help decision makers estimate and potentially mitigate future health impacts. We use the Weather Research and Forecasting model with online chemistry (WRF-Chem) to simulate wildfire plume evolution. By varying the model resolution, meteorology reanalysis initial conditions, and biomass burning inventories, we are able to explore the sensitivity of model simulations to these various parameters. Satellite observations are used first to evaluate model skill, and then to constrain the model results. These data are then used to estimate population-level exposure, with the aim of better characterizing the effects that wildfire emissions have on human health.

Bathymetric and geographic population structure in the pan-Atlantic deep-sea bivalve Deminucula atacellana (Schenck, 1939).

PubMed

Zardus, John D; Etter, Ron J; Chase, Michael R; Rex, Michael A; Boyle, Elizabeth E

2006-03-01

The deep-sea soft-sediment environment hosts a diverse and highly endemic fauna of uncertain origin. We know little about how this fauna evolved because geographic patterns of genetic variation, the essential information for inferring patterns of population differentiation and speciation are poorly understood. Using formalin-fixed specimens from archival collections, we quantify patterns of genetic variation in the protobranch bivalve Deminucula atacellana, a species widespread throughout the Atlantic Ocean at bathyal and abyssal depths. Samples were taken from 18 localities in the North American, West European and Argentine basins. A hypervariable region of mitochondrial 16S rDNA was amplified by polymerase chain reaction (PCR) and sequenced from 130 individuals revealing 21 haplotypes. Except for several important exceptions, haplotypes are unique to each basin. Overall gene diversity is high (h = 0.73) with pronounced population structure (Phi(ST) = 0.877) and highly significant geographic associations (P < 0.0001). Sequences cluster into four major clades corresponding to differences in geography and depth. Genetic divergence was much greater among populations at different depths within the same basin, than among those at similar depths but separated by thousands of kilometres. Isolation by distance probably explains much of the interbasin variation. Depth-related divergence may reflect historical patterns of colonization or strong environmental selective gradients. Broadly distributed deep-sea organisms can possess highly genetically divergent populations, despite the lack of any morphological divergence.

Modeling fast and slow earthquakes at various scales

PubMed Central

IDE, Satoshi

2014-01-01

Earthquake sources represent dynamic rupture within rocky materials at depth and often can be modeled as propagating shear slip controlled by friction laws. These laws provide boundary conditions on fault planes embedded in elastic media. Recent developments in observation networks, laboratory experiments, and methods of data analysis have expanded our knowledge of the physics of earthquakes. Newly discovered slow earthquakes are qualitatively different phenomena from ordinary fast earthquakes and provide independent information on slow deformation at depth. Many numerical simulations have been carried out to model both fast and slow earthquakes, but problems remain, especially with scaling laws. Some mechanisms are required to explain the power-law nature of earthquake rupture and the lack of characteristic length. Conceptual models that include a hierarchical structure over a wide range of scales would be helpful for characterizing diverse behavior in different seismic regions and for improving probabilistic forecasts of earthquakes. PMID:25311138

Modeling fast and slow earthquakes at various scales.

PubMed

Ide, Satoshi

2014-01-01

Earthquake sources represent dynamic rupture within rocky materials at depth and often can be modeled as propagating shear slip controlled by friction laws. These laws provide boundary conditions on fault planes embedded in elastic media. Recent developments in observation networks, laboratory experiments, and methods of data analysis have expanded our knowledge of the physics of earthquakes. Newly discovered slow earthquakes are qualitatively different phenomena from ordinary fast earthquakes and provide independent information on slow deformation at depth. Many numerical simulations have been carried out to model both fast and slow earthquakes, but problems remain, especially with scaling laws. Some mechanisms are required to explain the power-law nature of earthquake rupture and the lack of characteristic length. Conceptual models that include a hierarchical structure over a wide range of scales would be helpful for characterizing diverse behavior in different seismic regions and for improving probabilistic forecasts of earthquakes.

High resolution characterization of northwest Mediterranean coastal waters thermal regimes: To better understand responses of benthic communities to climate change

NASA Astrophysics Data System (ADS)

Bensoussan, Nathaniel; Romano, Jean-Claude; Harmelin, Jean-Georges; Garrabou, Joaquim

2010-04-01

In the North West Mediterranean (NWM), mass mortality events (MME) of long-lived benthic species that have occurred over the last two decades have been related to regional warming trend. Gaining robust data sets on thermal regimes is critical to assess conditions to which species have adapted, detect extreme events and critically evaluate biological impacts. High resolution temperature ( T) time series obtained during 1999-2006 from 5 to 40 m depth at four contrasted sites of the NWM were analyzed: Area Marina Protegida de les Illes Medes (NE Spain), Riou (Marseilles, France), Parc National de Port-Cros (France), and Réserve Naturelle de Scandola (Corsica, France). The seasonal pattern showed winter T around 11-13 °C, and summer T mainly around 22-24 °C near surface to 18-20 °C at depth. Stratification dynamics showed recurrent downwellings (>40 m) at Medes, frequent observation (1/3rd of the summer) of deep and cold upwelled waters at Riou, while Scandola exhibited stable summer stratification and highest suprathermoclinal T. Port-Cros showed an intermediate regime that oscillated between Riou and Scandola depending on the occurrence of northern winds. Data distribution study permitted to identify and to characterize 3 large scale positive anomalies concomitant with the mass mortality outbreaks of summers 1999, 2003 and 2006. The analysis of biological surveys on gorgonian populations showed significant impacts during the 3 years with temperature anomalies. Besides the degree of impact showed inter-annual differences which could be related to different T conditions concomitant to mortality events, from slight increase in T extreme of only 1-2 °C over short duration, to lengthened more classical summer conditions. Our results therefore support the hypothesis that shallow NWM populations of long-lived benthic species are living near their upper thermal thresholds. Given actual trends and projections in NWM, the repetition of new MMEs in the next decades is extremely likely. In such context, the acquisition of dedicated high resolution T series proves to be crucial for increasing our detection, understanding and forecasting abilities.

Population projections for AIDS using an actuarial model.

PubMed

Wilkie, A D

1989-09-05

This paper gives details of a model for forecasting AIDS, developed for actuarial purposes, but used also for population projections. The model is only appropriate for homosexual transmission, but it is age-specific, and it allows variation in the transition intensities by age, duration in certain states and calendar year. The differential equations controlling transitions between states are defined, the method of numerical solution is outlined, and the parameters used in five different Bases of projection are given in detail. Numerical results for the population of England and Wales are shown.

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

DOE PAGES

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

2015-07-14

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

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services

PubMed Central

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier

2016-01-01

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat–health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe. PMID:26861369

Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services.

PubMed

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R; Rodó, Xavier

2016-02-06

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

PubMed Central

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-01-01

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders. PMID:26934496

A comparison of ensemble post-processing approaches that preserve correlation structures

NASA Astrophysics Data System (ADS)

Schefzik, Roman; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

Despite the fact that ensemble forecasts address the major sources of uncertainty, they exhibit biases and dispersion errors and therefore are known to improve by calibration or statistical post-processing. For instance the ensemble model output statistics (EMOS) method, also known as non-homogeneous regression approach (Gneiting et al., 2005) is known to strongly improve forecast skill. EMOS is based on fitting and adjusting a parametric probability density function (PDF). However, EMOS and other common post-processing approaches apply to a single weather quantity at a single location for a single look-ahead time. They are therefore unable of taking into account spatial, inter-variable and temporal dependence structures. Recently many research efforts have been invested in designing post-processing methods that resolve this drawback but also in verification methods that enable the detection of dependence structures. New verification methods are applied on two classes of post-processing methods, both generating physically coherent ensembles. A first class uses the ensemble copula coupling (ECC) that starts from EMOS but adjusts the rank structure (Schefzik et al., 2013). The second class is a member-by-member post-processing (MBM) approach that maps each raw ensemble member to a corrected one (Van Schaeybroeck and Vannitsem, 2015). We compare variants of the EMOS-ECC and MBM classes and highlight a specific theoretical connection between them. All post-processing variants are applied in the context of the ensemble system of the European Centre of Weather Forecasts (ECMWF) and compared using multivariate verification tools including the energy score, the variogram score (Scheuerer and Hamill, 2015) and the band depth rank histogram (Thorarinsdottir et al., 2015). Gneiting, Raftery, Westveld, and Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., {133}, 1098-1118. Scheuerer and Hamill, 2015. Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities. Mon. Wea. Rev. {143},1321-1334. Schefzik, Thorarinsdottir, Gneiting. Uncertainty quantification in complex simulation models using ensemble copula coupling. Statistical Science {28},616-640, 2013. Thorarinsdottir, M. Scheuerer, and C. Heinz, 2015. Assessing the calibration of high-dimensional ensemble forecasts using rank histograms, arXiv:1310.0236. Van Schaeybroeck and Vannitsem, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.

Corps Water Management System (CWMS) Decision Support Modeling and Integration Use in the June 2007 Texas Floods

NASA Astrophysics Data System (ADS)

Charley, W. J.; Luna, M.

2007-12-01

The U.S. Army Corps of Engineers Corps Water Management System (CWMS) is a comprehensive data acquisition and hydrologic modeling system for short-term decision support of water control operations in real time. It encompasses data collection, validation and transformation, data storage, visualization, real time model simulation for decision-making support, and data dissemination. CWMS uses an Oracle database and Sun Solaris workstations for data processes, storage and the execution of models, with a client application (the Control and Visualization Interface, or CAVI) that can run on a Windows PC. CWMS was used by the Lower Colorado River Authority (LCRA) to make hydrologic forecasts of flows on the Lower Colorado River and operate reservoirs during the June 2007 event in Texas. The LCRA receives real-time observed gridded spatial rainfall data from OneRain, Inc. that which is a result of adjusting NexRad rainfall data with precipitation gages. This data is used, along with future precipitation estimates, for hydrologic forecasting by the rainfall-runoff modeling program HEC-HMS. Forecasted flows from HEC-HMS and combined with observed flows and reservoir information to simulate LCRA's reservoir operations and help engineers make release decisions based on the results. The river hydraulics program, HEC-RAS, computes river stages and water surface profiles for the computed flow. An inundation boundary and depth map of water in the flood plain can be calculated from the HEC-RAS results using ArcInfo. By varying future precipitation and releases, engineers can evaluate different "What if?" scenarios. What was described as an "extraordinary cluster of thunderstorms" that stalled over Burnet and Llano counties in Texas on June 27, 2007, dropped 17 to 19 inches of rainfall over a 6-hour period. The storm was classified over a 500-year event and the resulting flow over some of the smaller tributaries as a 100-year or better. CWMS was used by LCRA for flood forecasting and reservoir operations. The models accurately forecasting the flows and allowed engineers to determine that only four floodgates needed to be opened for Mansfield dam, in the Chain of Highland lakes. CWMS also forecasted the peak of the flood well before it happened. Smaller rain storms continued for a period of weeks and CWMS was used throughout the event calculating lake levels, closing of gates along with a hydro-generation schedule.

Evaluating simplified methods for liquefaction assessment for loss estimation

NASA Astrophysics Data System (ADS)

Kongar, Indranil; Rossetto, Tiziana; Giovinazzi, Sonia

2017-06-01

Currently, some catastrophe models used by the insurance industry account for liquefaction by applying a simple factor to shaking-induced losses. The factor is based only on local liquefaction susceptibility and this highlights the need for a more sophisticated approach to incorporating the effects of liquefaction in loss models. This study compares 11 unique models, each based on one of three principal simplified liquefaction assessment methods: liquefaction potential index (LPI) calculated from shear-wave velocity, the HAZUS software method and a method created specifically to make use of USGS remote sensing data. Data from the September 2010 Darfield and February 2011 Christchurch earthquakes in New Zealand are used to compare observed liquefaction occurrences to forecasts from these models using binary classification performance measures. The analysis shows that the best-performing model is the LPI calculated using known shear-wave velocity profiles, which correctly forecasts 78 % of sites where liquefaction occurred and 80 % of sites where liquefaction did not occur, when the threshold is set at 7. However, these data may not always be available to insurers. The next best model is also based on LPI but uses shear-wave velocity profiles simulated from the combination of USGS VS30 data and empirical functions that relate VS30 to average shear-wave velocities at shallower depths. This model correctly forecasts 58 % of sites where liquefaction occurred and 84 % of sites where liquefaction did not occur, when the threshold is set at 4. These scores increase to 78 and 86 %, respectively, when forecasts are based on liquefaction probabilities that are empirically related to the same values of LPI. This model is potentially more useful for insurance since the input data are publicly available. HAZUS models, which are commonly used in studies where no local model is available, perform poorly and incorrectly forecast 87 % of sites where liquefaction occurred, even at optimal thresholds. This paper also considers two models (HAZUS and EPOLLS) for estimation of the scale of liquefaction in terms of permanent ground deformation but finds that both models perform poorly, with correlations between observations and forecasts lower than 0.4 in all cases. Therefore these models potentially provide negligible additional value to loss estimation analysis outside of the regions for which they have been developed.

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.

Recruiting Implications of the Long War for the Marine Corps

DTIC Science & Technology

2008-01-01

forecast future demographic complexion. Thus today’s marketing and advertising efforts can be tailored to shape tomorrow’s desired force diversity... marketing and advertising campaign. Continue to place Hispanic recruiters in urban centers with dense Hispanic population. Lastly, the Marine Corps

Forecasting Bromus tectorum and fire threat: site soil type versus population traits

USDA-ARS?s Scientific Manuscript database

Cheatgrass (Bromus tectorum), is an exotic invasive annual grass that increases the chance, rate, spread and season of wildfires. Cheatgrass truncates secondary succession by out-competing native perennial seedlings for limited moisture and resources. Habitats that historically burned every 60-110...

Feasibility of Forecasting Highway Safety in Support of Safety Incentive and Safety Target Programs.

DOT National Transportation Integrated Search

2007-11-01

Using the frequency of fatal crashes from the current observation period (e.g. month, year, etc.) as the : prediction of expected future performance does not account for changes in safety that result from : increases in exposure (population, addition...

Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Science implementation of Forecast Mekong for food and environmental security

USGS Publications Warehouse

Turnipseed, D. Phil

2012-01-01

Forecast Mekong is a significant international thrust under the Delta Research and Global Observation Network (DRAGON) of the U.S. Geological Survey (USGS) and was launched in 2009 by the U.S. Department of State and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam under U.S. Department of State Secretary Hillary R. Clinton's Lower Mekong Initiative to enhance U.S. engagement with countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. Since 2009, the USGS has worked closely with the U.S. Department of State; personnel from Cambodia, Laos, Thailand, and Vietnam; nongovernmental organizations; and academia to collect and use research and data from the Lower Mekong River Basin to provide hands-on results that will help decisionmakers in future planning and design for restoration, conservation, and management efforts in the Lower Mekong River Basin. In 2012 Forecast Mekong is highlighting the increasing cooperation between the United States and Lower Mekong River Basin countries in the areas of food and environmental security. Under the DRAGON, Forecast Mekong continues work in interactive data integration, modeling, and visualization system by initiating three-dimensional bathymetry and river flow data along with a pilot study of fish distribution, population, and migratory patterns in the Lower Mekong River Basin. When fully developed by the USGS, in partnership with local governments and universities throughout the Mekong River region, Forecast Mekong will provide valuable planning tools to visualize the consequences of climate change and river management.

Development of an Impact-Oriented Quantitative Coastal Inundation forecasting and early warning system with social and economic assessment

NASA Astrophysics Data System (ADS)

Fakhruddin, S. H. M.; Babel, Mukand S.; Kawasaki, Akiyuki

2014-05-01

Coastal inundations are an increasing threat to the lives and livelihoods of people living in low-lying, highly-populated coastal areas. According to a World Bank Report in 2005, at least 2.6 million people may have drowned due to coastal inundation, particularly caused by storm surges, over the last 200 years. Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. Recognizing this extreme vulnerability of coastal areas to inundation/flooding, and with a view to improve safety-related services for the community, research should strongly enhance today's forecasting, prediction and early warning capabilities in order to improve the assessment of coastal vulnerability and risks and develop adequate prevention, mitigation and preparedness measures. This paper tries to develop an impact-oriented quantitative coastal inundation forecasting and early warning system with social and economic assessment to address the challenges faced by coastal communities to enhance their safety and to support sustainable development, through the improvement of coastal inundation forecasting and warning systems.

Limited Genetic Connectivity between Gorgonian Morphotypes along a Depth Gradient

PubMed Central

Gori, Andrea; Lopez-González, Pablo; Bramanti, Lorenzo; Rossi, Sergio; Gili, Josep-Maria; Abbiati, Marco

2016-01-01

Gorgonian species show a high morphological variability in relation to the environment in which they live. In coastal areas, parameters such as temperature, light, currents, and food availability vary significantly with depth, potentially affecting morphology of the colonies and the structure of the populations, as well as their connectivity patterns. In tropical seas, the existence of connectivity between shallow and deep populations supported the hypothesis that the deep coral reefs could potentially act as (reproductive) refugia fostering re-colonization of shallow areas after mortality events. Moreover, this hypothesis is not so clear accepted in temperate seas. Eunicella singularis is one of the most common gorgonian species in Northwestern Mediterranean Sea, playing an important role as ecosystem engineer by providing biomass and complexity to the coralligenous habitats. It has a wide bathymetric distribution ranging from about 10 m to 100 m. Two depth-related morphotypes have been identified, differing in colony morphology, sclerite size and shape, and occurrence of symbiotic algae, but not in mitochondrial DNA haplotypes. In the present study the genetic structure of E. singularis populations along a horizontal and bathymetric gradient was assessed using microsatellites and ITS1 sequences. Restricted gene flow was found at 30–40 m depth between the two Eunicella morphotypes. Conversely, no genetic structuring has been found among shallow water populations within a spatial scale of ten kilometers. The break in gene flow between shallow and deep populations contributes to explain the morphological variability observed at different depths. Moreover, the limited vertical connectivity hinted that the refugia hypothesis does not apply to E. singularis. Re-colonization of shallow water populations, occasionally affected by mass mortality events, should then be mainly fueled by larvae from other shallow water populations. PMID:27490900

Population Size and Distribution of Rhizobium leguminosarum bv. trifolii in Relation to Total Soil Bacteria and Soil Depth †

PubMed Central

Bottomley, Peter J.; Dughri, Muktar H.

1989-01-01

Bacterial cells small enough to pass through 0.4-μm-pore-size filters made up 5 to 9% of the indigenous bacterial population in 0- to 20-cm-depth samples of Abiqua silty clay loam. Within the same soil samples, cells of a similar dimension were stained with fluorescent antibodies specific to each of four antigenically distinct indigenous serogroups of Rhizobium leguminosarum bv. trifolii and made up 22 to 34% of the soil population of the four serogroups. Despite the extensive contribution of small cells to these soil populations, no evidence of their being capable of either growth or nodulation was obtained. The density of soil bacteria which could be cultured ranged between 0.5 and 8.5% of the >0.4-μm direct count regardless of media, season of sampling, or soil depth. In the same soil samples, the viable nodulating populations of biovar trifolii determined by the plant infection soil dilution technique ranged between 1 and 10% of the >0.4-μm direct-immunofluorescence count of biovar trifolii. The <0.4-μm cell populations of both total soil bacteria and biovar trifolii changed abruptly between the 10- to 15-cm and 15- to 20-cm soil depth increments, increasing from 5 to 20% and from 20 to 50%, respectively, of their direct-count totals. The increase in density of the small-cell population corresponded to a significant increase in soil bulk density (1.07 to 1.21 g cm−3). The percent contribution of the <0.4-μm direct count to individual serogroup totals increased with soil depth by approximately 2-fold (39 to 87%) for serogroups 17 and 21 and by 12-fold (6 to 75%) for serogroups 6 and 36. PMID:16347896

3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Huang, Jingyi; McBratney, Alex B.; Minasny, Budiman; Triantafilis, John

2017-06-01

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although inversion of apparent electrical conductivity (ECa) measured by electromagnetic induction to calculate depth-specific electrical conductivity (σ) has been used, it is difficult to apply it across a field. In this paper we use a calibration established along a transect, across a 3.94-ha field with varying soil texture, using an ensemble Kalman filter (EnKF) to monitor and nowcast the 3-dimensional θ dynamics on 16 separate days over a period of 38 days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising σ generated by quasi-3d inversions of DUALEM-421S ECa data. Results showed that the distribution of θ was controlled by soil texture, topography, and vegetation. Soil water dried fastest at the beginning after the initial irrigation event and decreased with time and soil depth, which was consistent with classical soil drying theory and experiments. It was also found that the soil dried fastest in the loamy and duplex soils present in the field, which was attributable to deep drainage and preferential flow. It was concluded that the EnKF approach can be used to improve the irrigation efficiency by applying variable irrigation rates across the field. In addition, soil water status can be nowcasted across large spatial extents using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.

Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Walko, R. L.

2006-03-01

This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and mesoscale forcing, the high resolution forecast (Hi-Res) has better performance compared to the ensemble mean for rainfall thresholds larger than 10mm but it tends to overestimate precipitation for lower amounts. This yields larger false alarms that have a detrimental effect on objective scores for lower thresholds. To exploit the advantages of a probabilistic forecast compared to a deterministic one, the relation between the ECMWF-EPS 700 hPa geopotential height spread and LEPS performance is analyzed. Results are promising even if additional studies are required.

Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

2017-12-01

The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to Quebrada de Ramón's time of concentration) and the temperatures during the event. This is a fundamental input to a hydrological model that could forecast flash floods. Finally, WSM-6Class microphysics was chosen as the one with best performance, but a geostatistical approach to countervail WRF forecasts' shortcomings over Andean piedmont is required.

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

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

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

2014-09-12

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

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Testing the depth-differentiation hypothesis in a deepwater octocoral

USGS Publications Warehouse

Quattrini, Andrea; Baums, Iliana B.; Shank, Timothy M.; Morrison, Cheryl L.; Cordes, Erik E.

2015-01-01

The depth-differentiation hypothesis proposes that the bathyal region is a source of genetic diversity and an area where there is a high rate of species formation. Genetic differentiation should thus occur over relatively small vertical distances, particularly along the upper continental slope (200–1000 m) where oceanography varies greatly over small differences in depth. To test whether genetic differentiation within deepwater octocorals is greater over vertical rather than geographical distances, Callogorgia delta was targeted. This species commonly occurs throughout the northern Gulf of Mexico at depths ranging from 400 to 900 m. We found significant genetic differentiation (FST = 0.042) across seven sites spanning 400 km of distance and 400 m of depth. A pattern of isolation by depth emerged, but geographical distance between sites may further limit gene flow. Water mass boundaries may serve to isolate populations across depth; however, adaptive divergence with depth is also a possible scenario. Microsatellite markers also revealed significant genetic differentiation (FST = 0.434) between C. delta and a closely related species, Callogorgia americana, demonstrating the utility of microsatellites in species delimitation of octocorals. Results provided support for the depth-differentiation hypothesis, strengthening the notion that factors covarying with depth serve as isolation mechanisms in deep-sea populations.

Application of Catastrophe Risk Modelling to Evacuation Public Policy

NASA Astrophysics Data System (ADS)

Woo, G.

2009-04-01

The decision by civic authorities to evacuate an area threatened by a natural hazard is especially fraught when the population in harm's way is extremely large, and where there is considerable uncertainty in the spatial footprint, scale, and strike time of a hazard event. Traditionally viewed as a hazard forecasting issue, civil authorities turn to scientists for advice on a potentially imminent dangerous event. However, the level of scientific confidence varies enormously from one peril and crisis situation to another. With superior observational data, meteorological and hydrological hazards are generally better forecast than geological hazards. But even with Atlantic hurricanes, the track and intensity of a hurricane can change significantly within a few hours. This complicated and delayed the decision to call an evacuation of New Orleans when threatened by Hurricane Katrina, and would present a severe dilemma if a major hurricane were appearing to head for New York. Evacuation needs to be perceived as a risk issue, requiring the expertise of catastrophe risk modellers as well as geoscientists. Faced with evidence of a great earthquake in the Indian Ocean in December 2004, seismologists were reluctant to give a tsunami warning without more direct sea observations. Yet, from a risk perspective, the risk to coastal populations would have warranted attempts at tsunami warning, even though there was significant uncertainty in the hazard forecast, and chance of a false alarm. A systematic coherent risk-based framework for evacuation decision-making exists, which weighs the advantages of an evacuation call against the disadvantages. Implicitly and qualitatively, such a cost-benefit analysis is undertaken by civic authorities whenever an evacuation is considered. With the progress in catastrophe risk modelling, such an analysis can be made explicit and quantitative, providing a transparent audit trail for the decision process. A stochastic event set, the core of a catastrophe risk model, is required to explore the casualty implications of different possible hazard scenarios, to assess the proportion of an evacuated population who would owe their lives to an evacuation, and to estimate the economic loss associated with an unnecessary evacuation. This paper will review the developing methodology for applying catastrophe risk modelling to support public policy in evacuation decision-making, and provide illustrations from across the range of natural hazards. Evacuation during volcanic crises is a prime example, recognizing the improving forecasting skill of volcanologists, now able to account probabilistically for precursory seismological, geodetic, and geochemical monitoring data. This methodology will be shown to help civic authorities make sounder risk-informed decisions on the timing and population segmentation of evacuation from both volcanoes and calderas, such as Vesuvius and Campi Flegrei, which are in densely populated urban regions.

Landform degradation on Mercury, the moon, and Mars - Evidence from crater depth/diameter relationships

NASA Technical Reports Server (NTRS)

Malin, M. C.; Dzurisin, D.

1977-01-01

Craters on Mercury, the moon, and Mars were classified into two groups, namely, fresh and degraded craters, on the basis of qualitative visual degradation as revealed by degree of rim crispness, terraced interior walls, slumping from crater walls, etc., and the depth/diameter relationship of craters was studied. Lunar and Mercurian crater populations indicate the existence of terrain-correlated degradational phenomena. The depth/diameter relations for Mercury and the moon display remarkably similar forms, suggesting similar degrees of landform degradation. Depth/diameter curves display a break in slope, dividing two distinct crater populations. Mars craters show few of the trends of those of Mercury and the moon. The depth/diameter curve has no definite break in slope, though there is considerable depth variation. The role of nonballistic degradation in connection with the early formation of large expanses of intercrater plains is discussed.

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.

Role of the Internet in Anticipating and Mitigating Earthquake Catastrophes, and the Emergence of Personal Risk Management (Invited)

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Donnellan, A.; Graves, W.; Tiampo, K. F.; Klein, W.

2009-12-01

Risks from natural and financial catastrophes are currently managed by a combination of large public and private institutions. Public institutions usually are comprised of government agencies that conduct studies, formulate policies and guidelines, enforce regulations, and make “official” forecasts. Private institutions include insurance and reinsurance companies, and financial service companies that underwrite catastrophe (“cat”) bonds, and make private forecasts. Although decisions about allocating resources and developing solutions are made by large institutions, the costs of dealing with catastrophes generally fall for the most part on businesses and the general public. Information on potential risks is generally available to the public for some hazards but not others. For example, in the case of weather, private forecast services are provided by www.weather.com and www.wunderground.com. For earthquakes in California (only), the official forecast is the WGCEP-USGS forecast, but provided in a format that is difficult for the public to use. Other privately made forecasts are currently available, for example by the JPL QuakeSim and Russian groups, but these efforts are limited. As more of the world’s population moves increasingly into major seismic zones, new strategies are needed to allow individuals to manage their personal risk from large and damaging earthquakes. Examples include individual mitigation measures such as retrofitting, as well as microinsurance in both developing and developed countries, as well as other financial strategies. We argue that the “long tail” of the internet offers an ideal, and greatly underutilized mechanism to reach out to consumers and to provide them with the information and tools they need to confront and manage seismic hazard and risk on an individual, personalized basis. Information of this type includes not only global hazard forecasts, which are now possible, but also global risk estimation. Additionally, social networking tools are available that will allow self-organizing, disaster-resilient communities to arise as emergent structures from the underlying nonlinear social dynamics. In this talk, we argue that the current style of risk management is not making adequate use of modern internet technology, and that significantly more can be done. We suggest several avenues to proceed, in particular making use of the internet for earthquake forecast and information delivery, as well as tracking forecast validation and verification on a real-time basis. We also show examples of forecasts delivered over the internet, and describe how these are made.