Molecular cloning and multifunctional characterization of GRIM-19 (gene associated with retinoid-interferon-induced mortality 19) homologue from turbot (Scophthalmus maximus).

PubMed

Wang, Na; Wang, Xianli; Yang, Changgeng; Zhao, Xiaojie; Zhang, Yuxi; Wang, Tianzi; Chen, Songlin

2014-03-01

GRIM-19 (gene associated with retinoid-interferon-induced mortality 19), a novel cell death regulatory gene, plays important roles in cell apoptosis, embryogenesis, mitochondrial respiratory chain and immune response. To date, little information is known about fish GRIM-19 characteristics except orange-spotted grouper (Epinephelus coioides). Here a new GRIM-19 gene is identified and characterized from turbot (Scophthalmus maximus), an economic marine fish in China and Europe. Briefly, turbot GRIM-19 is a 595-bp gene encoding a 144 amino acids protein, which shares the closest relationship with Atlantic halibut (Hippoglossus hippoglossus). The expression of turbot grim-19 in liver, spleen and kidney is up-regulated by the infection of Vibrio anguillarum and LCDV (lymphocystis disease virus). Subsequently, a recombinant protein of turbot GRIM-19 is acquired and the anti-bacterial function is proved by liquid culture inhibition experiment. The subcellular location indicates that turbot GRIM-19 is co-localized with STAT3 in the cytoplasm, which is mainly determined by GRIM-19 41-84 amino acids and STAT3 1-321 amino acids. Finally, the involvements of turbot GRIM-19 in cell apoptosis and NF-κB pathway are investigated. All these data help to understand GRIM-19 function in fish, as well as provide the application possibility of GRIM-19 in fish disease resistance breeding. Copyright © 2013 Elsevier Ltd. All rights reserved.

The potential of telehealth for 'business as usual' in outpatient clinics.

PubMed

Day, Karen; Kerr, Patricia

2012-04-01

A six-month pilot study was conducted to ascertain the value of using high-definition videoconferencing equipment in an outpatients' setting. The videoconferencing equipment, which included digital biometric equipment, was installed in the outpatient clinics of a remote health service in New Zealand. Use of the equipment was evaluated using action research techniques. Clinicians were interviewed about their assessment of the equipment's usefulness. Patients and their carers completed questionnaires about their clinic experience. During the pilot trial, 109 patients were seen in 25 clinics of six different specialities. Questionnaire results showed that patients and their companions had a good user experience, similar to a face-to-face appointment. Clinicians found that the large screen, sense of proximity, video clarity and definition, and lack of sound/picture lag worked well for certain types of outpatients' clinics, e.g. methadone maintenance clinics. The need for process changes made it difficult to turn telehealth into business as usual in an environment built for face-to-face appointments. We conclude that videoconference equipment has potential to become integral to outpatients' clinics.

Expression of GRIM-19 in adenomyosis and its possible role in pathogenesis.

PubMed

Wang, Jing; Deng, Xiaohui; Yang, Yang; Yang, Xingsheng; Kong, Beihua; Chao, Lan

2016-04-01

To study the expression of the gene associated with retinoid-interferon (IFN)-induced mortality 19 (GRIM-19) in the endometrial tissue of patients with adenomyosis and to describe the possible pathogenic mechanisms of this phenomenon. Experimental study using human samples and cell lines. University-affiliated hospital. Ectopic and eutopic endometrial tissues were obtained from 30 patients with adenomyosis, whereas normal endometrial specimens were obtained from 10 control patients without adenomyosis. Patients with rapid pathology report-confirmed adenomyosis were recruited, and eutopic and ectopic endometrial tissue samples were collected from patients who had undergone hysterectomies by either the transabdominal or laparoscopic method at Qilu Hospital. Normal endometrial tissue was collected from a group of control patients without adenomyosis. Immunohistochemistry (IHC) was performed to evaluate the expression of GRIM-19, phospho-signal transducer and activator of transcription 3 (Y705) (Y705) (pSTAT3(Y705)), and vascular endothelial growth factor (VEGF) in endometrial tissue samples. The protein levels of GRIM-19, pSTAT3(Y705), STAT3, and VEGF were detected by Western blot. Apoptosis in endometrial specimens was assayed by TUNEL. Immunohistochemistry with an antibody directed against CD34 was performed to detect new blood vessels in the endometrial tissue. GRIM-19 small interfering RNA and a recombinant plasmid carrying GRIM-19 were constructed to evaluate the effects of GRIM-19 on the downstream factors pSTAT3(Y705), STAT3, and VEGF in Ishikawa cells. The expression of GRIM-19 was down-regulated in the eutopic endometria of patients with adenomyosis compared with the endometria of patients in the control group, and it was further reduced in the endometrial glandular epithelial cells of adenomyotic lesions. Apoptosis was reduced in the eutopic endometrium compared with the control group, and it was significantly reduced in ectopic endometrial tissues. In

NDI Acquisition. An Alternative to Business as Usual. Report of the DSMC 1991-1992 Military Research Fellows

DTIC Science & Technology

1992-10-01

sealed bidding and competitive proposals. governed by the same regulations and laws The sealed bidding procedure requires ade- that govern procurement ...Summary xiv NDI ACQUISITION: An Alternative to "Business as Usual" to successful, effective government procure - posal Cover Sheet). Moreover, the...became policy when the OPlP ;,;sued the first opment costs. These benefits may be offset by in a series of memoranda governing procure - performance

Forecasting: Exercises to Enhance Learning from Business Simulations

ERIC Educational Resources Information Center

Clark, Timothy S.; Kent, Brian M.

2013-01-01

Forecasting the outputs of dynamic systems develops a richer understanding of relevant inputs and their interrelationships than merely observing them ex post. Academic business simulations foster students' development of this critical competency, but learning outcomes can be significantly augmented with relatively simple, complementary exercises…

Expression of GRIM-19 in unexplained recurrent spontaneous abortion and possible pathogenesis.

PubMed

Yang, Yang; Cheng, Laiyang; Deng, Xiaohui; Yu, Hongling; Chao, Lan

2018-05-08

Is aberrant expression of gene associated with retinoid-interferon-induced mortality-19 (GRIM-19) associated with unexplained recurrent spontaneous abortion (URSA)? GRIM-19 deficiency may regulate regulatory T cell/ T helper 17 cell (Treg/Th17) balance partly through reactive oxygen species (ROS) - mammalian target of rapamycin (mTOR) signaling axis in URSA. Immunological disorders may cause impaired maternal immune tolerance to the fetus and result in fetal rejection. The differentiation of Treg and Th17 cells is controlled by phosphoinositide 3-kinase (PI3K)/Akt/mTOR signaling pathway. GRIM-19 participates in the immune response, but its role in URSA is largely unknown. The current study included 28 URSA patients and 30 non-pregnant healthy women. The proportion of Treg and Th17 cells in peripheral blood of URSA patients and control subjects were assessed with flow cytometry. The expression of GRIM-19 in peripheral blood lymphocytes (PBLs) was measured with quantitative real-time PCR and western blot analysis. Furthermore, the ROS level in the PBLs of URSA patients and control subjects were assessed by 2'-7'-dichlorodihydrofluorescein diacetate (DCFH-DA) staining. Then, Akt/mTOR expression in the PBLs was measured. Downregulation of GRIM-19 in Jurkat cells was performed by specific small interfering RNA (siRNA). Then, intracellular ROS production and the expression of p-mTOR, which is known to enhance Th17 differentiation and decrease Treg cell differentiation, were detected. Finally, N-acetylcysteine (NAC) was used to decrease the intracellular ROS level, and the expression of p-mTOR was measured. The proportion of Treg cells was reduced in URSA patients, whereas the proportion of Th17 cells was increased. The expression of GRIM-19 was significantly lower in PBLs of URSA patients. Furthermore, there is a considerable increase in intracellular ROS production and a high level of p-Akt and p-mTOR expression in the PBLs of URSA patients compared with the control

Laser scan of the Grimming Mts. (Austria) with the latest LiDAR VZ-4000 equipment: preliminary results

NASA Astrophysics Data System (ADS)

Bauer, Harald; Hatzenbichler, Georg; Amon, Philipp; Fallah, Mohammad; Tari, Gabor; Grasemann, Bernhard

2013-04-01

As part of a cooperation project between OMV, RIEGL and the University of Vienna the new LiDAR (Light Detection and Ranging) VZ-4000 laser scanner was tested at the Grimming Mts. of the Eastern Alps in Austria. The prominent Grimming Mts. lies in the eastern part of the Dachstein Massif at the southern margin of the Northern Calcareous Alps. The Grimming, with a peak of 2,351 m above sea level, is one of the highest isolated mountains in Europe. Because of its spectacular topography, the Grimming has been used as an important surface reference mark since 1822. From a structural geology standpoint, the Grimming forms a huge antiform made up of dominantly well-bedded Triassic Dachstein Limestone. Because of the relatively well exposed bedrock surfaces above the tree-line and the fairly complex internal structure, the Grimming Mts. provides an ideal target for testing new high resolution laser scan techniques and devices. The maximum distance from the scanning positions on the nearby valley floor to the mountain face was about 4,500 m and the generated point cloud has an average resolution of 25 points per square meter. The purpose of this work was to test the latest version of the high resolution LiDAR laser equipment in a setting which falls beyond the capabilities of most existing LiDAR devices. The results of the pilot study include high-resolution spatial data on bedding planes, fault planes and the thickness variations of individual beds within the Dachstein Limestone. For the first time, the data obtained can be directly used to generate the proper 3D geometry of folds and faults observed on the Grimming Mts. This leads to a modern understanding of this prominent Alpine anticline in terms of structural geology.

Does environmental certification in coffee promote "business as usual"? A case study from the Western Ghats, India.

PubMed

Bose, Arshiya; Vira, Bhaskar; Garcia, Claude

2016-12-01

Conservation initiatives are designed to address threats to forests and biodiversity, often through partnerships with natural-resource users who are incentivized to change their land-use and livelihood practices to avoid further biodiversity loss. In particular, direct incentives programmes that provide monetary benefits are commended for being effective in achieving conservation across short timescales. In biodiversity-rich areas, outside protected areas, such as coffee agroforestry systems, direct incentives, such as certification schemes, are used to motivate coffee producers to maintain native tree species, natural vegetation, restrict wildlife hunting, and conserve soil and water, in addition to encouraging welfare of workers. However, despite these claims, there is a lack of strong evidence of the on-ground impact of such schemes. To assess the conservation importance of certification, we describe a case study in the Western Ghats biodiversity hotspot of India, in which coffee growers are provided price incentives to adopt Rainforest Alliance certification standards. We analyse the conservation and social outcomes of this programme by studying peoples' experiences of participating in certification. Despite high compliance and effective implementation, we find a strong case for the endorsement of 'business as usual' with no changes in farm management as a result of certification. We find that such 'business as usual' participation in certification creates grounds for diminishing credibility and local support for conservation efforts. Working towards locally relevant conservation interventions, rather than implementing global blueprints, may lead to more meaningful biodiversity conservation and increased community support for conservation initiatives in coffee landscapes.

No more 'business as usual' with audit and feedback interventions: towards an agenda for a reinvigorated intervention.

PubMed

Ivers, Noah M; Sales, Anne; Colquhoun, Heather; Michie, Susan; Foy, Robbie; Francis, Jill J; Grimshaw, Jeremy M

2014-01-17

Audit and feedback interventions in healthcare have been found to be effective, but there has been little progress with respect to understanding their mechanisms of action or identifying their key 'active ingredients.' Given the increasing use of audit and feedback to improve quality of care, it is imperative to focus further research on understanding how and when it works best. In this paper, we argue that continuing the 'business as usual' approach to evaluating two-arm trials of audit and feedback interventions against usual care for common problems and settings is unlikely to contribute new generalizable findings. Future audit and feedback trials should incorporate evidence- and theory-based best practices, and address known gaps in the literature. We offer an agenda for high-priority research topics for implementation researchers that focuses on reviewing best practices for designing audit and feedback interventions to optimize effectiveness.

"No-Business-As-Usual German": A Critical Pedagogy of Business German

ERIC Educational Resources Information Center

Robinson, Benjamin

2004-01-01

In this article, the author describes his "Business German" course. His course sought to narrate a dialectic of agency and institution. He started by asserting a distinction between the intending subject--with its plural desires, interests, and identifications--and the world it acts in, through and upon. This distinction between acting subject and…

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Greenhouse gas emissions from the waste sector in Argentina in business-as-usual and mitigation scenarios.

PubMed

Santalla, Estela; Córdoba, Verónica; Blanco, Gabriel

2013-08-01

The objective of this work was the application of 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for the estimation of methane and nitrous oxide emissions from the waste sector in Argentina as a preliminary exercise for greenhouse gas (GHG) inventory development and to compare with previous inventories based on 1996 IPCC Guidelines. Emissions projections to 2030 were evaluated under two scenarios--business as usual (BAU), and mitigation--and the calculations were done by using the ad hoc developed IPCC software. According to local activity data, in the business-as-usual scenario, methane emissions from solid waste disposal will increase by 73% by 2030 with respect to the emissions of year 2000. In the mitigation scenario, based on the recorded trend of methane captured in landfills, a decrease of 50% from the BAU scenario should be achieved by 2030. In the BAU scenario, GHG emissions from domestic wastewater will increase 63% from 2000 to 2030. Methane emissions from industrial wastewater, calculated from activity data of dairy, swine, slaughterhouse, citric, sugar, and wine sectors, will increase by 58% from 2000 to 2030 while methane emissions from domestic will increase 74% in the same period. Results show that GHG emissions calculated from 2006 IPCC Guidelines resulted in lower levels than those reported in previous national inventories for solid waste disposal and domestic wastewater categories, while levels were 18% higher for industrial wastewater. The implementation of the 2006 IPCC Guidelines for National Greenhouse Inventories is now considering by the UNFCCC for non-Annex I countries in order to enhance the compilation of inventories based on comparable good practice methods. This work constitutes the first GHG emissions estimation from the waste sector of Argentina applying the 2006 IPCC Guidelines and the ad doc developed software. It will contribute to identifying the main differences between the models applied in the estimation of

Acid-Sulfate-Weathering Activity in Shergottite Sites on Mars Recorded in Grim Glasses

NASA Technical Reports Server (NTRS)

Rao, M. N.; Nyquist, L. E.; Ross, K.; Sutton, S. R.; Schwandt, C. S.

2011-01-01

Based on mass spectrometric studies of sulfur species in Shergotty and EET79001, [1] and [2] showed that sulfates and sulfides occur in different proportions in shergottites. Sulfur speciation studies in gas-rich impact-melt (GRIM) glasses in EET79001 by the XANES method [3] showed that S K-XANES spectra in GRIM glasses from Lith A indicate that S is associated with Ca and Al presumably as sulfides/sulfates whereas the XANES spectra of amorphous sulfide globules in GRIM glasses from Lith B indicate that S is associated with Fe as FeS. In these amorphous iron sulfide globules, [4] found no Ni using FE-SEM and suggested that the globules resulting from immiscible sulfide melt may not be related to the igneous iron sulfides having approximately 1-3% Ni. Furthermore, in the amorphous iron sulfides from 507 GRIM glass, [5] determined delta(sup 34)S values ranging from +3.5%o to -3.1%o using Nano-SIMS. These values plot between the delta(sup 34)S value of +5.25%o determined in the sulfate fraction in Shergotty [6] at one extreme and the value of -1.7%o obtained for igneous sulfides in EET79001 and Shergotty [7] at the other. These results suggest that the amorphous Fe-S globules likely originated by shock reduction of secondary iron sulfate phases occurring in the regolith precursor materials during impact [7]. Sulfates in the regolith materials near the basaltic shergottite sites on Mars owe their origin to surficial acid-sulfate interactions. We examine the nature of these reactions by studying the composition of the end products in altered regolith materials. For the parent material composition, we use that of the host shergottite material in which the impact glasses are situated.

Insulin upregulates GRIM-19 and protects cardiac mitochondrial morphology in type 1 diabetic rats partly through PI3K/AKT signaling pathway.

PubMed

Li, Yong-Guang; Dong, Zhi-Feng; Chen, Kan-Kai; He, Ya-Ping; Dai, Xiao-Yan; Li, Shuai; Li, Jing-Bo; Zhu, Wei; Wei, Meng

2017-11-04

Insulin is involved in the development of diabetic heart disease and is important in the activities of mitochondrial complex I. However, the effect of insulin on cardiac mitochondrial nicotinamide adenine dinucleotide dehydrogenase (ubiquinone) 1 subunit of retinoic-interferon-induced mortality 19 (GRIM-19) has not been characterized. The aim of this study was to investigate the effect of insulin on the mitochondrial GRIM-19 in the hearts of rats with streptozotocin (STZ)-induced type 1 diabetes. Protein changes of GRIM-19 were evaluated by western blotting and reverse transcription-quantitative polymerase chain reaction. Furthermore, the effects of insulin on mitochondrial complex I were detected in HeLa cells and H9C2 cardiac myocytes. During the development of diabetic heart disease, the cardiac function did not change within the 8 weeks, but the mitochondrial morphology was altered. The hearts from the rats with STZ-induced diabetes exhibited reduced expression of GRIM-19. Prior to the overt cardiac dilatation, mitochondrial alterations were already present. Following subcutaneous insulin injection, it was demonstrated that GRIM-19 protein was altered, as well as the mitochondrial morphology. The phosphoinositide 3-kinase inhibitor LY294002 had an effect on insulin signaling in H9C2 cardiacmyocytes, and decreased the level of GRIM-19 by half compared with that in the insulin group. The results indicate that insulin is essential for the control of cardiac mitochondrial morphology and the GRIM-19 expression partly via PI3K/AKT signaling pathways. Copyright © 2017. Published by Elsevier Inc.

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.

PubMed

Kim, Jeremie S; Senol Cali, Damla; Xin, Hongyi; Lee, Donghyuk; Ghose, Saugata; Alser, Mohammed; Hassan, Hasan; Ergin, Oguz; Alkan, Can; Mutlu, Onur

2018-05-09

Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be

Structural analysis of a functional DIAP1 fragment bound to grim and hid peptides.

PubMed

Wu, J W; Cocina, A E; Chai, J; Hay, B A; Shi, Y

2001-07-01

The inhibitor of apoptosis protein DIAP1 suppresses apoptosis in Drosophila, with the second BIR domain (BIR2) playing an important role. Three proteins, Hid, Grim, and Reaper, promote apoptosis, in part by binding to DIAP1 through their conserved N-terminal sequences. The crystal structures of DIAP1-BIR2 by itself and in complex with the N-terminal peptides from Hid and Grim reveal that these peptides bind a surface groove on DIAP1, with the first four amino acids mimicking the binding of the Smac tetrapeptide to XIAP. The next 3 residues also contribute to binding through hydrophobic interactions. Interestingly, peptide binding induces the formation of an additional alpha helix in DIAP1. Our study reveals the structural conservation and diversity necessary for the binding of IAPs by the Drosophila Hid/Grim/Reaper and the mammalian Smac proteins.

The import of the transcription factor STAT3 into mitochondria depends on GRIM-19, a component of the electron transport chain.

PubMed

Tammineni, Prasad; Anugula, Chandrashekhar; Mohammed, Fareed; Anjaneyulu, Murari; Larner, Andrew C; Sepuri, Naresh Babu Venkata

2013-02-15

The signal transducer and activator of transcription 3 (STAT3), a nuclear transcription factor, is also present in mitochondria and regulates cellular respiration in a transcriptional-independent manner. The mechanism of STAT3 import into mitochondria remains obscure. In this report we show that mitochondrial-localized STAT3 resides in the inner mitochondrial membrane. In vitro import studies show that the gene associated with retinoid interferon induced cell mortality 19 (GRIM-19), a complex I subunit that acts as a chaperone to recruit STAT3 into mitochondria. In addition, GRIM-19 enhances the integration of STAT3 into complex I. A S727A mutation in STAT3 reduces its import and assembly even in the presence of GRIM-19. Together, our studies unveil a novel chaperone function for GRIM-19 in the recruitment of STAT3 into mitochondria.

Enhanced control of end-group composition in poly(3-hexylthiophene)s prepared by GRIM

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

Kochemba, William Michael; Kilbey, II, S Michael; Pickel, Deanna L

The ability to prepare well-defined semiconducting polymers is essential for understanding the link between structure and function in organic photovoltaic devices. A general method for enhanced control of the degree of functionality of end-functionalized poly(3-hexylthiophene)s (P3HT) prepared by Grignard Metathesis (GRIM) polymerization has been developed. In the absence of additives, the degree of functionality of end-functional P3HTs prepared by quenching of the GRIM polymerization with a Grignard reagent is dependent on the Grignard reagent utilized. In this study, additives such as styrene and 1-pentene are shown to alter the end-group composition of tolyl-functionalized P3HTs as determined by MALDI-TOF MS. Inmore » particular, when quenching the GRIM polymerization with tolylmagnesium bromide a modest decrease in the difunctional product is observed, and the yield of the monofunctional product increases significantly. Temperature and lithium chloride (LiCl) addition also play impactful roles. Monofunctional P3HT is found to be the major product (65%) when the functionalization is done in the presence of LiCl and styrene at 0oC, whereas in the absence of additives the monofunctional product is present at only 20%.« less

Forecasting forecast skill

NASA Technical Reports Server (NTRS)

Kalnay, Eugenia; Dalcher, Amnon

1987-01-01

It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

Oxidation States of Grim Glasses in EET79001 Based on Vanadium Valence

NASA Technical Reports Server (NTRS)

Sutton, S. R.; Rao, M. N.; Nyquist, L. E.

2010-01-01

Gas-rich impact-melt (GRIM) glasses in SNC meteorites are very rich in Martian atmospheric noble gases and sulfur suggesting a possible occurrence of regolith-derived secondary mineral assemblages in these samples. Previously, we have studied two GRIM glasses, 506 and 507, from EET79001 Lith A and Lith B, respectively, for elemental abundances and spatial distribution of sulfur using EMPA (WDS) and FE-SEM (EDS) techniques and for sulfur-speciation using K-edge XANES techniques. These elemental and FE-SEM micro-graph data at several locations in the GRIM glasses from Shergotty (DBS), Zagami 994 and EET79001, Lith B showed that FeO and SO3 are positively correlated (SO3 represents a mixture of sulfide and sulfate). FE-SEM (EDS) study revealed that the sulfur-rich pockets in these glasses contain numerous micron-sized iron-sulfide (Fe-S) globules sequestered throughout the volume. However, in some areas (though less frequently), we detected significant Fe-S-O signals suggesting the occurrence of iron sulfate. These GRIM glasses were studied by K-edge microXANES techniques for sulfur speciation in association with iron in sulfur-rich areas. In both samples, we found the sulfur speciation dominated by sulfide with minor oxidized sulfur mixed in with various proportions. The abundance of oxidized sulfur was greater in 506 than in 507. Based on these results, we hypothesize that sulfur initially existed as sulfate in the glass precursor materials and, on shock-impact melting of the precursor materials producing these glasses, the oxidized sulfur was reduced to predominately sulfide. In order to further test this hypothesis, we have used microXANES to measure the valence states of vanadium in GRIM glasses from Lith A and Lith B to complement and compare with previous analogous measurements on Lith C (note: 506 and 507 contain the largest amounts of martian atmospheric gases but the gas-contents in Lith C measured by are unknown). Vanadium is ideal for addressing this re

Regional Assessment of Urban Impacts on Landcover and Open Space Finds a Smart Urban Growth Policy Performs Little Better than Business as Usual

PubMed Central

Thorne, James H.; Santos, Maria J.; Bjorkman, Jacquelyn H.

2013-01-01

Assessment of landscape change is critical for attainment of regional sustainability goals. Urban growth assessments are needed because over half the global population now lives in cities, which impact biodiversity, ecosystem structure and ecological processes. Open space protection is needed to preserve these attributes, and provide the resources humans need. The San Francisco Bay Area, California, is challenged to accommodate a population increase of 3.07 million while maintaining the region’s ecosystems and biodiversity. Our analysis of 9275 km2 in the Bay Area links historic trends for three measures: urban growth, protected open space, and landcover types over the last 70 years to future 2050 projections of urban growth and open space. Protected open space totaled 348 km2 (3.7% of the area) in 1940, and expanded to 2221 km2 (20.2%) currently. An additional 1038 km2 of protected open space is targeted (35.1%). Urban area historically increased from 396.5 km2 to 2239 km2 (24.1% of the area). Urban growth during this time mostly occurred at the expense of agricultural landscapes (62.9%) rather than natural vegetation. Smart Growth development has been advanced as a preferred alternative in many planning circles, but we found that it conserved only marginally more open space than Business-as-usual when using an urban growth model to portray policies for future urban growth. Scenarios to 2050 suggest urban development on non-urban lands of 1091, 956, or 179 km2, under Business-as-usual, Smart Growth and Infill policy growth scenarios, respectively. The Smart Growth policy converts 88% of natural lands and agriculture used by Business-as-usual, while Infill used only 40% of those lands. Given the historic rate of urban growth, 0.25%/year, and limited space available, the Infill scenario is recommended. While the data may differ, the use of an historic and future framework to track these three variables can be easily applied to other metropolitan areas. PMID

Regional assessment of urban impacts on landcover and open space finds a smart urban growth policy performs little better than business as usual.

PubMed

Thorne, James H; Santos, Maria J; Bjorkman, Jacquelyn H

2013-01-01

Assessment of landscape change is critical for attainment of regional sustainability goals. Urban growth assessments are needed because over half the global population now lives in cities, which impact biodiversity, ecosystem structure and ecological processes. Open space protection is needed to preserve these attributes, and provide the resources humans need. The San Francisco Bay Area, California, is challenged to accommodate a population increase of 3.07 million while maintaining the region's ecosystems and biodiversity. Our analysis of 9275 km² in the Bay Area links historic trends for three measures: urban growth, protected open space, and landcover types over the last 70 years to future 2050 projections of urban growth and open space. Protected open space totaled 348 km² (3.7% of the area) in 1940, and expanded to 2221 km² (20.2%) currently. An additional 1038 km² of protected open space is targeted (35.1%). Urban area historically increased from 396.5 km² to 2239 km² (24.1% of the area). Urban growth during this time mostly occurred at the expense of agricultural landscapes (62.9%) rather than natural vegetation. Smart Growth development has been advanced as a preferred alternative in many planning circles, but we found that it conserved only marginally more open space than Business-as-usual when using an urban growth model to portray policies for future urban growth. Scenarios to 2050 suggest urban development on non-urban lands of 1091, 956, or 179 km², under Business-as-usual, Smart Growth and Infill policy growth scenarios, respectively. The Smart Growth policy converts 88% of natural lands and agriculture used by Business-as-usual, while Infill used only 40% of those lands. Given the historic rate of urban growth, 0.25%/year, and limited space available, the Infill scenario is recommended. While the data may differ, the use of an historic and future framework to track these three variables can be easily applied to other metropolitan areas.

Business as Usual

ERIC Educational Resources Information Center

Cohen, Jeremy

2010-01-01

Even in an industry where rapid change is the status quo, it takes a special kind of company to handle the training challenges posed by a major corporate acquisition and massive product rollout. No one has ever accused Verizon of thinking small-scale when it comes to training initiatives, but over the last year, the telecommunications giant…

Business Not as Usual: Developing Socially Conscious Entrepreneurs and Intrapreneurs

ERIC Educational Resources Information Center

Parris, Denise Linda; McInnis-Bowers, Cecilia

2017-01-01

Our objective was to design an introductory business course to shape the mind-sets and skill sets of the next generation of socially conscious practitioners--to help students develop a sense of self-efficacy built on the confidence that they can make a positive impact on the world using entrepreneurial thinking and action. Essentially, the focus…

Essential role of grim-led programmed cell death for the establishment of corazonin-producing peptidergic nervous system during embryogenesis and metamorphosis in Drosophila melanogaster.

PubMed

Lee, Gyunghee; Sehgal, Ritika; Wang, Zixing; Nair, Sudershana; Kikuno, Keiko; Chen, Chun-Hong; Hay, Bruce; Park, Jae H

2013-03-15

In Drosophila melanogaster, combinatorial activities of four death genes, head involution defective (hid), reaper (rpr), grim, and sickle (skl), have been known to play crucial roles in the developmentally regulated programmed cell death (PCD) of various tissues. However, different expression patterns of the death genes also suggest distinct functions played by each. During early metamorphosis, a great number of larval neurons unfit for adult life style are removed by PCD. Among them are eight pairs of corazonin-expressing larval peptidergic neurons in the ventral nerve cord (vCrz). To reveal death genes responsible for the PCD of vCrz neurons, we examined extant and recently available mutations as well as RNA interference that disrupt functions of single or multiple death genes. We found grim as a chief proapoptotic gene and skl and rpr as minor ones. The function of grim is also required for PCD of the mitotic sibling cells of the vCrz neuronal precursors (EW3-sib) during embryonic neurogenesis. An intergenic region between grim and rpr, which, it has been suggested, may enhance expression of three death genes in embryonic neuroblasts, appears to play a role for the vCrz PCD, but not for the EW3-sib cell death. The death of vCrz neurons and EW3-sib is triggered by ecdysone and the Notch signaling pathway, respectively, suggesting distinct regulatory mechanisms of grim expression in a cell- and developmental stage-specific manner.

Essential role of grim-led programmed cell death for the establishment of corazonin-producing peptidergic nervous system during embryogenesis and metamorphosis in Drosophila melanogaster

PubMed Central

Lee, Gyunghee; Sehgal, Ritika; Wang, Zixing; Nair, Sudershana; Kikuno, Keiko; Chen, Chun-Hong; Hay, Bruce; Park, Jae H.

2013-01-01

Summary In Drosophila melanogaster, combinatorial activities of four death genes, head involution defective (hid), reaper (rpr), grim, and sickle (skl), have been known to play crucial roles in the developmentally regulated programmed cell death (PCD) of various tissues. However, different expression patterns of the death genes also suggest distinct functions played by each. During early metamorphosis, a great number of larval neurons unfit for adult life style are removed by PCD. Among them are eight pairs of corazonin-expressing larval peptidergic neurons in the ventral nerve cord (vCrz). To reveal death genes responsible for the PCD of vCrz neurons, we examined extant and recently available mutations as well as RNA interference that disrupt functions of single or multiple death genes. We found grim as a chief proapoptotic gene and skl and rpr as minor ones. The function of grim is also required for PCD of the mitotic sibling cells of the vCrz neuronal precursors (EW3-sib) during embryonic neurogenesis. An intergenic region between grim and rpr, which, it has been suggested, may enhance expression of three death genes in embryonic neuroblasts, appears to play a role for the vCrz PCD, but not for the EW3-sib cell death. The death of vCrz neurons and EW3-sib is triggered by ecdysone and the Notch signaling pathway, respectively, suggesting distinct regulatory mechanisms of grim expression in a cell- and developmental stage-specific manner. PMID:23519152

A revised radiation package of G-packed McICA and two-stream approximation: Performance evaluation in a global weather forecasting model

NASA Astrophysics Data System (ADS)

Baek, Sunghye

2017-07-01

For more efficient and accurate computation of radiative flux, improvements have been achieved in two aspects, integration of the radiative transfer equation over space and angle. First, the treatment of the Monte Carlo-independent column approximation (MCICA) is modified focusing on efficiency using a reduced number of random samples ("G-packed") within a reconstructed and unified radiation package. The original McICA takes 20% of CPU time of radiation in the Global/Regional Integrated Model systems (GRIMs). The CPU time consumption of McICA is reduced by 70% without compromising accuracy. Second, parameterizations of shortwave two-stream approximations are revised to reduce errors with respect to the 16-stream discrete ordinate method. Delta-scaled two-stream approximation (TSA) is almost unanimously used in Global Circulation Model (GCM) but contains systematic errors which overestimate forward peak scattering as solar elevation decreases. These errors are alleviated by adjusting the parameterizations of each scattering element—aerosol, liquid, ice and snow cloud particles. Parameterizations are determined with 20,129 atmospheric columns of the GRIMs data and tested with 13,422 independent data columns. The result shows that the root-mean-square error (RMSE) over the all atmospheric layers is decreased by 39% on average without significant increase in computational time. Revised TSA developed and validated with a separate one-dimensional model is mounted on GRIMs for mid-term numerical weather forecasting. Monthly averaged global forecast skill scores are unchanged with revised TSA but the temperature at lower levels of the atmosphere (pressure ≥ 700 hPa) is slightly increased (< 0.5 K) with corrected atmospheric absorption.

Vietnam's forest transition in retrospect: demonstrating weaknesses in business-as-usual scenarios for REDD.

PubMed

Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld

2015-05-01

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.

Vietnam's Forest Transition in Retrospect: Demonstrating Weaknesses in Business-as-Usual Scenarios for REDD+

NASA Astrophysics Data System (ADS)

Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld

2015-05-01

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.

Business as Usual: Exploring Private Sector Participation in American Public Schools.

ERIC Educational Resources Information Center

Shakeshaft, Charol; Trachtman, Roberta

1987-01-01

Chief executives of corporations were surveyed to see why they participated in school-business collaborations. The survey focused on: (1) what type of corporation was likely to participate; (2) types of partnership; (4) benefits for education; (5) beliefs about corporate involvement; (6) education and corporate philanthropy; and (7) the impact of…

Medicine as a business.

PubMed

Matthews, Merrill

2004-09-01

There is a growing debate over whether medicine should function like a business, guided, as businesses are, by concerns such as profits and customer satisfaction. Of course, for-profit businesses already permeate medicine, and those businesses are not confused about their priorities: providing high quality goods and services people want, at affordable prices. These companies know that they must do well in order to continue doing good. Critics of the business model argue that the profit motive makes health care too expensive and that only by nationalizing the health care system can doctors provide high quality care at an affordable cost to society. However, a survey of journals and newspaper articles about the Canadian health care system, often cited as an anti-business model for U.S. reform, reveals that quality has suffered significantly under that system. Patients wait in long lines for health care, and sometimes cannot get help at all. This paper argues that incentives in the U.S. health care system are complicated, and that health care needs to work more like a business--not less. Doctors don't know whom they are serving--patients, insurers, employers or the government--because it is usually someone other than the patient who it paying the bill. The way to get the incentives structured properly is to allow patients to control more of their health care dollars--perhaps through a system of Medical Savings Accounts. Following the business model is the only way to ensure that medicine provides high quality services at affordable prices--just like every other sector of the economy.

Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

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

It's Not Business as Usual: New and Emerging Career in Marketing, Finance, and Management

ERIC Educational Resources Information Center

Miller, April J.

2010-01-01

There have been many changes in the field of business as a result of technological advancements, government regulations, and shifts in focus. These new career opportunities have arisen as a result: social media marketers, financial examiners, and project managers. In this article, the author discusses these new and emerging career opportunities in…

Business not as usual

NASA Technical Reports Server (NTRS)

1991-01-01

Typical design simplification ideas which reduce costs; combustion chamber design simplification; combustion chambers; castings vs. machined and welded forgings; automated inspection; and life cycle costs are outlined. This presentation is represented by viewgraphs.

English as the Language of International Business Communication

ERIC Educational Resources Information Center

Kuiper, Alison

2007-01-01

In teaching business communication, instructors usually can take for granted that English is the language of business communication in a globalised world. Even in a multicultural and multilinguistic country such as Malaysia, the assumption that English is the language to use is shared by those who manage programs, those who teach, and students.…

Changes in snowmelt runoff timing in western North America under a 'business as usual' climate change scenario

USGS Publications Warehouse

Stewart, I.T.; Cayan, D.R.; Dettinger, M.D.

2004-01-01

Spring snowmelt is the most important contribution of many rivers in western North America. If climate changes, this contribution may change. A shift in the timing of springtime snowmelt towards earlier in the year already is observed during 1948-2000 in many western rivers. Streamflow timing changes for the 1995-2099 period are projected using regression relations between observed streamflow-timing responses in each river, measured by the temporal centroid of streamflow (CT) each year, and local temperature (TI) and precipitation (PI) indices. Under 21st century warming trends predicted by the Parallel Climate Model (PCM) under business-as-usual greenhouse-gas emissions, streamflow timing trends across much of western North America suggest even earlier springtime snowmelt than observed to date. Projected CT changes are consistent with observed rates and directions of change during the past five decades, and are strongest in the Pacific Northwest, Sierra Nevada, and Rocky Mountains, where many rivers eventually run 30-40 days earlier. The modest PI changes projected by PCM yield minimal CT changes. The responses of CT to the simultaneous effects of projected TI and PI trends are dominated by the TI changes. Regression-based CT projections agree with those from physically-based simulations of rivers in the Pacific Northwest and Sierra Nevada.

Proceedings of the 1983 EMU Conference on Foreign Languages for Business (Ypsilanti, Michigan, April 7-9, 1983). Part II: Program Development and Retraining Strategies.

ERIC Educational Resources Information Center

Voght, Geoffrey M., Ed.

A collection of 10 papers from the second part of the conference on the applications of foreign languages and international studies to business focuses on the development of programs in foreign languages for business purposes. The papers include: "Foreign Languages for Global Vocations: From Theory to Practice" (Rochelle K. Kelz), "A Grim(m) Fairy…

Evolving forecasting classifications and applications in health forecasting

PubMed Central

Soyiri, Ireneous N; Reidpath, Daniel D

2012-01-01

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

Ability of crime, demographic and business data to forecast areas of increased violence.

PubMed

Bowen, Daniel A; Mercer Kollar, Laura M; Wu, Daniel T; Fraser, David A; Flood, Charles E; Moore, Jasmine C; Mays, Elizabeth W; Sumner, Steven A

2018-05-24

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.

Teaching across Borders: Business as Usual?

ERIC Educational Resources Information Center

Allen, Bobbe McGhie

2011-01-01

The quest to comprehend how cultural differences can impact learning is one of those intriguing challenges that continue to beguile some scholars and educational leaders even at a time that is characterized as globalized. This dissertation is a qualitative case study about teaching to culturally diverse populations and is primarily based on the…

Managing health care costs: strategies available to small businesses.

PubMed

Higgins, C W; Finley, L; Kinard, J

1990-07-01

Although health care costs continue to rise at an alarming rate, small businesses can take steps to help moderate these costs. First, business firms must restructure benefits so that needless surgery is eliminated and inpatient hospital care is minimized. Next, small firms should investigate the feasibility of partial self-insurance options such as risk pooling and purchasing preferred premium plans. Finally, small firms should investigate the cost savings that can be realized through the use of alternative health care delivery systems such as HMOs and PPOs. Today, competition is reshaping the health care industry by creating more options and rewarding efficiency. The prospect of steadily rising prices and more choices makes it essential that small employers become prudent purchasers of employee health benefits. For American businesses, the issue is crucial. Unless firms can control health care costs, they will have to keep boosting the prices of their goods and services and thus become less competitive in the global marketplace. In that event, many workers will face a prospect even more grim than rising medical premiums: losing their jobs.

Business as Usual: A Lack of Institutional Innovation in Global Health Governance Comment on "Global Health Governance Challenges 2016 - Are We Ready?"

PubMed

Lee, Kelley

2016-08-17

There were once again high expectations that a major global health event - the Ebola virus outbreak of 2014-2015 - would trigger meaningfully World Health Organization (WHO) reform and strengthen global health governance (GHG). Rather than a "turning point," however, the global community has gone back to business as usual. This has occurred against a backdrop of worldwide political turmoil, characterised by a growing rejection of existing political leaders and state-centric institutions. Debates about GHG so far have given insufficient attention to the need for institutional innovation. This entails rethinking the traditional bureaucratic model of postwar intergovernmental organizations which is disconnected from the transboundary, fast-paced nature of today's globalizing world. © 2017 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

PubMed Central

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

PubMed

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

TRAVEL FORECASTER

NASA Technical Reports Server (NTRS)

Mauldin, L. E.

1994-01-01

Business travel planning within an organization is often a time-consuming task. Travel Forecaster is a menu-driven, easy-to-use program which plans, forecasts cost, and tracks actual vs. planned cost for business-related travel of a division or branch of an organization and compiles this information into a database to aid the travel planner. The program's ability to handle multiple trip entries makes it a valuable time-saving device. Travel Forecaster takes full advantage of relational data base properties so that information that remains constant, such as per diem rates and airline fares (which are unique for each city), needs entering only once. A typical entry would include selection with the mouse of the traveler's name and destination city from pop-up lists, and typed entries for number of travel days and purpose of the trip. Multiple persons can be selected from the pop-up lists and multiple trips are accommodated by entering the number of days by each appropriate month on the entry form. An estimated travel cost is not required of the user as it is calculated by a Fourth Dimension formula. With this information, the program can produce output of trips by month with subtotal and total cost for either organization or sub-entity of an organization; or produce outputs of trips by month with subtotal and total cost for international-only travel. It will also provide monthly and cumulative formats of planned vs. actual outputs in data or graph form. Travel Forecaster users can do custom queries to search and sort information in the database, and it can create custom reports with the user-friendly report generator. Travel Forecaster 1.1 is a database program for use with Fourth Dimension Runtime 2.1.1. It requires a Macintosh Plus running System 6.0.3 or later, 2Mb of RAM and a hard disk. The standard distribution medium for this package is one 3.5 inch 800K Macintosh format diskette. Travel Forecaster was developed in 1991. Macintosh is a registered trademark of

Business as Usual: Sex Stereotyping in Business Education.

ERIC Educational Resources Information Center

Project on Sex Stereotyping in Education, Red Bank, NJ.

The module described in this document is part of a series of instructional modules on sex-role stereotyping in education. This document (including all but the cassette tape) is the module that explores the myths and stereotypes that have limited women in the world of work. The material provides suggestions for helping students expand occupational…

Curcumin synergistically increases effects of β-interferon and retinoic acid on breast cancer cells in vitro and in vivo by up-regulation of GRIM-19 through STAT3-dependent and STAT3-independent pathways.

PubMed

Ren, Min; Wang, Ying; Wu, Xiaodong; Ge, Suxia; Wang, Benzhong

2017-03-01

The study aimed to investigate the effects of combination treatment of curcumin and β-interferon (IFN-β)/retinoic acid (RA) on breast cancer cells, including cell viability, apoptosis and migration, and to determine the mechanisms related to GRIM-19 through STAT3-dependent and STAT3-independent pathways. The following groups were used for the in vitro experiment: control siRNA, GRIM-19 siRNA, IFN-β/RA and IFN-β/RA + curcumin. Cell viability is by the MTT method, cell apoptosis by flow cytometry and cell migration by wound healing experiment; GRIM-19, STAT3, survivin, Bcl-2, GADD153 and COX-2 expression was measured by Western blot. In vivo experiment, MCF-7 cells were subcutaneously injected into nude mice. GRIM-19 siRNA promoted MCF-7 cell proliferation and migration; inhibited cell apoptosis; and promoted the expression of STAT3, survivin, Bcl-2 and MMP-9. IFN-β/RA inhibited cell proliferation and migration; promoted cell apoptosis; up-regulated GRIM-19; and inhibited the expression of STAT3, survivin, Bcl-2 and MMP-9. Combination treatment of curcumin and IFN-β/RA had a stronger effect than that of the IFN-β/RA group. In addition, curcumin and IFN-β/RA combination inhibited the expression of COX-2 and up-regulated GADD153. Curcumin synergistically increases the effects of IFN-β/RA on breast cancer cells. The mechanism may be related to the up-regulation of GRIM-19 through STAT3-dependent and STAT3-independent pathways.

Acquisition forecast: Fiscal year 1995

NASA Technical Reports Server (NTRS)

1995-01-01

This volume includes projections of all anticipated FY95, and beyond, NASA contract actions above $25,000 that small and small disadvantaged businesses may be able to perform under direct contract with the government or as subcontractors. The forecast consolidates anticipated procurements at each NASA center into an agencywide report, with the aim of increasing industries' advance knowledge of NASA requirements and enhancing competition in contracting. Each center forecast report is divided into three principal categories of procurement: research and development, services, and supplies and equipment.

The Art and Science of Long-Range Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Hathaway, David H.; Wilson, Robert M.

2006-01-01

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

Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

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

Das, S.

1991-12-01

The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import moremore » petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.« less

Business as Usual? Not in Vermont.

ERIC Educational Resources Information Center

Proulx, Raymond J.; Jimerson, Lorna

1998-01-01

Vermont's Equal Education Opportunity Act of 1997 will radically increase the proportion of state money designated for public education and transform the entire state taxation system. Spurred by a court decision invalidating the state's school finance system, Act 60 establishes a statewide property tax for a general state support grant, includes…

Atmospheric Fluoroform (CHF3, HFC-23) at Cape Grim, Tasmania (1978-1995)

DOE Data Explorer

Oram, D. E. [University of East Anglia, Norwich, United Kingdom; Sturges, W. T. [University of East Anglia, Norwich, United Kingdom; Penkett, S. A. [University of East Anglia, Norwich, United Kingdom; McCulloch, A. [ICI Chemicals and Polymers, Ltd., Cheshire, United Kingdom; Fraser, P. J. [CRC for Southern Hemisphere Meteorology, Victoria, Australia

2000-10-01

The sampling and analytical methods are described more fully in Oram et al. (1998). In summary, air samples were taken from the archive of Cape Grim, Tasmania (41oS, 145oE) air samples collected from 1978 through 1995. Comparisons of CFC-11, CFC-12, CFC-113, CH3CCl3, and CH4 data between archive samples and corresponding in-situ samples for the same dates confirm that the archive samples are both representative and stable over time. Samples were analyzed by gas chromatography-mass spectrometry (GC-MS), using a KCl-passivated alumina PLOT column. Fluoroform was monitored on mass 69 (CF3+). The analytical precision (one standard deviation of the mean) for two or three replicate analyses was typically ± 1% of the mean measured value. The overall uncertainty of the observed data is ± 10%, taking into account uncertainties in the preparation of the primary standards, the purity of the fluoroform used to make the primary standards, as well as the analytical precision.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

FAA (Federal Aviation Administration) Aviation Forecasts: Fiscal Years 1989-2000

DTIC Science & Technology

1989-03-01

predict interim business cycles. FAA FORECAST ECONOMIC ASSUMPTIONS FISCAL YEARS 1989 - 2000 HISTORICAL FORECAST PERCENT AVERAGE ANNUAL GROWTH ECONOMIC ...During previous economic cycles, changes in the general aviation industry have generally paralleled changes in business activity. Empirical results have...FiFAA-APO 89- MARCH 198 US eat e T of 0rrs orci Fedra Aviatio Ad instato 0 NA II I1 Technical Report Documentation Page 1 ReotN.2. Government

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

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

2012-09-01

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

Forecasting risk of bankruptcy for machine-building plants

NASA Astrophysics Data System (ADS)

Telipenko, E.; Zakharova, A.; Sopova, Svetlana

2015-09-01

The paper presents an overview of well-known bankruptcy risk forecasting models, elaborated as by Russian so by foreign authors, on the basis of the data about financial and business activities of the biggest machine-building Russian plants. The authors substantiate and confirm appropriateness of a fuzzy set model to the problem of bankruptcy risk forecasting. This model is worked out on the basis of 10 most important factors, which have the greatest influence on sales proceeds as the main financial source for a production plant.

A Preliminary Study of Grade Forecasting by Students

ERIC Educational Resources Information Center

Armstrong, Michael J.

2013-01-01

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

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

ERIC Educational Resources Information Center

Mabert, Vincent A.; Radcliffe, Robert C.

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

41 CFR 109-27.5106-4 - Withdrawals/returns forecasts.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Management Regulations System (Continued) DEPARTMENT OF ENERGY PROPERTY MANAGEMENT REGULATIONS SUPPLY AND... forecasts. The Business Center for Precious Metals Sales and Recovery will request annually from each DOE field organization its long-range forecast of anticipated withdrawals from the pool and returns to the...

41 CFR 109-27.5106-4 - Withdrawals/returns forecasts.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Management Regulations System (Continued) DEPARTMENT OF ENERGY PROPERTY MANAGEMENT REGULATIONS SUPPLY AND... forecasts. The Business Center for Precious Metals Sales and Recovery will request annually from each DOE field organization its long-range forecast of anticipated withdrawals from the pool and returns to the...

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

NASA Astrophysics Data System (ADS)

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

2008-12-01

Observations of coronal mass ejections (CMEs) from heliospheric imagers such as the Solar Mass Ejection Imager (SMEI) can lead to significant improvements in operational space weather forecasting. We are working with the Air Force Weather Agency (AFWA) to ingest SMEI all-sky imagery with appropriate tools to help forecasters improve their operational space weather forecasts. We describe two approaches: 1) Near- real time analysis of propagating CMEs from SMEI images alone combined with near-Sun observations of CME onsets and, 2) Using these calculations of speed as a mid-course correction to the HAFv2 solar wind model forecasts. HAFv2 became operational at AFWA in late 2006. The objective is to determine a set of practical procedures that the duty forecaster can use to update or correct a solar wind forecast using heliospheric imager data. SMEI observations can be used inclusively to make storm forecasts, as recently discussed in Webb et al. (Space Weather, in press, 2008). We have developed a point-and-click analysis tool for use with SMEI images and are working with AFWA to ensure that timely SMEI images are available for analyses. When a frontside solar eruption occurs, especially if within about 45 deg. of Sun center, a forecaster checks for an associated CME observed by a coronagraph within an appropriate time window. If found, especially if the CME is a halo type, the forecaster checks SMEI observations about a day later, depending on the apparent initial CME speed, for possibly associated CMEs. If one is found, then the leading edge is measured over several successive frames and an elongation-time plot constructed. A minimum of three data points, i.e., over 3-4 orbits or about 6 hours, are necessary for such a plot. Using the solar source location and onset time of the CME from, e.g., SOHO observations, and assuming radial propagation, a distance-time relation is calculated and extrapolated to the 1 AU distance. As shown by Webb et al., the storm onset time

Probabilistic forecasting for extreme NO2 pollution episodes.

PubMed

Aznarte, José L

2017-10-01

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Use of Business Case Studies in Business German Classes.

ERIC Educational Resources Information Center

Schutte, Lilith

The use of business case studies, defined as sophisticated models that present practical business problems and theoretical guidelines that can be used to solve the problems, is discussed. It is suggested that the main advantages of case studies are that they are usually more interesting to read than theoretical materials and they encourage student…

7 CFR 612.5 - Dissemination of water supply forecasts and basic data.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Dissemination of water supply forecasts and basic data... SUPPLY FORECASTS § 612.5 Dissemination of water supply forecasts and basic data. Water supply outlook reports prepared by NRCS and its cooperators containing water supply forecasts and basic data are usually...

Spatial-temporal forecasting the sunspot diagram

NASA Astrophysics Data System (ADS)

Covas, Eurico

2017-09-01

Aims: We attempt to forecast the Sun's sunspot butterfly diagram in both space (I.e. in latitude) and time, instead of the usual one-dimensional time series forecasts prevalent in the scientific literature. Methods: We use a prediction method based on the non-linear embedding of data series in high dimensions. We use this method to forecast both in latitude (space) and in time, using a full spatial-temporal series of the sunspot diagram from 1874 to 2015. Results: The analysis of the results shows that it is indeed possible to reconstruct the overall shape and amplitude of the spatial-temporal pattern of sunspots, but that the method in its current form does not have real predictive power. We also apply a metric called structural similarity to compare the forecasted and the observed butterfly cycles, showing that this metric can be a useful addition to the usual root mean square error metric when analysing the efficiency of different prediction methods. Conclusions: We conclude that it is in principle possible to reconstruct the full sunspot butterfly diagram for at least one cycle using this approach and that this method and others should be explored since just looking at metrics such as sunspot count number or sunspot total area coverage is too reductive given the spatial-temporal dynamical complexity of the sunspot butterfly diagram. However, more data and/or an improved approach is probably necessary to have true predictive power.

No more business as usual: enticing companies to sharply lower the public health costs of the products they sell.

PubMed

Sugarman, S

2009-03-01

Cigarettes, alcohol, junk food and motor vehicles cause a staggeringly high level of death, injury and disease. Business leaders from the industries that make these products currently try to frame these negative outcomes as 'collateral damage' that is someone else's problem. That framing is not only morally objectionable, but also overlooks the possibility that, with proper prodding, industry could substantially mitigate these public health disasters. A promising regulatory tool called 'performance-based regulation' is a new approach to combating the problem. Simply put, performance-based regulation would impose a legal obligation on manufacturers to reduce their negative social costs. Rather than suing the firms for damages, or telling them how they should run their businesses differently (as typical 'command and control' regimes do), performance-based regulation allows the firms to determine how best to decrease today's negative public health consequences. Like other public health strategies, performance-based regulation shifts the focus away from individual consumers on to those who are far more likely to achieve real public health gains. Analogous to a tax on causing harm that exceeds a threshold level, performance-based regulation seeks to harness private initiative in pursuit of the public good.

Antibiotic research and development: business as usual?

PubMed

Harbarth, S; Theuretzbacher, U; Hackett, J

2015-01-01

The global burden of antibiotic resistance is tremendous and, without new anti-infective strategies, will continue to increase in the coming decades. Despite the growing need for new antibiotics, few pharmaceutical companies today retain active antibacterial drug discovery programmes. One reason is that it is scientifically challenging to discover new antibiotics that are active against the antibiotic-resistant bacteria of current clinical concern. However, the main hurdle is diminishing economic incentives. Increased global calls to minimize the overuse of antibiotics, the cost of meeting regulatory requirements and the low prices of currently marketed antibiotics are strong deterrents to antibacterial drug development programmes. New economic models that create incentives for the discovery of new antibiotics and yet reconcile these incentives with responsible antibiotic use are long overdue. DRIVE-AB is a €9.4 million public-private consortium, funded by the EU Innovative Medicines Initiative, that aims to define a standard for the responsible use of antibiotics and to develop, test and recommend new economic models to incentivize investment in producing new anti-infective agents. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Good Bye Traditional Budgeting, Hello Rolling Forecast: Has the Time Come?

ERIC Educational Resources Information Center

Zeller, Thomas L.; Metzger, Lawrence M.

2013-01-01

This paper argues for a new approach to accounting textbook budgeting material. The business environment is not stable. Change is continuous, for large and small business alike. A business must act and react to generate shareholder value. The rolling forecast provides the necessary navigational insight. The traditional annual static budget does…

Simulation of shear plugging through thin plates using the GRIM Eulerian hydrocode

NASA Astrophysics Data System (ADS)

Church, P.; Cornish, R.; Cullis, I.; Lynch, N.

2000-03-01

Ballistic experiments have been performed using aluminum spheres against 10-mm rolled homogenous armour (RHA), MARS270, MARS300, and titanium alloy plates to investigate the influence of the plugging mechanism on material properties. The experiments have measured the threshold for plug mass and velocity as well as the recovered aluminum sphere mass over a range of velocities. Some of the experiments have been simulated using the in-house second generation Eulerian hydrocode GRIM. The calculations feature advanced material algorithms derived from interrupted tensile testing techniques and a triaxial failure model derived from notched tensile tests over a range of strain rates and temperatures. The effect of mesh resolution on the results has been investigated and understood. The simulation results illustrate the importance of the constitutive model in the shear localization process and the subsequent plugging phenomena. The stress triaxiality is seen as the dominant feature in controlling the onset and subsequent propagation of the crack leading to the shear plug. The simulations have demonstrated that accurate numerics coupled with accurate constitutive and fracture algorithms can successfully reproduce the observed experimental features. However, extrapolation of the fracture data leads to the simulations overpredicting the plug damage. The reasons for this are discussed.

Forecasting approaches to the Mekong River

NASA Astrophysics Data System (ADS)

Plate, E. J.

2009-04-01

Hydrologists distinguish between flood forecasts, which are concerned with events of the immediate future, and flood predictions, which are concerned with events that are possible, but whose date of occurrence is not determined. Although in principle both involve the determination of runoff from rainfall, the analytical approaches differ because of different objectives. The differences between the two approaches will be discussed, starting with an analysis of the forecasting process. The Mekong River in south-east Asia is used as an example. Prediction is defined as forecast for a hypothetical event, such as the 100-year flood, which is usually sufficiently specified by its magnitude and its probability of occurrence. It forms the basis for designing flood protection structures and risk management activities. The method for determining these quantities is hydrological modeling combined with extreme value statistics, today usually applied both to rainfall events and to observed river discharges. A rainfall-runoff model converts extreme rainfall events into extreme discharges, which at certain gage points along a river are calibrated against observed discharges. The quality of the model output is assessed against the mean value by means of the Nash-Sutcliffe quality criterion. The result of this procedure is a design hydrograph (or a family of design hydrographs) which are used as inputs into a hydraulic model, which converts the hydrograph into design water levels according to the hydraulic situation of the location. The accuracy of making a prediction in this sense is not particularly high: hydrologists know that the 100-year flood is a statistical quantity which can be estimated only within comparatively wide error bounds, and the hydraulics of a river site, in particular under conditions of heavy sediment loads has many uncertainties. Safety margins, such as additional freeboards are arranged to compensate for the uncertainty of the prediction. Forecasts, on the

Survival Sales Forecasting.

ERIC Educational Resources Information Center

Paradiso, James; Stair, Kenneth

Intended to provide insight into the dynamics of demand analysis, this paper presents an eight-step method for forecasting sales. Focusing on sales levels that must be achieved to enjoy targeted profits in favor of the usual approach of emphasizing how much will be sold within a given period, a sample situation is provided to illustrate this…

Behavioural therapies versus treatment as usual for depression

PubMed Central

Caldwell, Deborah; Hunot, Vivien; Moore, Theresa HM; Davies, Philippa; Jones, Hannah; Lewis, Glyn; Churchill, Rachel

2014-01-01

This is the protocol for a review and there is no abstract. The objectives are as follows: To examine the effectiveness and acceptability of all BT approaches compared with treatment as usual/waiting list/attention placebo control conditions for acute depression.To examine the effectiveness and acceptability of different BT approaches (behavioural therapy, behavioural activation, social skills training and relaxation training) compared with treatment as usual/waiting list/attention placebo control conditions for acute depression.To examine the effectiveness and acceptability of all BT approaches compared with different types of comparator (standard care, no treatment, waiting list, attention placebo) for acute depression. PMID:25411561

Humanistic therapies versus treatment as usual for depression

PubMed Central

Davies, Philippa; Hunot, Vivien; Moore, Theresa HM; Caldwell, Deborah; Jones, Hannah; Lewis, Glyn; Churchill, Rachel

2014-01-01

This is the protocol for a review and there is no abstract. The objectives are as follows: To examine the effectiveness and acceptability of all humanistic therapies compared with treatment as usual/waiting list/attention placebo control conditions for acute depression.To examine the effectiveness and acceptability of different humanistic therapy models (person-centred, gestalt, process-experiential, transactional analysis, existential and non-directive therapies) compared with treatment as usual/waiting list/attention placebo control conditions for acute depression.To examine the effectiveness and acceptability of all humanistic therapies compared with different types of comparator (standard care, no treatment, waiting list, attention placebo) for acute depression. PMID:25408624

Forecasting financial asset processes: stochastic dynamics via learning neural networks.

PubMed

Giebel, S; Rainer, M

2010-01-01

Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

Empirical seasonal forecasts of the NAO

NASA Astrophysics Data System (ADS)

Sanchezgomez, E.; Ortizbevia, M.

2003-04-01

We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

Forecasting the value of credit scoring

NASA Astrophysics Data System (ADS)

Saad, Shakila; Ahmad, Noryati; Jaffar, Maheran Mohd

2017-08-01

Nowadays, credit scoring system plays an important role in banking sector. This process is important in assessing the creditworthiness of customers requesting credit from banks or other financial institutions. Usually, the credit scoring is used when customers send the application for credit facilities. Based on the score from credit scoring, bank will be able to segregate the "good" clients from "bad" clients. However, in most cases the score is useful at that specific time only and cannot be used to forecast the credit worthiness of the same applicant after that. Hence, bank will not know if "good" clients will always be good all the time or "bad" clients may become "good" clients after certain time. To fill up the gap, this study proposes an equation to forecast the credit scoring of the potential borrowers at a certain time by using the historical score related to the assumption. The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the forecast scoring. Result shows the forecast scoring is highly accurate as compared to actual credit scoring.

A multiscale forecasting method for power plant fleet management

NASA Astrophysics Data System (ADS)

Chen, Hongmei

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market

Forecast communication through the newspaper Part 2: perceptions of uncertainty

NASA Astrophysics Data System (ADS)

Harris, Andrew J. L.

2015-04-01

In the first part of this review, I defined the media filter and how it can operate to frame and blame the forecaster for losses incurred during an environmental disaster. In this second part, I explore the meaning and role of uncertainty when a forecast, and its basis, is communicated through the response and decision-making chain to the newspaper, especially during a rapidly evolving natural disaster which has far-reaching business, political, and societal impacts. Within the media-based communication system, there remains a fundamental disconnect of the definition of uncertainty and the interpretation of the delivered forecast between various stakeholders. The definition and use of uncertainty differs especially between scientific, media, business, and political stakeholders. This is a serious problem for the scientific community when delivering forecasts to the public though the press. As reviewed in Part 1, the media filter can result in a negative frame, which itself is a result of bias, slant, spin, and agenda setting introduced during passage of the forecast and its uncertainty through the media filter. The result is invariably one of anger and fury, which causes loss of credibility and blaming of the forecaster. Generation of a negative frame can be aided by opacity of the decision-making process that the forecast is used to support. The impact of the forecast will be determined during passage through the decision-making chain where the precautionary principle and cost-benefit analysis, for example, will likely be applied. Choice of forecast delivery format, vehicle of communication, syntax of delivery, and lack of follow-up measures can further contribute to causing the forecast and its role to be misrepresented. Follow-up measures to negative frames may include appropriately worded press releases and conferences that target forecast misrepresentation or misinterpretation in an attempt to swing the slant back in favor of the forecaster. Review of

Hurricane track forecast cones from fluctuations

PubMed Central

Meuel, T.; Prado, G.; Seychelles, F.; Bessafi, M.; Kellay, H.

2012-01-01

Trajectories of tropical cyclones may show large deviations from predicted tracks leading to uncertainty as to their landfall location for example. Prediction schemes usually render this uncertainty by showing track forecast cones representing the most probable region for the location of a cyclone during a period of time. By using the statistical properties of these deviations, we propose a simple method to predict possible corridors for the future trajectory of a cyclone. Examples of this scheme are implemented for hurricane Ike and hurricane Jimena. The corridors include the future trajectory up to at least 50 h before landfall. The cones proposed here shed new light on known track forecast cones as they link them directly to the statistics of these deviations. PMID:22701776

Planning documents: a business planning strategy.

PubMed

Kaehrle, P A

2000-06-01

Strategic planning and business plan development are essential nursing management skills in today's competitive, fast paced, continually changing health care environment. Even in times of great uncertainty, nurse managers need to plan and forecast for the future. A well-written business plan allows nurse managers to communicate their expertise and proactively contribute to the programmatic decisions and changes occurring within their patient population or service area. This article presents the use of planning documents as a practical, strategic business planning strategy. Although the model addresses orthopedic services specifically, nurse managers can gain an understanding and working knowledge of planning concepts that can be applied to all patient populations.

A new forecast presentation tool for offshore contractors

NASA Astrophysics Data System (ADS)

Jørgensen, M.

2009-09-01

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

e-Business Innovation: The Next Decade

NASA Astrophysics Data System (ADS)

Marca, David A.

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

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

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).

Forecast first: An argument for groundwater modeling in reverse

USGS Publications Warehouse

White, Jeremy

2017-01-01

Numerical groundwater models are important compo-nents of groundwater analyses that are used for makingcritical decisions related to the management of ground-water resources. In this support role, models are oftenconstructed to serve a specific purpose that is to provideinsights, through simulation, related to a specific func-tion of a complex aquifer system that cannot be observeddirectly (Anderson et al. 2015).For any given modeling analysis, several modelinput datasets must be prepared. Herein, the datasetsrequired to simulate the historical conditions are referredto as the calibration model, and the datasets requiredto simulate the model’s purpose are referred to as theforecast model. Future groundwater conditions or otherunobserved aspects of the groundwater system may besimulated by the forecast model—the outputs of interestfrom the forecast model represent the purpose of themodeling analysis. Unfortunately, the forecast model,needed to simulate the purpose of the modeling analysis,is seemingly an afterthought—calibration is where themajority of time and effort are expended and calibrationis usually completed before the forecast model is evenconstructed. Herein, I am proposing a new groundwatermodeling workflow, referred to as the “forecast first”workflow, where the forecast model is constructed at anearlier stage in the modeling analysis and the outputsof interest from the forecast model are evaluated duringsubsequent tasks in the workflow.

Study on Electricity Business Expansion and Electricity Sales Based on Seasonal Adjustment

NASA Astrophysics Data System (ADS)

Zhang, Yumin; Han, Xueshan; Wang, Yong; Zhang, Li; Yang, Guangsen; Sun, Donglei; Wang, Bolun

2017-05-01

[1] proposed a novel analysis and forecast method of electricity business expansion based on Seasonal Adjustment, we extend this work to include the effect the micro and macro aspects, respectively. From micro aspect, we introduce the concept of load factor to forecast the stable value of electricity consumption of single new consumer after the installation of new capacity of the high-voltage transformer. From macro aspects, considering the growth of business expanding is also stimulated by the growth of electricity sales, it is necessary to analyse the antecedent relationship between business expanding and electricity sales. First, forecast electricity consumption of customer group and release rules of expanding capacity, respectively. Second, contrast the degree of fitting and prediction accuracy to find out the antecedence relationship and analyse the reason. Also, it can be used as a contrast to observe the influence of customer group in different ranges on the prediction precision. Finally, Simulation results indicate that the proposed method is accurate to help determine the value of expanding capacity and electricity consumption.

Road icing forecasting and detecting system

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

NASA Astrophysics Data System (ADS)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

Towards custom made seasonal/decadal forecasting

NASA Astrophysics Data System (ADS)

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark

2014-05-01

Climate indices offer the possibility to deliver information to the end user that can be easily applied to their field of work. For instance, a 3-monthly mean average temperature does not say much about the Heating Degree Days of a season, or how many frost days there are to be expected. Hence, delivering aggregated climate information can be more useful to the consumer than just raw data. In order to ensure that the end-users actually get what they need, the providers need to know what exactly they need to deliver. Hence, the specific user-needs have to be identified. In the framework of EUPORIAS, interviews with the end-user were conducted in order to learn more about the types of information that are needed. But also to investigate what knowledge exists among the users about seasonal/decadal forecasting and in what way uncertainties are taken into account. It is important that we gain better knowledge of how forecasts/predictions are applied by the end-user to their specific situation and business. EUPORIAS, which is embedded in the framework of EU FP7, aims exactly to improve that knowledge and deliver very specific forecasts that are custom made. Here we present examples of seasonal forecasts and their skill of several climate impact indices with direct relevance for specific economic sectors, such as energy. The results are compared to the visualization of conventional depiction of seasonal forecasts, such as 3 monthly average temperature tercile probabilities and the differences are highlighted.

Medicine as business and profession.

PubMed

Agich, G J

1990-12-01

This paper analyzes one dimension of the frequently alleged contradiction between treating medicine as a business and as a profession, namely the incompatibility between viewing the physician patient relationship in economic and moral terms. The paper explores the utilitarian foundations of economics and the deontological foundations of professional medical ethics as one source for the business/medicine conflict that influences beliefs about the proper understanding of the therapeutic relationship. It then, focuses on the contrast and distinction between medicine as business and profession by critically analyzing the classic economic view of the moral status of medicine articulated by Kenneth Arrow. The paper concludes with a discussion of some advantages associated with regarding medicine as a business.

Working With Suicidal Clients: "Not" Business as Usual

ERIC Educational Resources Information Center

Ellis, Thomas E.; Goldston, David B.

2012-01-01

In this introduction to a special series of articles on working with suicidal clients, we note that much of the recent growth in theory and research pertaining to suicidal individuals has been contributed by cognitive-behavioral theorists and researchers. This work has established that suicidal people manifest important cognitive vulnerabilities…

Business as Usual? It's Just Not an Option

ERIC Educational Resources Information Center

Blewitt, John

2010-01-01

There is no doubt that in order to address the serious challenges arising from anthropogenic--or human-produced--climate change, Britain, along with the rest of the world, needs to adopt policies and develop skills that will create a low-carbon economy with a highly effective use of renewable and natural resources. People need to create the…

Indicators for European Union Policies. Business as Usual?

ERIC Educational Resources Information Center

Saltelli, Andrea; D'Hombres, Beatrice; Jesinghaus, Jochen; Manca, Anna Rita; Mascherini, Massimiliano; Nardo, Michela; Saisana, Michaela

2011-01-01

This paper looks at the role of "statistics-based knowledge" in the making of EU policy. We highlight "shortcomings" in the use of statistical indicators made in the course of the Lisbon strategy, ended in 2010. In our opinion the shortcomings are: (i) The paradox of the "coexistence" within the same European…

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

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

A new scoring method for evaluating the performance of earthquake forecasts and predictions

NASA Astrophysics Data System (ADS)

Zhuang, J.

2009-12-01

This study presents a new method, namely the gambling score, for scoring the performance of earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. A fair scoring scheme should reward the success in a way that is compatible with the risk taken. Suppose that we have the reference model, usually the Poisson model for usual cases or Omori-Utsu formula for the case of forecasting aftershocks, which gives probability p0 that at least 1 event occurs in a given space-time-magnitude window. The forecaster, similar to a gambler, who starts with a certain number of reputation points, bets 1 reputation point on ``Yes'' or ``No'' according to his forecast, or bets nothing if he performs a NA-prediction. If the forecaster bets 1 reputation point of his reputations on ``Yes" and loses, the number of his reputation points is reduced by 1; if his forecasts is successful, he should be rewarded (1-p0)/p0 reputation points. The quantity (1-p0)/p0 is the return (reward/bet) ratio for bets on ``Yes''. In this way, if the reference model is correct, the expected return that he gains from this bet is 0. This rule also applies to probability forecasts. Suppose that p is the occurrence probability of an earthquake given by the forecaster. We can regard the forecaster as splitting 1 reputation point by betting p on ``Yes'' and 1-p on ``No''. In this way, the forecaster's expected pay-off based on the reference model is still 0. From the viewpoints of both the reference model and the forecaster, the rule for rewarding and punishment is fair. This method is also extended to the continuous case of point process models, where the reputation points bet by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when

Verification of space weather forecasts at the UK Met Office

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

IEA Wind Task 36 Forecasting

NASA Astrophysics Data System (ADS)

Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

2017-04-01

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

A forecast of broadcast satellite communications

NASA Technical Reports Server (NTRS)

Martino, J. P.; Lenz, R. C., Jr.

1977-01-01

This paper presents forecasts of likely changes in broadcast satellite technology, the technology of ground terminals, and the technology of terrestrial communications competitive with satellites. The impacts of these changes in technology are then assessed, using a cross-impact model of U.S. domestic telecommunications, to determine the consequences of various possible changes in communications satellite technology. These consequences are discussed in terms of various possible services, for households, businesses, and specialized customers, which might become economically viable as a result of improvements in satellite technology.

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

Layered Ensemble Architecture for Time Series Forecasting.

PubMed

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Characterisation of J(O1D) at Cape Grim 2000-2005

NASA Astrophysics Data System (ADS)

Wilson, S. R.

2014-07-01

Estimates of the rate of production of excited oxygen atoms due to the photolysis of ozone J(O1D) have been derived from radiation measurements carried out at Cape Grim, Tasmania (40.6° S, 144.7° E). These estimates agree well with measurements made during SOAPEX-II and with model estimates of clear sky photolysis rates. Observations spanning 2000-2005 have been used to quantify the impact of season, cloud and ozone column amount. The annual cycle of J(O1D) has been investigated via monthly means. These means show an inter-annual variation (monthly standard deviation) of 9%, but in midsummer and midwinter this reduces to 3-4%. Factors dependent upon solar zenith angle and satellite derived total ozone column explain 87% of the observed signal variation of the individual measurements. The impact of total column ozone, expressed as a Radiation Amplification Factor (RAF), is found to be ~1.45, in agreement with model estimates. This ozone dependence explains 20% of the variation observed at medium solar zenith angles (30-50°). The impact of clouds results in a median reduction of 14% in J(O1D) for the same solar zenith angle range. At all solar zenith angles less than 50° approximately 10% of measurements show enhanced J(O1D) due to cloud scattering and this fraction climbs to 25% at higher solar angles. Including estimates of cloudiness derived from Long Wave Radiation measurements resulted in a statistically significant fit to observations but the quality of the fit did not increase significantly as measured by the reduced R2.

An Evaluation of Critical Thinking Competencies in Business Settings

ERIC Educational Resources Information Center

Dwyer, Christopher P.; Boswell, Amy; Elliott, Mark A.

2015-01-01

Although critical thinking (CT) skills are usually considered as domain general (Gabbenesch, 2006; Halpern, 2003), CT ability may benefit from expertise knowledge and skill. The current study examined both general CT ability and CT ability related to business scenarios for individuals (a) expert in business, (b) novice in business, and (c) with no…

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis

PubMed Central

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-01

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. PMID:25398910

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis.

PubMed

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-02

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. © 2014 The Authors.

The business of demographics.

PubMed

Russell, C

1984-06-01

The emergence of "demographics" in the past 15 years is a vital tool for American business research and planning. Tracing demographic trends became important for businesses when traditional consumer markets splintered with the enormous changes since the 1960s in US population growth, age structure, geographic distribution, income, education, living arrangements, and life-styles. The mass of reliable, small-area demographic data needed for market estimates and projections became available with the electronic census--public release of Census Bureau census and survey data on computer tape, beginning with the 1970 census. Census Bureau tapes as well as printed reports and microfiche are now widely accessible at low cost through summary tape processing centers designated by the bureau and its 12 regional offices and State Data Center Program. Data accessibility, plummeting computer costs, and businessess' unfamiliarity with demographics spawned the private data industry. By 1984, 70 private companies were offering demographic services to business clients--customized information repackaged from public data or drawn from proprietary data bases created from such data. Critics protest the for-profit use of public data by companies able to afford expensive mainframe computer technology. Business people defend their rights to public data as taxpaying ceitzens, but they must ensure that the data are indeed used for the public good. They must also question the quality of demographic data generated by private companies. Business' demographic expertise will improve when business schools offer training in demography, as few now do, though 40 of 88 graduate-level demographic programs now include business-oriented courses. Lower cost, easier access to business demographics is growing as more census data become available on microcomputer diskettes and through on-line linkages with large data bases--from private data companies and the Census Bureau itself. A directory of private and

Neuronal Susceptibility to GRIM in Drosophila melanogaster Measures the Rate of Genetic Changes that Scale to Lifespan

PubMed Central

Bedoukian, Matthew A.; Rodriguez, Sarah M.; Cohen, Matthew B.; Duncan Smith, Stuart V.; Park, Jennifer

2009-01-01

Gene expression in Drosophila melanogaster changes significantly throughout life and some of these changes can be delayed by lowering ambient temperature and also by dietary restriction. These two interventions are known to slow the rate of aging as well as the accumulation of damage. It is unknown, however, whether gene expression changes that occur during development and early adult life make an animal more vulnerable to death. Here we develop a method capable of measuring the rate of programmed genetic changes during young adult life in Drosophila melanogaster and show that these changes can be delayed or accelerated in a manner that is predictive of longevity. We show that temperature shifts and dietary restriction, which slow the rate of aging in Drosophila melanogaster, extend the window of neuronal susceptibility to GRIM over-expression in a way that scales to lifespan. We propose that this susceptibility can be used to test compounds and genetic manipulations that alter the onset of senescence by changing the programmed timing of gene expression that correlates and may be causal to aging. PMID:19428445

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

NASA Astrophysics Data System (ADS)

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

Possible sources of forecast errors generated by the global/regional assimilation and prediction system for landfalling tropical cyclones. Part I: Initial uncertainties

NASA Astrophysics Data System (ADS)

Zhou, Feifan; Yamaguchi, Munehiko; Qin, Xiaohao

2016-07-01

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). The GRAPES forecasts were made for 16 landfalling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions ("initials", hereafter), which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

Discipline Based Instruction in Business Law

ERIC Educational Resources Information Center

Custin, Richard E.; Demas, John C.; Lampe, Marc; Custin, Colette L.

2013-01-01

Undergraduate business law courses typically utilize traditional textbooks organized by topic. Individual chapters, address the usual topics including contracts, torts, the court system and ethics. An innovative approach to facilitating a business law course involves segregating sections of the course into common business disciplines. Rather than…

Cerebral hemodynamics with intra-aortic balloon pump: business as usual?

PubMed

Caldas, J R; Panerai, R B; Bor-Seng-Shu, E; Almeida, J P; Ferreira, G S R; Camara, L; Nogueira, R C; Oliveira, M L; Jatene, F B; Robinson, T G; Hajjar, L A

2017-06-22

Intra-aortic balloon pump (IABP) is commonly used as mechanical support after cardiac surgery or cardiac shock. Although its benefits for cardiac function have been well documented, its effects on cerebral circulation are still controversial. We hypothesized that transfer function analysis (TFA) and continuous estimates of dynamic cerebral autoregulation (CA) provide consistent results in the assessment of cerebral autoregulation in patients with IABP. Continuous recordings of blood pressure (BP, intra-arterial line), end-tidal CO 2 , heart rate and cerebral blood flow velocity (CBFV, transcranial Doppler) were obtained (i) 5 min with IABP ratio 1:3, (ii) 5 min, starting 1 min with the IABP-ON, and continuing for another 4 min without pump assistance (IABP-OFF). Autoregulation index (ARI) was estimated from the CBFV response to a step change in BP derived by TFA and as a function of time using an autoregressive moving-average model during removal of the device (ARI t ). Critical closing pressure and resistance area-product were also obtained. ARI with IABP-ON (4.3  ±  1.2) were not different from corresponding values at IABP-OFF (4.7  ±  1.4, p  =  0.42). Removal of the balloon had no effect on ARI t , CBFV, BP, cerebral critical closing pressure or resistance area-product. IABP does not disturb cerebral hemodynamics. TFA and continuous estimates of dynamic CA can be used to assess cerebral hemodynamics in patients with IABP. These findings have important implications for the design of studies of critically ill patients requiring the use of different invasive support devices.

The Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Targeted observations to improve tropical cyclone track forecasts in the Atlantic and eastern Pacific basins

NASA Astrophysics Data System (ADS)

Aberson, Sim David

third limit, though the results are inconclusive. Due to limited aircraft resources, optimal observing strategies for these missions must be developed. Since observations in areas of decaying error modes are unlikely to have large impact on subsequent forecasts, such strategies should be based on taking observations in those geographic locations corresponding to the most rapidly growing error modes in the numerical models and on known deficiencies in current data assimilation systems. Here, the most rapidly growing modes are represented by areas of large forecast spread in the NCEP bred-mode global ensemble forecasting system. The sampling strategy requires sampling the entire target region at approximately the same resolution as the North American rawinsonde network to limit the possibly spurious spread of information from dropwindsonde observations into data-sparse regions where errors are likely to grow. When only the subset of data in these fully-sampled target regions is assimilated into the numerical models, statistically significant reduction of the track forecast errors of up to 25% within the critical first two days of the forecast are seen. These model improvements are comparable with the cumulative business-as-usual track forecast model improvements expected over eighteen years.

A research model--forecasting incident rates from optimized safety program intervention strategies.

PubMed

Iyer, P S; Haight, J M; Del Castillo, E; Tink, B W; Hawkins, P W

2005-01-01

INTRODUCTION/PROBLEM: Property damage incidents, workplace injuries, and safety programs designed to prevent them, are expensive aspects of doing business in contemporary industry. The National Safety Council (2002) estimated that workplace injuries cost $146.6 billion per year. Because companies are resource limited, optimizing intervention strategies to decrease incidents with less costly programs can contribute to improved productivity. Systematic data collection methods were employed and the forecasting ability of a time-lag relationship between interventions and incident rates was studied using various statistical methods (an intervention is not expected to have an immediate nor infinitely lasting effect on the incident rate). As a follow up to the initial work, researchers developed two models designed to forecast incident rates. One is based on past incident rate performance and the other on the configuration and level of effort applied to the safety and health program. Researchers compared actual incident performance to the prediction capability of each model over 18 months in the forestry operations at an electricity distribution company and found the models to allow accurate prediction of incident rates. These models potentially have powerful implications as a business-planning tool for human resource allocation and for designing an optimized safety and health intervention program to minimize incidents. Depending on the mathematical relationship, one can determine what interventions, where and how much to apply them, and when to increase or reduce human resource input as determined by the forecasted performance.

Forecasting intense geomagnetic activity using interplanetary magnetic field data

NASA Astrophysics Data System (ADS)

Saiz, E.; Cid, C.; Cerrato, Y.

2008-12-01

Southward interplanetary magnetic fields are considered traces of geoeffectiveness since they are a main agent of magnetic reconnection of solar wind and magnetosphere. The first part of this work revises the ability to forecast intense geomagnetic activity using different procedures available in the literature. The study shows that current methods do not succeed in making confident predictions. This fact led us to develop a new forecasting procedure, which provides trustworthy results in predicting large variations of Dst index over a sample of 10 years of observations and is based on the value Bz only. The proposed forecasting method appears as a worthy tool for space weather purposes because it is not affected by the lack of solar wind plasma data, which usually occurs during severe geomagnetic activity. Moreover, the results obtained guide us to provide a new interpretation of the physical mechanisms involved in the interaction between the solar wind and the magnetosphere using Faraday's law.

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

NASA Astrophysics Data System (ADS)

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

2006-09-01

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

How To Set Up Your Own Small Business. Study Guide.

ERIC Educational Resources Information Center

American Inst. of Small Business, Minneapolis, MN.

This study guide is intended for use with the separately available entrepreneurship education text "How To Set Up Your Own Business." The guide includes student exercises that have been designed to accompany chapters dealing with the following topics: determining whether or not to set up a small business, doing market research, forecasting sales,…

Effects of Forecasts on the Revisions of Concurrent Seasonally Adjusted Data Using the X-11 Seasonal Adjustment Procedure.

ERIC Educational Resources Information Center

Bobbitt, Larry; Otto, Mark

Three Autoregressive Integrated Moving Averages (ARIMA) forecast procedures for Census Bureau X-11 concurrent seasonal adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. Forecasts were obtained from fitted seasonal ARIMA models augmented with…

Forecasting annual aboveground net primary production in the intermountain west

USDA-ARS?s Scientific Manuscript database

For many land manager’s annual aboveground net primary production, or plant growth, is a key factor affecting business success, profitability and each land manager's ability to successfully meet land management objectives. The strategy often utilized for forecasting plant growth is to assume every y...

Student Cheating: As Serious an Academic Integrity Problem as Faculty-Administration Business as Usual?

ERIC Educational Resources Information Center

Puka, Bill

2005-01-01

Most faculty and administrators rate academic dishonesty a high crime, fatal to education. What cheating shows that merits strong opposition is a student's pride in deceptively "getting over" on professors and "the system," even where both are recognized as fair. This affection for injustice and casual disregard for honest dealings must be trained…

The Research of Regression Method for Forecasting Monthly Electricity Sales Considering Coupled Multi-factor

NASA Astrophysics Data System (ADS)

Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui

2018-01-01

The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.

A Posteriori Correction of Forecast and Observation Error Variances

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid

2005-01-01

Proposed method of total observation and forecast error variance correction is based on the assumption about normal distribution of "observed-minus-forecast" residuals (O-F), where O is an observed value and F is usually a short-term model forecast. This assumption can be accepted for several types of observations (except humidity) which are not grossly in error. Degree of nearness to normal distribution can be estimated by the symmetry or skewness (luck of symmetry) a(sub 3) = mu(sub 3)/sigma(sup 3) and kurtosis a(sub 4) = mu(sub 4)/sigma(sup 4) - 3 Here mu(sub i) = i-order moment, sigma is a standard deviation. It is well known that for normal distribution a(sub 3) = a(sub 4) = 0.

Business as Usual: Amazon.com and the Academic Library

ERIC Educational Resources Information Center

Van Ullen, Mary K.; Germain, Carol Anne

2002-01-01

In 1999, Steve Coffman proposed that libraries form a single interlibrary loan based entity patterned after Amazon.com. This study examined the suitability of Amazon.com's Web interface and record enhancements for academic libraries. Amazon.com could not deliver circulating monographs in the University at Albany Libraries' collection quickly…

Business as Usual? Not for These Middle-Grades Students

ERIC Educational Resources Information Center

Crawford, Heather; Wiest, Lynda

2011-01-01

A perpetual dilemma of schooling is how to help students develop skills needed for everyday life, including the work world. Quantitative literacy, also called numeracy, involves an ability to apply essential mathematics skills to authentic or near-authentic tasks. Carefully planned classroom activities can help students develop these important…

"The Good Man Speaking Well," or Business as Usual.

ERIC Educational Resources Information Center

Lyon, Arabella

1994-01-01

Comments on the interview of Stephen Toulmin in the preceding issue of this journal. Critiques Toulmin's concepts regarding logical consensus and pluralism. Discusses various kinds of pluralisms. (HB)

Forecasting new product diffusion using both patent citation and web search traffic

PubMed Central

Lee, Won Sang; Choi, Hyo Shin

2018-01-01

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods. PMID:29630616

Forecasting new product diffusion using both patent citation and web search traffic.

PubMed

Lee, Won Sang; Choi, Hyo Shin; Sohn, So Young

2018-01-01

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product's diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

Phantosmia as a meteorological forecaster

NASA Astrophysics Data System (ADS)

Aiello, S. R.; Hirsch, A. R.

2013-09-01

In normosmics, olfactory ability has been found to vary with ambient humidity, barometric pressure, and season. While hallucinated sensations of phantom pain associated with changes in weather have been described, a linkage to chemosensory hallucinations has heretofore not been reported. A 64-year-old white male with Parkinson's disease presents with 5 years of phantosmia of a smoky burnt wood which changed to onion-gas and then to a noxious skunk-onion excrement odor. Absent upon waking it increases over the day and persists for hours. When severe, there appears a phantom taste with the same qualities as the odor. It is exacerbated by factors that manipulate intranasal pressure, such as coughing. When eating or sniffing, the actual flavors replace the phantosmia. Since onset, he noted the intensity and frequency of the phantosmia forecasted the weather. Two to 3 h before a storm, the phantosmia intensifies from a level 0 to a 7-10, which persists through the entire thunderstorm. Twenty years prior, he reported the ability to forecast the weather, based on pain in a torn meniscus, which vanished after surgical repair. Extensive olfactory testing demonstrates underlying hyposmia. Possible mechanisms for such chemosensory-meteorological linkage includes: air pressure induced synesthesia, disinhibition of spontaneous olfactory discharge, exacerbation of ectopic discharge, affect mediated somatic sensory amplification, and misattribution error with expectation and recall bias. This is the first reported case of weather-induced exacerbation of phantosmia. Further investigation of the connection between chemosensory complaints and ambient weather is warranted.

Monitoring and Modeling: The Future of Volcanic Eruption Forecasting

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

NASA Astrophysics Data System (ADS)

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

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

Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

NASA Astrophysics Data System (ADS)

Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab

2014-01-01

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

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

Taking the business continuity programme to a corporate leadership role.

PubMed

Messer, Ira

2009-11-01

The paper discusses a process to raise the awareness and value of your continuity programme to higher levels by leveraging existing data. It gives examples of how to utilise the structure of the Business Impact Analysis tool to develop an enterprise level of information capture, and then utilise that data to generate an enterprise level 'group think' which results in incorporation of business continuity as a part of the business-as-usual model.

Introducing an M-Commerce Course into the Business Management Curriculum: Experiences and Recommendations

ERIC Educational Resources Information Center

Nandi, Santosh; Nandi, Madhavi L.

2015-01-01

Mobility has become an important extension to the business strategies of present-day organizations. Thus, organizations are increasingly seeking managers with knowledge of value chain related to mobile-oriented business activities, usually referred to as mobile commerce (m-commerce). Accordingly, business management schools are interesting in…

Business Process Aware IS Change Management in SMEs

NASA Astrophysics Data System (ADS)

Makna, Janis

Changes in the business process usually require changes in the computer supported information system and, vice versa, changes in the information system almost always cause at least some changes in the business process. In many situations it is not even possible to detect which of those changes are causes and which of them are effects. Nevertheless, it is possible to identify a set of changes that usually happen when one of the elements of the set changes its state. These sets of changes may be used as patterns for situation analysis to anticipate full range of activities to be performed to get the business process and/or information system back to the stable state after it is lost because of the changes in one of the elements. Knowledge about the change pattern gives an opportunity to manage changes of information systems even if business process models and information systems architecture are not neatly documented as is the case in many SMEs. Using change patterns it is possible to know whether changes in information systems are to be expected and how changes in information systems activities, data and users will impact different aspects of the business process supported by the information system.

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.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

NASA Astrophysics Data System (ADS)

Ma, Chaoqun; Wang, Tijian; Zang, Zengliang; Li, Zhijin

2018-07-01

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.

Stochastic demographic forecasting.

PubMed

Lee, R D

1992-11-01

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

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Business grants

NASA Astrophysics Data System (ADS)

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

Integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts

NASA Astrophysics Data System (ADS)

Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf

2016-04-01

A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

NASA Astrophysics Data System (ADS)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

Software forecasting as it is really done: A study of JPL software engineers

NASA Technical Reports Server (NTRS)

Griesel, Martha Ann; Hihn, Jairus M.; Bruno, Kristin J.; Fouser, Thomas J.; Tausworthe, Robert C.

1993-01-01

This paper presents a summary of the results to date of a Jet Propulsion Laboratory internally funded research task to study the costing process and parameters used by internally recognized software cost estimating experts. Protocol Analysis and Markov process modeling were used to capture software engineer's forecasting mental models. While there is significant variation between the mental models that were studied, it was nevertheless possible to identify a core set of cost forecasting activities, and it was also found that the mental models cluster around three forecasting techniques. Further partitioning of the mental models revealed clustering of activities, that is very suggestive of a forecasting lifecycle. The different forecasting methods identified were based on the use of multiple-decomposition steps or multiple forecasting steps. The multiple forecasting steps involved either forecasting software size or an additional effort forecast. Virtually no subject used risk reduction steps in combination. The results of the analysis include: the identification of a core set of well defined costing activities, a proposed software forecasting life cycle, and the identification of several basic software forecasting mental models. The paper concludes with a discussion of the implications of the results for current individual and institutional practices.

Librarianship as a Business.

ERIC Educational Resources Information Center

Campbell, Sharon

2000-01-01

Discusses the need to think of public libraries as businesses rather than as functions of society. Topics include keeping old customers and attracting new ones; taxes; competition from the private sector; the use of technology; training people to use technology; mission statements; library services; and marketing. (LRW)

Ionospheric ion temperature forecasting in multiples of 27 days

NASA Astrophysics Data System (ADS)

Sojka, Jan J.; Schunk, Robert W.; Nicolls, Michael J.

2014-03-01

The ionospheric variability found at auroral locations is usually assumed to be unpredictable. The magnetosphere, which drives this ionospheric variability via storms and substorms, is at best only qualitatively describable. In this study we demonstrate that over a 3 year period, ionospheric variability observed from Poker Flat, Alaska, has, in fact, a high degree of long-term predictability. The observations used in this study are (a) the solar wind high speed stream velocity measured by the NASA Advanced Composition Explorer satellite, used to define the corotating interaction region (CIR), and (b) the ion temperature at 300 km altitude measured by the National Science Foundation Poker Flat Incoherent Scatter Radar over Poker Flat, Alaska. After determining a seasonal and diurnal climatology for the ion temperature, we show that the residual ion temperature heating events occur synchronously with CIR-geospace interactions. Furthermore, we demonstrate examples of ion temperature forecasting at 27, 54, and 81 days. A rudimentary operational forecasting scenario is described for forecasting recurrence 27 days ahead for the CIR-generated geomagnetic storms. These forecasts apply specifically to satellite tracking operations (thermospheric drag) and emergency HF-radio communications (ionospheric modifications) in the polar regions. The forecast is based on present-day solar and solar wind observations that can be used to uniquely identify the coronal hole and its CIR. From this CIR epoch, a 27 day forecast is then made.

Business as a Vocation: Catholic Social Teaching and Business Education

ERIC Educational Resources Information Center

Turkson, Peter K. A.

2012-01-01

Building on "Vocation of the Business Leader," the recently released document from the Pontifical Council for Justice and Peace, along with input from Catholic business and educational leaders from around the world, this essay examines five pillars on which a Catholic business school should build its mission: foundations; the purpose of…

Modeling the prediction of business intelligence system effectiveness.

PubMed

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

NASA Astrophysics Data System (ADS)

Dutton, John A.; James, Richard P.; Ross, Jeremy D.

2013-06-01

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.

International survey for good practices in forecasting uncertainty assessment and communication

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Piotte, Olivier

2014-05-01

Achieving technically sound flood forecasts is a crucial objective for forecasters but remains of poor use if the users do not understand properly their significance and do not use it properly in decision making. One usual way to precise the forecasts limitations is to communicate some information about their uncertainty. Uncertainty assessment and communication to stakeholders are thus important issues for operational flood forecasting services (FFS) but remain open fields for research. French FFS wants to publish graphical streamflow and level forecasts along with uncertainty assessment in near future on its website (available to the greater public). In order to choose the technical options best adapted to its operational context, it carried out a survey among more than 15 fellow institutions. Most of these are providing forecasts and warnings to civil protection officers while some were mostly working for hydroelectricity suppliers. A questionnaire has been prepared in order to standardize the analysis of the practices of the surveyed institutions. The survey was conducted by gathering information from technical reports or from the scientific literature, as well as 'interviews' driven by phone, email discussions or meetings. The questionnaire helped in the exploration of practices in uncertainty assessment, evaluation and communication. Attention was paid to the particular context within which every insitution works, in the analysis drawn from raw results. Results show that most services interviewed assess their forecasts uncertainty. However, practices can differ significantly from a country to another. Popular techniques are ensemble approaches. They allow to take into account several uncertainty sources. Statistical past forecasts analysis (such as the quantile regressions) are also commonly used. Contrary to what was expected, only few services emphasize the role of the forecaster (subjective assessment). Similar contrasts can be observed in uncertainty

The Chicago to Iowa City intercity passenger rail route : business plan.

DOT National Transportation Integrated Search

2011-03-21

Business Plan Highlights : -No Iowa General Fund or RIIF appropriations : -State/local partnership : -Funds operation for the first 10 years : -Local cash commitment to passenger rail : -Conservative and practical financial forecasts : -Three compone...

Seasonal forecasts of impact-relevant climate information indices developed as part of the EUPORIAS project

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas

2015-04-01

Climate information indices (CIIs) represent a way to communicate climate conditions to specific sectors and the public. As such, CIIs provide actionable information to stakeholders in an efficient way. Due to their non-linear nature, such CIIs can behave differently than the underlying variables, such as temperature. At the same time, CIIs do not involve impact models with different sources of uncertainties. As part of the EU project EUPORIAS (EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale) we have developed examples of seasonal forecasts of CIIs. We present forecasts and analyses of the skill of seasonal forecasts for CIIs that are relevant to a variety of economic sectors and a range of stakeholders: heating and cooling degree days as proxies for energy demand, various precipitation and drought-related measures relevant to agriculture and hydrology, a wild fire index, a climate-driven mortality index and wind-related indices tailored to renewable energy producers. Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skillful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables, forecasts of CIIs provide added value from a user perspective.

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

NASA Technical Reports Server (NTRS)

Chin, Mian; Ginoux, Paul; Lucchesi, Robert; Huebert, Barry; Weber, Rodney; Anderson, Tad; Masonis, Sarah; Blomquist, Byron; Bandy, Alan; Thornton, Donald

2003-01-01

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

Medical teleconferencing with high-definition video presentation on the 'usual' Internet.

PubMed

Obuchi, Toshiro; Shima, Hiroji; Iwasaki, Akinori

2013-06-01

Although medical teleconferences on advanced academic networks have been common (Telemed J E Health 15:112-117, 1; Asian J Endosc Surg 3:185-188, 2; Surg Today 41:1579-1581, 3; Telemedicine development center of Asia. http://www.aqua.med.kyushu-u.ac.jp/eg/index.html . Accessed 6 March 2013, 4), reports regarding 'usual' Internet teleconferences or tele-lectures employing a telecommunication system for business use are very rare. Medical teleconferences and tele-lectures on the Internet were held three times between our institutions and other institutions, using the 'HD Com' made by Panasonic (HD Com. http://panasonic.biz/com/visual/ . Accessed 6 March 2013, 5), which is a high-definition telecommunication system for business tele-meeting. All of our medical telecommunications were successfully completed without any troubles. This system allows for all kinds of presentations using personal computers to be made from each station, so that discussions with high-definition surgical video presentation, which has recently been developed, could be effortlessly established despite the distance between institutions. Unlike telecommunication using advanced academic networks, this system can run without any need for specific engineering support, on the usual Internet. Medical telecommunication employing this system is likely to become common among ordinary hospitals in the near future.

Use of ground-based wind profiles in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

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

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

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

Life cycle assessment of hybrid vehicles recycling: Comparison of three business lines of dismantling.

PubMed

Belboom, Sandra; Lewis, Grégory; Bareel, Pierre-François; Léonard, Angélique

2016-04-01

This paper undertakes an environmental evaluation of hybrid vehicles recycling, using industrial data from Comet Traitement SA in Belgium. Three business lines have been modelled and analysed. The first one is relative to the business as usual with a dismantling to recover batteries and engines followed by shredding and post shredding treatments. The second one considers, in addition, the removal of electronic control units (ECU) before shredding followed by same steps than in the first line and the last one is relative to the additional removal of big plastic parts before shredding and business as usual post shredding treatments. Results show non-significant environmental benefits when ECU or large parts of plastics are recovered before shredding. Improvements in terms of environmental benefits are lower than the uncertainty of the results. Indeed, the performing usual process for end-of-life vehicles (ELV) treatment reaches 97% of the ELV which is valorised in terms of metal and energy recoveries. Post shredding treatment units include metals, plastics and energy recovery of residues. Comet business as usual route for ELV valorisation is in accordance with the requirements of the European directive and recommendations for further improvement with dismantling of other parts (ECU or plastics) before shredding are non-relevant in this case. Copyright © 2016 Elsevier Ltd. All rights reserved.

In situ Carbon 13 and Oxygen 18 Ratios of Atmospheric CO2 from Cape Grim\\, Tasmania\\, Australia,1982-1993 (DB1014)

DOE Data Explorer

Francey, R. J. [CSIRO Division of Atmospheric Research, Mordialloc, Victoria, Australia; Allison, C. E. [CSIRO Division of Atmospheric Research, Mordialloc, Victoria, Australia

1998-01-01

Since 1982, a continuous program of sampling atmospheric CO2 to determine stable isotope ratios has been maintained at the Australian Baseline Air Pollution Station, Cape Grim, Tasmania (40°, 40'56"S, 144°, 41'18"E). The process of in situ extraction of CO2 from air, the preponderance of samples collected in conditions of strong wind from the marine boundary layer of the Southern Ocean, and the determination of all isotope ratios relative to a common high purity CO2 reference gas with isotopic δ13C close to atmospheric values, are a unique combination of factors with respect to obtaining a globally representative signal from a surface site. Air samples are collected during baseline condition episodes at a frequency of around one sample per week. Baseline conditions are characterized by wind direction in the sector 190°-280°, condensation nucleus concentration below 600 per cm-3, and steady continuous CO2 concentrations (variation ± 0.2 ppmv per hour). A vacuum pump draws air from either the 10 m or 70 m intakes and sampling alternates between the two intakes. The air from the intake is dried with a trap immersed in an alcohol bath at about -80°C. Mass spectrometer analyses for δ13C and δ18O are carried out by CSIRO's Division of Atmospheric Research in Aspendale, usually one to three weeks following collection. This record is possibly the most accurate representation of global atmospheric 13C behavior over the last decade and may be used to partition the uptake of fossil-fuel carbon emissions between ocean and terrestrial plant reservoirs. Using these data, Francey et al. (1995) observed a gradual decrease in δ13C from 1982 to 1993, but with a pronounced flattening from 1988 to 1990; a trend that appears to involve the terrestrial carbon cycle.

Strategic Forecast: U.S. Navy beyond the Year 2000. Phase 3. The Role of the Navy as an Instrument of National Policy.

DTIC Science & Technology

1987-09-01

a " business as usual" approach, as in the Vietnam War. The U.S. mobilization base, both in manpower assets and in industrial capabilities, is less...Europe. As if this were not sufficient to terrorize .11 the West European poeple , the USSR was ruled by a feared and mysterious autocrat, Joseph Stalin... solutions , such as the cession of some territory, or the S!. recognition of mutual spheres of influence or the good will or diplomatic skills of particular

Exploring What Determines the Use of Forecasts of Varying Time Periods in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

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

2016-12-01

Weather and climate forecasts are promoted as ways to improve water management, especially in the face of changing environmental conditions. However, studies indicate many stakeholders who may benefit from such information do not use it. This study sought to better understand which personal factors (e.g., trust in forecast sources, perceptions of accuracy) were important determinants of the use of 4-day, 3-month, and 12-month rainfall forecasts by stakeholders in water management-related sectors in the seasonally dry province of Guanacaste, Costa Rica. From August to October 2015, we surveyed 87 stakeholders from a mix of government agencies, local water committees, large farms, tourist businesses, environmental NGO's, and the public. The result of an exploratory factor analysis suggests that trust in "informal" forecast sources (traditional methods, family advice) and in "formal" sources (government, university and private company science) are independent of each other. The result of logistic regression analyses suggest that 1) greater understanding of forecasts is associated with a greater probability of 4-day and 3-month forecast use, but not 12-month forecast use, 2) a greater probability of 3-month forecast use is associated with a lower level of trust in "informal" sources, and 3), feeling less secure about water resources, and regularly using many sources of information (and specifically formal meetings and reports) are each associated with a greater probability of using 12-month forecasts. While limited by the sample size, and affected by the factoring method and regression model assumptions, these results do appear to suggest that while forecasts of all times scales are used to some extent, local decision makers' decisions to use 4-day and 3-month forecasts appear to be more intrinsically motivated (based on their level of understanding and trust) and the use of 12-month forecasts seems to be more motivated by a sense of requirement or mandate.

Injury rate as an indicator of business success.

PubMed

Holizki, Theresa; Nelson, Larry; McDonald, Rose

2006-01-01

Health and safety professionals and organizations have often suggested that promoting and improving health and safety in the workplace will improve business success. We conducted a study of all new small businesses that registered with the Workers' Compensation Board of British Columbia (WCB of BC) in the years 1993, 1995, 1996 and 1997, assessing their injury rate in the first 5 complete years of business. The data set represents 53,913 new businesses and 19,332 claims. Businesses were grouped by the number of years between registering for WCB coverage and termination of coverage. Injury rates were determined for each calendar year for each industry sector as injuries per 100 person-years, based on payroll information provided by the businesses. Across all industries, businesses that failed between 1 and 2 yr of start-up had an average injury rate of 9.71 while businesses that survived more than 5 yr had an average injury rate of only 3.89 in their first year of business (p<0.000001). The WCB of BC demonstrated a statistical correlation between health and safety in the workplace and the survival of a small business.

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.

Therapy recommendation "act as usual" in patients with whiplash injuries QTF I°.

PubMed

Dehner, Christoph; Kraus, Michael; Schöll, Hendrik; Schneider, Florian; Richter, Peter; Kramer, Michael

2012-08-20

Up to now no therapy study has used the classification system of the Quebec Task Force (QTF) to differentiate between patients with (QTF II°) and without functional disorders (QTF I°). This differentiation seems meaningful, as this difference may be relevant for the correct treatment planning. In this context the effect of the therapy recommendation "act as usual" has been evaluated in a homogeneous patient collective with whiplash injuries QTF I°. 470 patients with acute whiplash injuries had been catched in this study and classified according to the QTF. 359 patients (76.4%) with QTF I° injuries could be identified. Out of that 162 patients were enrolled to the study and received the therapy recommendation "act as usual" and the adapted pain treatment with non-steroidal anti-inflammatory drugs (NSAID). After six months the outcome was evaluated by phone. After injury the median pain score assessed by a visual analogue scale (VAS) was 5.4 (min = 3.3; max = 8.5). After six months 5 of the 162 patients complained intermittent pain symptoms (VAS values < 2). This is consistent with a chronification rate of 3.1%. After injury, the median pain disability index (PDI) was 3.9 (min = 1.9; max = 7.7). After six months 3 of the 162 patients stated persisting disability during sporting and physical activities (VAS values < 1). The therapy recommendation "act as usual" in combination with an adapted pain treatment is sufficient. Usually patients with whiplash injuries QTF I° do not need physical therapy. An escalation of therapy measures should be reserved to patients with complicated healing processes.

How To Set Up Your Own Small Business. Volumes I-II and Overhead Transparencies.

ERIC Educational Resources Information Center

Fallek, Max

This two-volume textbook and collection of overhead transparency masters is intended for use in a course in setting up a small business. The following topics are covered in the first volume: getting off to a good start, doing market research, forecasting sales, financing a small business, understanding the different legal needs of different types…

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

NASA Astrophysics Data System (ADS)

Shafiee-Jood, M.; Cai, X.

2017-12-01

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

Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

NASA Astrophysics Data System (ADS)

Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

2015-04-01

Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate-term aftershock activity as soon as possible is important. The epidemic-type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher-order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug-in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate-term forecasting, we show that the Bayesian forecasting performs better than the plug-in forecasting on average in terms of the log-likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg-Richter (G-R) formula. We show that the NP forecast performs better than the G-R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate-term assessment of aftershock probabilities.

Biopharma business models in Canada.

PubMed

March-Chordà, I; Yagüe-Perales, R M

2011-08-01

This article provides new insights into the different strategy paths or business models currently being implemented by Canadian biopharma companies. Through a case-study methodology, seven biopharma companies pertaining to three business models were analyzed, leading to a broad set of results emerging from the following areas: activity, business model and strategy; management and human resources; and R&D, technology and innovation strategy. The three business models represented were: model 1 (conventional biotech oriented to new drug development, radical innovation and search for discoveries); model 2 (development of a technology platform, usually in proteomics and bioinformatics); and model 3 (incremental innovation, with shorter and less risky development timelines). Copyright © 2011 Elsevier Ltd. All rights reserved.

Multifractal analysis of Moroccan family business stock returns

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-11-01

In this paper, long-range temporal correlations at different scales in Moroccan family business stock returns are investigated. For comparison purpose, presence of multifractality is also investigated in Casablanca Stock Exchange (CSE) major indices: MASI which is the all shares index and MADEX which is the index of most liquid shares. It is found that return series of both family business companies and major stock market indices show strong evidence of multifractality. In particular, empirical results reveal that short (long) fluctuations in family business stock returns are less (more) persistent (anti-persistent) than short fluctuations in market indices. In addition, both serial correlation and distribution characteristics significantly influence the strength of the multifractal spectrums of CSE and family business stocks returns. Furthermore, results from multifractal spectrum analysis suggest that family business stocks are less risky. Thus, such differences in price dynamics could be exploited by investors and forecasters in active portfolio management.

Averaging business cycles vs. myopia: Do we need a long term vision when developing IRP?

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

McDonald, C.; Gupta, P.C.

1995-05-01

Utility demand forecasting is inherently imprecise due to the number of uncertainties resulting from business cycles, policy making, technology breakthroughs, national and international political upheavals and the limitations of the forecasting tools. This implies that revisions based primarily on recent experience could lead to unstable forecasts. Moreover, new planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning decisions.

The improved business valuation model for RFID company based on the community mining method.

PubMed

Li, Shugang; Yu, Zhaoxu

2017-01-01

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company's net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies.

The improved business valuation model for RFID company based on the community mining method

PubMed Central

Li, Shugang; Yu, Zhaoxu

2017-01-01

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company’s net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies. PMID:28459815

Forecast Verification: Identification of small changes in weather forecasting skill

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Integrating predictive information into an agro-economic model to guide agricultural planning

NASA Astrophysics Data System (ADS)

Block, Paul; Zhang, Ying; You, Liangzhi

2017-04-01

Seasonal climate forecasts can inform long-range planning, including water resources utilization and allocation, however quantifying the value of this information on the economy is often challenging. For rain-fed farmers, skillful season-ahead predictions may lead to superior planning, as compared to business as usual strategies, resulting in additional benefits or reduced losses. In this study, regional-level probabilistic precipitation forecasts of the major rainy season in Ethiopia are fed into an agro-economic model, adapted from the International Food Policy Research Institute, to evaluate economic outcomes (GDP, poverty rates, etc.) as compared with a no-forecast approach. Based on forecasted conditions, farmers can select various actions: adjusting crop area and crop type, purchasing drought resistant seed, or applying additional fertilizer. Preliminary results favor the forecast-based approach, particularly through crop area reallocation.

32 CFR 644.509 - Status as small business.

Code of Federal Regulations, 2010 CFR

2010-07-01

....509 Status as small business. (a) Definition. Each invitation for bids for the sale of timber with an estimated value of $2,000 or more will contain a definition of small business and provision for self... set-asides. The definition of small business provided in Figure 11-20 in ER 405-1-12 omits portions of...

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

NASA Astrophysics Data System (ADS)

VandenBoogart, Lance M.

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

Women as a business imperative.

PubMed

Schwartz, F N

1992-01-01

In 1989, Felice N. Schwartz's HBR article "Management Women and the New Facts of Life" generated a huge debate over the rules established by corporations in their handling of women executives. Now in "Women as a Business Imperative," Schwartz follows up with practical insights about the costs companies incur in passing over qualified businesswomen. In the form of a memo to a fictional CEO, Schwartz describes how the atmosphere within most companies is corrosive to women and must change. Preconceptions harbored by male senior managers about women are so deeply ingrained that many men are not even aware of them. Yet senior managers must help women advance. Those companies that accept their responsibility to make radical change--both in women's treatment and in family support--can improve their bottom lines enormously. Treating women as a business imperative is the equivalent of creating a unique R&D product for which there is great demand. Most companies ignore child care and other family concerns. Many companies hire women to ensure mere adequacy and avoid litigation. Women's ambitions and energies are stifled by such businesses at the same time that women have demonstrated their competence and potential in the best business schools. High turnover results. However, the restraints that now hold women back can be loosened easily. CEOs and other senior managers must support their female employees by (1) acknowledging the fundamental difference between women and men--the biological fact of maternity; (2) allowing flexibility for women and men who need it; (3) providing training that takes advantage of women's leadership potential; and (4) eliminating the corrosive atmosphere and the barriers that exist for women in the workplace.

Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error.

PubMed

Joslyn, Susan L; LeClerc, Jared E

2012-03-01

Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather warning system is used. The work reported here tested the relative benefits of several forecast formats, comparing decisions made with and without uncertainty forecasts. In three experiments, participants assumed the role of a manager of a road maintenance company in charge of deciding whether to pay to salt the roads and avoid a potential penalty associated with icy conditions. Participants used overnight low temperature forecasts accompanied in some conditions by uncertainty estimates and in others by decision advice comparable to categorical warnings. Results suggested that uncertainty information improved decision quality overall and increased trust in the forecast. Participants with uncertainty forecasts took appropriate precautionary action and withheld unnecessary action more often than did participants using deterministic forecasts. When error in the forecast increased, participants with conventional forecasts were reluctant to act. However, this effect was attenuated by uncertainty forecasts. Providing categorical decision advice alone did not improve decisions. However, combining decision advice with uncertainty estimates resulted in the best performance overall. The results reported here have important implications for the development of forecast formats to increase compliance with severe weather warnings as well as other domains in which one must act in the face of uncertainty. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

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

Business as a Site of Language Contact.

ERIC Educational Resources Information Center

Harris, Sandra; Bargiela-Chiappini, Francesca

2003-01-01

Discusses the field of language for business. Argues for redressing the balance of research into business as a site of language contact in favor of less well-represented languages and cultures through indigenous discourse studies, and notes the increasing frequency and importance of work involving Asian languages. (Author/VWL)

Hospital output forecasts and the cost of empty hospital beds.

PubMed Central

Pauly, M V; Wilson, P

1986-01-01

This article investigates the cost incurred when hospitals have different levels of beds to treat a given number of patients. The cost of hospital care is affected by both the forecasted level of admissions and the actual number of admissions. When the relationship between forecasted and actual admissions is held constant, it is found that an empty hospital bed at a typical hospital in Michigan has a relatively low cost, about 13 percent or less of the cost of an occupied bed. However, empty beds in large hospitals do add significantly to cost. If hospital beds are closed, whether by closing beds at hospitals which remain in business or by closing entire hospitals, cost savings are estimated to be small. PMID:3759473

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Fishing Forecasts

NASA Technical Reports Server (NTRS)

1988-01-01

ROFFS stands for Roffer's Ocean Fishing Forecasting Service, Inc. Roffer combines satellite and computer technology with oceanographic information from several sources to produce frequently updated charts sometimes as often as 30 times a day showing clues to the location of marlin, sailfish, tuna, swordfish and a variety of other types. Also provides customized forecasts for racing boats and the shipping industry along with seasonal forecasts that allow the marine industry to formulate fishing strategies based on foreknowledge of the arrival and departure times of different fish. Roffs service exemplifies the potential for benefits to marine industries from satellite observations. Most notable results are reduced search time and substantial fuel savings.

Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

DOE PAGES

Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas; ...

2016-11-14

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less

Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

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

Moran, Kelly Renee; Fairchild, Geoffrey; Generous, Nicholas

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection andmore » Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.« less

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast

PubMed Central

Moran, Kelly R.; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y.

2016-01-01

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. PMID:28830111

The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative

NASA Astrophysics Data System (ADS)

Shaleh, W.; Rasim; Wahyudin

2018-01-01

Problems at PT. XYZ currently only rely on manual bookkeeping, then the cost of production will swell and all investments invested to be less to predict sales and inventory of goods. If the inventory prediction of goods is to large, then the cost of production will swell and all investments invested to be less efficient. Vice versa, if the inventory prediction is too small it will impact on consumers, so that consumers are forced to wait for the desired product. Therefore, in this era of globalization, the development of computer technology has become a very important part in every business plan. Almost of all companies, both large and small, use computer technology. By utilizing computer technology, people can make time in solving complex business problems. Computer technology for companies has become an indispensable activity to provide enhancements to the business services they manage but systems and technologies are not limited to the distribution model and data processing but the existing system must be able to analyze the possibilities of future company capabilities. Therefore, the company must be able to forecast conditions and circumstances, either from inventory of goods, force, or profits to be obtained. To forecast it, the data of total sales from December 2014 to December 2016 will be calculated by using the method of Holt Winters, which is the method of time series prediction (Multiplicative Seasonal Method) it is seasonal data that has increased and decreased, also has 4 equations i.e. Single Smoothing, Trending Smoothing, Seasonal Smoothing and Forecasting. From the results of research conducted, error value in the form of MAPE is below 1%, so it can be concluded that forecasting with the method of Holt Winter Multiplicative.

Air Pollution Forecasts: An Overview

PubMed Central

Bai, Lu; Wang, Jianzhou; Lu, Haiyan

2018-01-01

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

Air Pollution Forecasts: An Overview.

PubMed

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

2018-04-17

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Physician Assistants and Nurse Practitioners as a Usual Source of Care

ERIC Educational Resources Information Center

Everett, Christine M.; Schumacher, Jessica R.; Wright, Alexandra; Smith, Maureen A.

2009-01-01

Purpose: To identify characteristics and outcomes of patients who use physician assistants and nurse practitioners (PA/NPs) as a usual source of care. Methods: Cross sectional analysis using the telephone and mail surveys of the Wisconsin Longitudinal Study (WLS), a prospective cohort study of Wisconsin high school graduates and selected siblings…

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

PubMed Central

Brauer, Michael; Henderson, Sarah B.

2013-01-01

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

Improved Anvil Forecasting

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2000-01-01

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

Michael Novak's "Business as a Calling" as a Vehicle for Addressing Ethical and Policy Concerns in a Business Law Course

ERIC Educational Resources Information Center

Murphy, Tonia Hap

2008-01-01

This article describes the author's experience of incorporating Michael Novak's "Business as a Calling: Work and the Examined Life" into a Business Law course. The author views it as a positive addition to the course, one that may be of interest to her colleagues at other institutions. Accordingly, after an overview of Novak's analysis in…

Know-How in Postwar Business and Law.

PubMed

O'Reagan, Douglas

In the mid-twentieth century, businesses around the world began to see technical know-how as one of the most important assets they could possess. While their exact definitions of know-how varied (usually centering on employees' tacit knowledge; accumulated, minor innovations rather than just patentable inventions; and tailoring to local conditions), the rapidly growing perception that it was invaluable led to widespread know-how licensing. As businesses embraced it, legal scholars and business lawyers during the 1950s through the 1970s scrambled to clarify legal bases for intellectual property protections for know-how. In the 1970s Supreme Court decisions undermined this effort, and a consortium of legal organizations turned instead to lobbying for statutory protection for the related, narrower category of "trade secrets." Despite the rise and relative decline of know-how in American business and law, interest in the term spread to other languages and legal systems, and the repercussions of these shifting understandings of technology transfer remain with us today.

48 CFR 19.1303 - Status as a HUBZone small business concern.

Code of Federal Regulations, 2011 CFR

2011-10-01

... REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Historically Underutilized Business Zone (HUBZone) Program 19.1303 Status as a HUBZone small business concern. (a) Status as a HUBZone small business concern is determined by the Small Business Administration (SBA) in accordance with 13 CFR part 126. (b) If...

48 CFR 19.1303 - Status as a HUBZone small business concern.

Code of Federal Regulations, 2013 CFR

2013-10-01

... REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Historically Underutilized Business Zone (HUBZone) Program 19.1303 Status as a HUBZone small business concern. (a) Status as a HUBZone small business concern is determined by the Small Business Administration (SBA) in accordance with 13 CFR part 126. (b) If...

48 CFR 19.1303 - Status as a HUBZone small business concern.

Code of Federal Regulations, 2012 CFR

2012-10-01

... REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Historically Underutilized Business Zone (HUBZone) Program 19.1303 Status as a HUBZone small business concern. (a) Status as a HUBZone small business concern is determined by the Small Business Administration (SBA) in accordance with 13 CFR part 126. (b) If...

48 CFR 19.1303 - Status as a HUBZone small business concern.

Code of Federal Regulations, 2014 CFR

2014-10-01

... REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Historically Underutilized Business Zone (HUBZone) Program 19.1303 Status as a HUBZone small business concern. (a) Status as a HUBZone small business concern is determined by the Small Business Administration (SBA) in accordance with 13 CFR part 126. (b) If...

Data-Driven Disease Forecasting

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

Generous, Nicholas

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

Forecasting eruption size: what we know, what we don't know

NASA Astrophysics Data System (ADS)

Papale, Paolo

2017-04-01

Any eruption forecast includes an evaluation of the expected size of the forthcoming eruption, usually expressed as the probability associated to given size classes. Such evaluation is mostly based on the previous volcanic history at the specific volcano, or it is referred to a broader class of volcanoes constituting "analogues" of the one under specific consideration. In any case, use of knowledge from past eruptions implies considering the completeness of the reference catalogue, and most importantly, the existence of systematic biases in the catalogue, that may affect probability estimates and translate into biased volcanic hazard forecasts. An analysis of existing catalogues, with major reference to the catalogue from the Smithsonian Global Volcanism Program, suggests that systematic biases largely dominate at global, regional and local scale: volcanic histories reconstructed at individual volcanoes, often used as a reference for volcanic hazard forecasts, are the result of systematic loss of information with time and poor sample representativeness. That situation strictly requires the use of techniques to complete existing catalogues, as well as careful consideration of the uncertainties deriving from inadequate knowledge and model-dependent data elaboration. A reconstructed global eruption size distribution, obtained by merging information from different existing catalogues, shows a mode in the VEI 1-2 range, <0.1% incidence of eruptions with VEI 7 or larger, and substantial uncertainties associated with individual VEI frequencies. Even larger uncertainties are expected to derive from application to individual volcanoes or classes of analogue volcanoes, suggesting large to very large uncertainties associated to volcanic hazard forecasts virtually at any individual volcano worldwide.

EnrollForecast for Excel: K-12 Enrollment Forecasting Program. Software & User's Guide. [Computer Diskette].

ERIC Educational Resources Information Center

Smith, Curtis A.

"EnrollForecast for Excel" will generate a 5-year forecast of K-12 student enrollment. It will also work for any combination of grades between kindergarten and twelth. The forecasts can be printed as either a table or a graph. The user must provide birth history (only if forecasting kindergarten) and enrollment history information. The user also…

An overview of health forecasting.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

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

Forecasting of wet snow avalanche activity: Proof of concept and operational implementation

NASA Astrophysics Data System (ADS)

Gobiet, Andreas; Jöbstl, Lisa; Rieder, Hannes; Bellaire, Sascha; Mitterer, Christoph

2017-04-01

State-of-the-art tools for the operational assessment of avalanche danger include field observations, recordings from automatic weather stations, meteorological analyses and forecasts, and recently also indices derived from snowpack models. In particular, an index for identifying the onset of wet-snow avalanche cycles (LWCindex), has been demonstrated to be useful. However, its value for operational avalanche forecasting is currently limited, since detailed, physically based snowpack models are usually driven by meteorological data from automatic weather stations only and have therefore no prognostic ability. Since avalanche risk management heavily relies on timely information and early warnings, many avalanche services in Europe nowadays start issuing forecasts for the following days, instead of the traditional assessment of the current avalanche danger. In this context, the prognostic operation of detailed snowpack models has recently been objective of extensive research. In this study a new, observationally constrained setup for forecasting the onset of wet-snow avalanche cycles with the detailed snow cover model SNOWPACK is presented and evaluated. Based on data from weather stations and different numerical weather prediction models, we demonstrate that forecasts of the LWCindex as indicator for wet-snow avalanche cycles can be useful for operational warning services, but is so far not reliable enough to be used as single warning tool without considering other factors. Therefore, further development currently focuses on the improvement of the forecasts by applying ensemble techniques and suitable post processing approaches to the output of numerical weather prediction models. In parallel, the prognostic meteo-snow model chain is operationally used by two regional avalanche warning services in Austria since winter 2016/2017 for the first time. Experiences from the first operational season and first results from current model developments will be reported.

Sub-Seasonal Climate Forecast Rodeo

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Adaptation of Mesoscale Weather Models to Local Forecasting

NASA Technical Reports Server (NTRS)

Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.

2003-01-01

objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

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

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

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

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

PubMed

Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y

2016-12-01

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by

Rethinking Business Education as a Profession: Implications for Catholic Universities

ERIC Educational Resources Information Center

Sullivan, William M.

2013-01-01

Professional education importantly shapes the way future professionals understand their work and their identity as members of their professional field. Undergraduate business education does this by giving students an understanding of the nature and functions of business as well as what they may hope for from a business career, along with the…

Forecast communication through the newspaper Part 1: Framing the forecaster

NASA Astrophysics Data System (ADS)

Harris, Andrew J. L.

2015-04-01

This review is split into two parts both of which address issues of forecast communication of an environmental disaster through the newspaper during a period of crisis. The first part explores the process by which information passes from the scientist or forecaster, through the media filter, to the public. As part of this filter preference, omission, selection of data, source, quote and story, as well as placement of the same information within an individual piece or within the newspaper itself, can serve to distort the message. The result is the introduction of bias and slant—that is, the message becomes distorted so as to favor one side of the argument against another as it passes through the filter. Bias can be used to support spin or agenda setting, so that a particular emphasis becomes placed on the story which exerts an influence on the reader's judgment. The net result of the filter components is either a negative (contrary) or positive (supportive) frame. Tabloidization of the news has also resulted in the use of strong, evocative, exaggerated words, headlines and images to support a frame. I illustrate these various elements of the media filter using coverage of the air space closure due to the April 2010 eruption of Eyjafjallajökull (Iceland). Using the British press coverage of this event it is not difficult to find examples of all media filter elements, application of which resulted in bias against the forecast and forecaster. These actors then became named and blamed. Within this logic, it becomes only too easy for forecasters and scientists to be framed in a negative way through blame culture. The result is that forecast is framed in such a way so as to cause the forecaster to be blamed for all losses associated with the loss-causing event. Within the social amplification of risk framework (SARF), this can amplify a negative impression of the risk, the event and the response. However, actions can be taken to avoid such an outcome. These actions

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful

Regionalization of post-processed ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-05-01

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

Teaching business ethics to professional engineers.

PubMed

Sauser, William I

2004-04-01

Without question "business ethics" is one of the hot topics of the day. Over the past months we have seen business after business charged with improper practices that violate commonly-accepted ethical norms. This has led to a loss of confidence in corporate management, and has had severe economic consequences. From many quarters business educators have heard the call to put more emphasis on ethical practices in their business courses and curricula. Engineering educators are also heeding this call, since the practice of engineering usually involves working for (or leading) a business and/or engaging in business transactions. In the summer of 2002, Auburn University's Engineering Professional Development program made the decision to produce--based on the author's Executive MBA course in Business Ethics--a distance-delivered continuing education program for professional engineers and surveyors. Participants across the USA now may use the course to satisfy continuing education requirements with respect to professional licensing and certification. This paper outlines the purpose and content of the course and describes its production, distribution, application, and evaluation.

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

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

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

Sources of information for tsunami forecasting in New Zealand

NASA Astrophysics Data System (ADS)

Barberopoulou, A.; Ristau, J. P.; D'Anastasio, E.; Wang, X.

2013-12-01

Tsunami science has evolved considerably in the last two decades due to technological advancements which also helped push for better numerical modelling of the tsunami phases (generation to inundation). The deployment of DART buoys has also been a considerable milestone in tsunami forecasting. Tsunami forecasting is one of the parts that tsunami modelling feeds into and is related to response, preparedness and planning. Usually tsunami forecasting refers to short-term forecasting that takes place in real-time after a tsunami has or appears to have been generated. In this report we refer to all types of forecasting (short-term or long-term) related to work in advance of a tsunami impacting a coastline that would help in response, planning or preparedness. We look at the standard types of data (seismic, GPS, water level) that are available in New Zealand for tsunami forecasting, how they are currently being used, other ways to use these data and provide recommendations for better utilisation. The main findings are: -Current investigations of the use of seismic parameters quickly obtained after an earthquake, have potential to provide critical information about the tsunamigenic potential of earthquakes. Further analysis of the most promising methods should be undertaken to determine a path to full implementation. -Network communication of the largest part of the GPS network is not currently at a stage that can provide sufficient data early enough for tsunami warning. It is believed that it has potential, but changes including data transmission improvements may have to happen before real-time processing oriented to tsunami early warning is implemented on the data that is currently provided. -Tide gauge data is currently under-utilised for tsunami forecasting. Spectral analysis, modal analysis based on identified modes and arrival times extracted from the records can be useful in forecasting. -The current study is by no means exhaustive of the ways the different types

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

The United Kingdom is an area vulnerable to damage due to storm surges, particularly the East Coast which suffered losses estimated at over £1 billion during the North Sea surge event of the 5th and 6th December 2013. Accurate forecasting of storm surge events for this region is crucial to enable government agencies to assess the risk of overtopping of coastal defences so they can respond appropriately, minimising risk to life and infrastructure. There has been an operational storm surge forecast service for this region since 1978, using a numerical model developed by the National Oceanography Centre (NOC) and run at the UK Met Office. This is also implemented as part of an ensemble prediction system, using perturbed atmospheric forcing to produce an ensemble surge forecast. In order to ensure efficient use of future supercomputer developments and to create synergy with existing operational coastal ocean models the Met Office and NOC have begun a joint project transitioning the storm surge forecast system from the current CS3X code base to a configuration based on the Nucleus for European Modelling of the Ocean (NEMO). This work involves both adapting NEMO to add functionality, such as allowing the drying out of ocean cells and changes allowing NEMO to run efficiently as a two-dimensional, barotropic model. As the ensemble surge forecast system is run with 12 members 4 times a day computational efficiency is of high importance. Upon completion this project will enable interesting scientific comparisons to be made between a NEMO based surge model and the full three-dimensional baroclinic NEMO based models currently run within the Met Office, facilitating assessment of the impact of baroclinic processes, and vertical resolution on sea surface height forecasts. Moving to a NEMO code base will also allow many future developments to be more easily used within the storm surge model due to the wide range of options which currently exist within NEMO or are planned for

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

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

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

NASA Astrophysics Data System (ADS)

Kim, Whan-Seon

2013-06-01

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.

Medium Range Forecasts Representation (and Long Range Forecasts?)

NASA Astrophysics Data System (ADS)

Vincendon, J.-C.

2009-09-01

The progress of the numerical forecasts urges us to interest us in more and more distant ranges. We thus supply more and more forecasts with term of some days. Nevertheless, precautions of use are necessary to give the most reliable and the most relevant possible information. Available in a TV bulletin or on quite other support (Internet, mobile phone), the interpretation and the representation of a medium range forecast (5 - 15 days) must be different from those of a short range forecast. Indeed, the "foresee-ability” of a meteorological phenomenon decreases gradually in the course of the ranges, it decreases all the more quickly that the phenomenon is of small scale. So, at the end of some days, the probability character of a forecast becomes very widely dominating. That is why in Meteo-France the forecasts of D+4 to D+7 are accompanied with a confidence index since around ten years. It is a figure between 1 and 5: the more we approach 5, the more the confidence in the supplied forecast is good. In the practice, an indication is supplied for period D+4 / D+5, the other one for period D+6 / D+7, every day being able to benefit from a different forecast, that is be represented in a independent way. We thus supply a global tendency over 24 hours with less and less precise symbols as the range goes away. Concrete examples will be presented. From now on two years, we also publish forecasts to D+8 / J+9, accompanied with a sign of confidence (" good reliability " or " to confirm "). These two days are grouped together on a single map because for us, the described tendency to this term is relevant on a duration about 48 hours with a spatial scale slightly superior to the synoptic scale. So, we avoid producing more than two zones of types of weather over France and we content with giving an evolution for the temperatures (still, in increase or in decline). Newspapers began to publish this information, it should soon be the case of televisions. It is particularly

The Forecast Interpretation Tool—a Monte Carlo technique for blending climatic distributions with probabilistic forecasts

USGS Publications Warehouse

Husak, Gregory J.; Michaelsen, Joel; Kyriakidis, P.; Verdin, James P.; Funk, Chris; Galu, Gideon

2011-01-01

Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.

Business/Marketing Education. Business Analysis/Business Computer Applications.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Div. of Occupational Education Programs.

This document contains 12 modules: 4 on business analysis and 8 on business computer applications. The business analysis modules are as follows: (1) the framework of business; (2) universal activities of business; (3) selected business subsystems; and (4) your place in business. Computer applications modules are on the following topics: (1)…

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters

NASA Technical Reports Server (NTRS)

Hesse, Michael

2009-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.

Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

NASA Astrophysics Data System (ADS)

Danhelka, Jan; Vlasak, Tomas

2010-05-01

Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Forecasting Skill

DTIC Science & Technology

1981-01-01

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

CRM as a General Practice of Every Business Organization

NASA Astrophysics Data System (ADS)

Ahmed, TS Ezaz; Prabhakar, D.

2012-11-01

Every business unit emphasizes on spurting a long term relationship with customers to nurture its stability in todayís blooming market. Customerís expectations are now not only limited to get best products and services, they also need a face-to-face business in which they want to receive exactly what they demand and in a quick time. Customer Relationship Management is an upright concept or strategy to solidify relations with customers and at the same time reducing cost and enhancing productivity and profitability in business. An ideal CRM system is a centralized collection all data sources under an organization and provides an atomistic real time vision of customer information. A CRM system is vast and significant, but it be can implemented for small business, as well as large enterprises also as the main goal is to assist the customers efficiently.

Overview of Hydrometeorologic Forecasting Procedures at BC Hydro

NASA Astrophysics Data System (ADS)

McCollor, D.

2004-12-01

Energy utility companies must balance production from limited sources with increasing demand from industrial, business, and residential consumers. The utility planning process requires a balanced, efficient, and effective distribution of energy from source to consumer. Therefore utility planners must consider the impact of weather on energy production and consumption. Hydro-electric companies should be particularly tuned to weather because their source of energy is water, and water supply depends on precipitation. BC Hydro operates as the largest hydro-electric company in western Canada, managing over 30 reservoirs within the province of British Columbia, and generating electricity for 1.6 million people. BC Hydro relies on weather forecasts of watershed precipitation and temperature to drive hydrologic reservoir inflow models and of urban temperatures to meet energy demand requirements. Operations and planning specialists in the company rely on current, value-added weather forecasts for extreme high-inflow events, daily reservoir operations planning, and long-term water resource management. Weather plays a dominant role for BC Hydro financial planners in terms of sensitive economic responses. For example, a two percent change in hydropower generation, due in large part to annual precipitation patterns, results in an annual net change of \\50 million in earnings. A five percent change in temperature produces a \\5 million change in yearly earnings. On a daily basis, significant precipitation events or temperature extremes involve potential profit/loss decisions in the tens of thousands of dollars worth of power generation. These factors are in addition to environmental and societal costs that must be considered equally as part of a triple bottom line reporting structure. BC Hydro water resource managers require improved meteorological information from recent advancements in numerical weather prediction. At BC Hydro, methods of providing meteorological forecast data

32 CFR 644.509 - Status as small business.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 4 2014-07-01 2013-07-01 true Status as small business. 644.509 Section 644.509 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) REAL PROPERTY REAL... estimated value of $2,000 or more will contain a definition of small business and provision for self...

32 CFR 644.509 - Status as small business.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 4 2012-07-01 2011-07-01 true Status as small business. 644.509 Section 644.509 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) REAL PROPERTY REAL... estimated value of $2,000 or more will contain a definition of small business and provision for self...

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Weather assessment and forecasting

NASA Technical Reports Server (NTRS)

1977-01-01

Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted

Business as Usual or Brave New World? A College President's Perspective.

ERIC Educational Resources Information Center

Keohane, Nannerl O.

1986-01-01

The Sloan Foundation's New Liberal Arts Program aims to make a fundamental transformation in the liberal arts curriculum, by infusing applied mathematics and technological literacy. The program is examined by the president of Wellesley College in the context of current philosophical and practical constraints in higher education. (MSE)

Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

NASA Astrophysics Data System (ADS)

Manukalo, V.

2012-12-01

Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of

A new road map for healthcare business success.

PubMed

Williams, Jeni

2011-05-01

Action steps hospitals should take to prepare their organizations for a changing business development environment include: Developing a comprehensive forecast of the ways in which reform and market forces will affect patient volumes and service line demand. Aligning with physicians and other care entities in a tightly integrated way. Heightening transparency related to quality and cost. Investing in marketing and social media to strengthen the organization's market position.

Business Angels - A Subspecies of the homo oeconomicus ludens.

PubMed

Hüsler, Laurenz; Platzer, Erich M

2014-12-01

Business Angels invest in start-up companies in their early stage. This type of investor usually has a good knowledge of the start-up's industry sector, and in addition to the funds he invests, his management experience and his network can be useful for start-ups. Business Angel involvement has shown to improve the success rate and the profitability of start-ups. The article depicts the relationship between entrepreneurs and Business Angels in four case examples.

Business Success and Its Factors as Perceived by Business School Students

ERIC Educational Resources Information Center

Soboleva, N. E.

2012-01-01

The author presents a typology of students in business education programs, and analyzes the characteristics of students' perceptions of business success that may result from their training. The factors that students try to maximize are a mix of financial, personal, and life-style issues. (Contains 3 tables.) [This article was translated by Kim…

Entrepreneurial Creativity as a Convergent Basis for Teaching Business Communication

ERIC Educational Resources Information Center

Grenci, Richard T.

2012-01-01

Of the "21st Century" business skills of communication, collaboration, creativity, and critical thinking, creativity arguably receives among the least explicit attention in traditional business core curricula. With that in mind, the context of entrepreneurial creativity is put forth as a basis for teaching business communication. By…

Forecasting deflation, intrusion and eruption at inflating volcanoes

NASA Astrophysics Data System (ADS)

Blake, Stephen; Cortés, Joaquín A.

2018-01-01

A principal goal of volcanology is to successfully forecast the start of volcanic eruptions. This paper introduces a general forecasting method, which relies on a stream of monitoring data and a statistical description of a given threshold criterion for an eruption to start. Specifically we investigate the timing of intrusive and eruptive events at inflating volcanoes. The gradual inflation of the ground surface is a well-known phenomenon at many volcanoes and is attributable to pressurised magma accumulating within a shallow chamber. Inflation usually culminates in a rapid deflation event caused by magma escaping from the chamber to produce a shallow intrusion and, in some cases, a volcanic eruption. We show that the ground elevation during 15 inflation periods at Krafla volcano, Iceland, increased with time towards a limiting value by following a decaying exponential with characteristic timescale τ. The available data for Krafla, Kilauea and Mauna Loa volcanoes show that the duration of inflation (t*) is approximately equal to τ. The distribution of t* / τ values follows a log-logistic distribution in which the central 60% of the data lie between 0.99 forecast, and forecasts can be updated after each new deformation measurement. The method provides stronger forecasts than one based on the distribution of repose times alone and is transferable to other types of monitoring data and/or other patterns of pre-eruptive unrest.

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

Business Schools under Fire: Humanistic Management Education as the Way Forward. Humanism in Business Series

ERIC Educational Resources Information Center

Amann, Wolfgang, Ed.; Pirson, Michael, Ed.; Dierksmeier, Claus, Ed.; Von Kimakowitz, Ernst, Ed.; Spitzeck, Heiko, Ed.

2011-01-01

In a time of instability trust in managers is low. Management education is being scrutinized for its impact on society and business schools have been considered as "silent partners in corporate crime." This book outlines how business schools can get out of the line of fire by presenting the cornerstones of a humanistic business…

Study on load forecasting to data centers of high power density based on power usage effectiveness

NASA Astrophysics Data System (ADS)

Zhou, C. C.; Zhang, F.; Yuan, Z.; Zhou, L. M.; Wang, F. M.; Li, W.; Yang, J. H.

2016-08-01

There is usually considerable energy consumption in data centers. Load forecasting to data centers is in favor of formulating regional load density indexes and of great benefit to getting regional spatial load forecasting more accurately. The building structure and the other influential factors, i.e. equipment, geographic and climatic conditions, are considered for the data centers, and a method to forecast the load of the data centers based on power usage effectiveness is proposed. The cooling capacity of a data center and the index of the power usage effectiveness are used to forecast the power load of the data center in the method. The cooling capacity is obtained by calculating the heat load of the data center. The index is estimated using the group decision-making method of mixed language information. An example is given to prove the applicability and accuracy of this method.

Value of the GENS Forecast Ensemble as a Tool for Adaptation of Economic Activity to Climate Change

NASA Astrophysics Data System (ADS)

Hancock, L. O.; Alpert, J. C.; Kordzakhia, M.

2009-12-01

In an atmosphere of uncertainty as to the magnitude and direction of climate change in upcoming decades, one adaptation mechanism has emerged with consensus support: the upgrade and dissemination of spatially-resolved, accurate forecasts tailored to the needs of users. Forecasting can facilitate the changeover from dependence on climatology that is increasingly out of date. The best forecasters are local, but local forecasters face great constraints in some countries. Indeed, it is no coincidence that some areas subject to great weather variability and strong processes of climate change are economically vulnerable: mountainous regions, for example, where heavy and erratic flooding can destroy the value built up by households over years. It follows that those best placed to benefit from forecasting upgrades may not be those who have invested in the greatest capacity to date. More-flexible use of the global forecasts may contribute to adaptation. NOAA anticipated several years ago that their forecasts could be used in new ways in the future, and accordingly prepared sockets for easy access to their archives. These could be used to empower various national and regional capacities. Verification to identify practical lead times for the economically important variables is a needed first step. This presentation presents the verification that our team has undertaken, a pilot effort in which we considered variables of interest to economic actors in several lower income countries, cf. shepherds in a remote area of Central Asia, and verified the ensemble forecasts of those variables.

Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

PubMed Central

Zhang, Banglin; Tallapragada, Vijay; Weng, Fuzhong; Liu, Qingfu; Sippel, Jason A.; Ma, Zaizhong; Bender, Morris A.

2016-01-01

The atmosphere−ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels. PMID:27698121

WOVOdat as a worldwide resource to improve eruption forecasts

NASA Astrophysics Data System (ADS)

Widiwijayanti, Christina; Costa, Fidel; Zar Win Nang, Thin; Tan, Karine; Newhall, Chris; Ratdomopurbo, Antonius

2015-04-01

During periods of volcanic unrest, volcanologists need to interpret signs of unrest to be able to forecast whether an eruption is likely to occur. Some volcanic eruptions display signs of impending eruption such as seismic activity, surface deformation, or gas emissions; but not all will give signs and not all signs are necessarily followed by an eruption. Volcanoes behave differently. Precursory signs of an eruption are sometimes very short, less than an hour, but can be also weeks, months, or even years. Some volcanoes are regularly active and closely monitored, while other aren't. Often, the record of precursors to historical eruptions of a volcano isn't enough to allow a forecast of its future activity. Therefore, volcanologists must refer to monitoring data of unrest and eruptions at similar volcanoes. WOVOdat is the World Organization of Volcano Observatories' Database of volcanic unrest - an international effort to develop common standards for compiling and storing data on volcanic unrests in a centralized database and freely web-accessible for reference during volcanic crises, comparative studies, and basic research on pre-eruption processes. WOVOdat will be to volcanology as an epidemiological database is to medicine. We have up to now incorporated about 15% of worldwide unrest data into WOVOdat, covering more than 100 eruption episodes, which includes: volcanic background data, eruptive histories, monitoring data (seismic, deformation, gas, hydrology, thermal, fields, and meteorology), monitoring metadata, and supporting data such as reports, images, maps and videos. Nearly all data in WOVOdat are time-stamped and geo-referenced. Along with creating a database on volcanic unrest, WOVOdat also developing web-tools to help users to query, visualize, and compare data, which further can be used for probabilistic eruption forecasting. Reference to WOVOdat will be especially helpful at volcanoes that have not erupted in historical or 'instrumental' time and

Application of Second-Moment Source Analysis to Three Problems in Earthquake Forecasting

NASA Astrophysics Data System (ADS)

Donovan, J.; Jordan, T. H.

2011-12-01

Though earthquake forecasting models have often represented seismic sources as space-time points (usually hypocenters), a more complete hazard analysis requires the consideration of finite-source effects, such as rupture extent, orientation, directivity, and stress drop. The most compact source representation that includes these effects is the finite moment tensor (FMT), which approximates the degree-two polynomial moments of the stress glut by its projection onto the seismic (degree-zero) moment tensor. This projection yields a scalar space-time source function whose degree-one moments define the centroid moment tensor (CMT) and whose degree-two moments define the FMT. We apply this finite-source parameterization to three forecasting problems. The first is the question of hypocenter bias: can we reject the null hypothesis that the conditional probability of hypocenter location is uniformly distributed over the rupture area? This hypothesis is currently used to specify rupture sets in the "extended" earthquake forecasts that drive simulation-based hazard models, such as CyberShake. Following McGuire et al. (2002), we test the hypothesis using the distribution of FMT directivity ratios calculated from a global data set of source slip inversions. The second is the question of source identification: given an observed FMT (and its errors), can we identify it with an FMT in the complete rupture set that represents an extended fault-based rupture forecast? Solving this problem will facilitate operational earthquake forecasting, which requires the rapid updating of earthquake triggering and clustering models. Our proposed method uses the second-order uncertainties as a norm on the FMT parameter space to identify the closest member of the hypothetical rupture set and to test whether this closest member is an adequate representation of the observed event. Finally, we address the aftershock excitation problem: given a mainshock, what is the spatial distribution of aftershock

Traffic forecasting report : 2007.

DOT National Transportation Integrated Search

2008-05-01

This is the sixth edition of the Traffic Forecasting Report (TFR). This edition of the TFR contains the latest (predominantly 2007) forecasting/modeling data as follows: : Functional class average traffic volume growth rates and trends : Vehi...

Forecasting Influenza Epidemics in Hong Kong.

PubMed

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

2015-07-01

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

Forecasting Influenza Epidemics in Hong Kong

PubMed Central

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

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

LinkedIn as a Learning Tool in Business Education

ERIC Educational Resources Information Center

Cooper, Brett; Naatus, Mary Kate

2014-01-01

This article summarizes the existing research on social media as a learning tool in higher education and adds to the literature on incorporating social media tools into collegiate business education by suggesting specific course content areas of business where LinkedIn exercises and training can be incorporated. LinkedIn as a classroom tool cannot…

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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.

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

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

A multi-source data assimilation framework for flood forecasting: Accounting for runoff routing lags

NASA Astrophysics Data System (ADS)

Meng, S.; Xie, X.

2015-12-01

In the flood forecasting practice, model performance is usually degraded due to various sources of uncertainties, including the uncertainties from input data, model parameters, model structures and output observations. Data assimilation is a useful methodology to reduce uncertainties in flood forecasting. For the short-term flood forecasting, an accurate estimation of initial soil moisture condition will improve the forecasting performance. Considering the time delay of runoff routing is another important effect for the forecasting performance. Moreover, the observation data of hydrological variables (including ground observations and satellite observations) are becoming easily available. The reliability of the short-term flood forecasting could be improved by assimilating multi-source data. The objective of this study is to develop a multi-source data assimilation framework for real-time flood forecasting. In this data assimilation framework, the first step is assimilating the up-layer soil moisture observations to update model state and generated runoff based on the ensemble Kalman filter (EnKF) method, and the second step is assimilating discharge observations to update model state and runoff within a fixed time window based on the ensemble Kalman smoother (EnKS) method. This smoothing technique is adopted to account for the runoff routing lag. Using such assimilation framework of the soil moisture and discharge observations is expected to improve the flood forecasting. In order to distinguish the effectiveness of this dual-step assimilation framework, we designed a dual-EnKF algorithm in which the observed soil moisture and discharge are assimilated separately without accounting for the runoff routing lag. The results show that the multi-source data assimilation framework can effectively improve flood forecasting, especially when the runoff routing has a distinct time lag. Thus, this new data assimilation framework holds a great potential in operational flood

More Intense Experiences, Less Intense Forecasts: Why People Overweight Probability Specifications in Affective Forecasts

PubMed Central

Buechel, Eva C.; Zhang, Jiao; Morewedge, Carey K.; Vosgerau, Joachim

2014-01-01

We propose that affective forecasters overestimate the extent to which experienced hedonic responses to an outcome are influenced by the probability of its occurrence. The experience of an outcome (e.g., winning a gamble) is typically more affectively intense than the simulation of that outcome (e.g., imagining winning a gamble) upon which the affective forecast for it is based. We suggest that, as a result, experiencers allocate a larger share of their attention toward the outcome (e.g., winning the gamble) and less to its probability specifications than do affective forecasters. Consequently, hedonic responses to an outcome are less sensitive to its probability specifications than are affective forecasts for that outcome. The results of 6 experiments provide support for our theory. Affective forecasters overestimated how sensitive experiencers would be to the probability of positive and negative outcomes (Experiments 1 and 2). Consistent with our attentional account, differences in sensitivity to probability specifications disappeared when the attention of forecasters was diverted from probability specifications (Experiment 3) or when the attention of experiencers was drawn toward probability specifications (Experiment 4). Finally, differences in sensitivity to probability specifications between forecasters and experiencers were diminished when the forecasted outcome was more affectively intense (Experiments 5 and 6). PMID:24128184

More intense experiences, less intense forecasts: why people overweight probability specifications in affective forecasts.

PubMed

Buechel, Eva C; Zhang, Jiao; Morewedge, Carey K; Vosgerau, Joachim

2014-01-01

We propose that affective forecasters overestimate the extent to which experienced hedonic responses to an outcome are influenced by the probability of its occurrence. The experience of an outcome (e.g., winning a gamble) is typically more affectively intense than the simulation of that outcome (e.g., imagining winning a gamble) upon which the affective forecast for it is based. We suggest that, as a result, experiencers allocate a larger share of their attention toward the outcome (e.g., winning the gamble) and less to its probability specifications than do affective forecasters. Consequently, hedonic responses to an outcome are less sensitive to its probability specifications than are affective forecasts for that outcome. The results of 6 experiments provide support for our theory. Affective forecasters overestimated how sensitive experiencers would be to the probability of positive and negative outcomes (Experiments 1 and 2). Consistent with our attentional account, differences in sensitivity to probability specifications disappeared when the attention of forecasters was diverted from probability specifications (Experiment 3) or when the attention of experiencers was drawn toward probability specifications (Experiment 4). Finally, differences in sensitivity to probability specifications between forecasters and experiencers were diminished when the forecasted outcome was more affectively intense (Experiments 5 and 6).

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

Why Don't We Learn to Accurately Forecast Feelings? How Misremembering Our Predictions Blinds Us to Past Forecasting Errors

ERIC Educational Resources Information Center

Meyvis, Tom; Ratner, Rebecca K.; Levav, Jonathan

2010-01-01

Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent…

A pharmacy business management simulation exercise as a practical application of business management material and principles.

PubMed

Rollins, Brent L; Gunturi, Rahul; Sullivan, Donald

2014-04-17

To implement a pharmacy business management simulation exercise as a practical application of business management material and principles and assess students' perceived value. As part of a pharmacy management and administration course, students made various calculations and management decisions in the global categories of hours of operation, inventory, pricing, and personnel. The students entered the data into simulation software and a realistic community pharmacy marketplace was modeled. Course topics included accounting, economics, finance, human resources, management, marketing, and leadership. An 18-item posttest survey was administered. Students' slightly to moderately agreed the pharmacy simulation program enhanced their knowledge and understanding, particularly of inventory management, cash flow statements, balance sheets, and income statements. Overall attitudes toward the pharmacy simulation program were also slightly positive and students also slightly agreed the pharmacy simulation program enhanced their learning of pharmacy business management. Inventory management was the only area in which students felt they had at least "some" exposure to the assessed business management topics during IPPEs/internship, while all other areas of experience ranged from "not at all" to "a little." The pharmacy simulation program is an effective active-learning exercise and enhanced students' knowledge and understanding of the business management topics covered.

A Pharmacy Business Management Simulation Exercise as a Practical Application of Business Management Material and Principles

PubMed Central

Rollins, Brent L.; Gunturi, Rahul; Sullivan, Donald

2014-01-01

Objective. To implement a pharmacy business management simulation exercise as a practical application of business management material and principles and assess students’ perceived value. Design. As part of a pharmacy management and administration course, students made various calculations and management decisions in the global categories of hours of operation, inventory, pricing, and personnel. The students entered the data into simulation software and a realistic community pharmacy marketplace was modeled. Course topics included accounting, economics, finance, human resources, management, marketing, and leadership. Assessment. An 18-item posttest survey was administered. Students’ slightly to moderately agreed the pharmacy simulation program enhanced their knowledge and understanding, particularly of inventory management, cash flow statements, balance sheets, and income statements. Overall attitudes toward the pharmacy simulation program were also slightly positive and students also slightly agreed the pharmacy simulation program enhanced their learning of pharmacy business management. Inventory management was the only area in which students felt they had at least “some” exposure to the assessed business management topics during IPPEs/internship, while all other areas of experience ranged from “not at all” to “a little.” Conclusion. The pharmacy simulation program is an effective active-learning exercise and enhanced students’ knowledge and understanding of the business management topics covered. PMID:24761023

Integrating predictive information into an agro-economic model to guide agricultural management

NASA Astrophysics Data System (ADS)

Zhang, Y.; Block, P.

2016-12-01

Skillful season-ahead climate predictions linked with responsive agricultural planning and management have the potential to reduce losses, if adopted by farmers, particularly for rainfed-dominated agriculture such as in Ethiopia. Precipitation predictions during the growing season in major agricultural regions of Ethiopia are used to generate predicted climate yield factors, which reflect the influence of precipitation amounts on crop yields and serve as inputs into an agro-economic model. The adapted model, originally developed by the International Food Policy Research Institute, produces outputs of economic indices (GDP, poverty rates, etc.) at zonal and national levels. Forecast-based approaches, in which farmers' actions are in response to forecasted conditions, are compared with no-forecast approaches in which farmers follow business as usual practices, expecting "average" climate conditions. The effects of farmer adoption rates, including the potential for reduced uptake due to poor predictions, and increasing forecast lead-time on economic outputs are also explored. Preliminary results indicate superior gains under forecast-based approaches.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Forecasts and predictions of eruptive activity at Mount St. Helens, USA: 1975-1984

USGS Publications Warehouse

Swanson, D.A.; Casadevall, T.J.; Dzurisin, D.; Holcomb, R.T.; Newhall, C.G.; Malone, S.D.; Weaver, C.S.

1985-01-01

Public statements about volcanic activity at Mount St. Helens include factual statements, forecasts, and predictions. A factual statement describes current conditions but does not anticipate future events. A forecast is a comparatively imprecise statement of the time, place, and nature of expected activity. A prediction is a comparatively precise statement of the time, place, and ideally, the nature and size of impending activity. A prediction usually covers a shorter time period than a forecast and is generally based dominantly on interpretations and measurements of ongoing processes and secondarily on a projection of past history. The three types of statements grade from one to another, and distinctions are sometimes arbitrary. Forecasts and predictions at Mount St. Helens became increasingly precise from 1975 to 1982. Stratigraphic studies led to a long-range forecast in 1975 of renewed eruptive activity at Mount St. Helens, possibly before the end of the century. On the basis of seismic, geodetic and geologic data, general forecasts for a landslide and eruption were issued in April 1980, before the catastrophic blast and landslide on 18 May 1980. All extrusions except two from June 1980 to the end of 1984 were predicted on the basis of integrated geophysical, geochemical, and geologic monitoring. The two extrusions that were not predicted were preceded by explosions that removed a substantial part of the dome, reducing confining pressure and essentially short-circuiting the normal precursors. ?? 1985.

Type- and Subtype-Specific Influenza Forecast.

PubMed

Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2017-03-01

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

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

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

Business planning health-check questionnaire: a survey of first, second, third and fourth wave NHS Trusts.

PubMed

Bennett, A R; Banks, J M

1999-02-01

This paper reports the results of primary research which was carried out in July 1995 with respect to business planning within first, second, third and fourth wave National Health Service (NHS) Trusts. The purpose of the research was to examine current practice in these Trusts in three areas--namely, the levels of responsibility for business planning in general, the business planning processes applied by these Trusts, and the tools and techniques used by business planning managers in the compilation of business plans. The research, based on a 37.5% response rate, concludes that, as a general rule, business planning in first, second, third and fourth wave NHS Trusts tends to be a board-level activity, where senior managers have a job title which reflects this function. Secondly, the research shows that by far the greatest challenge for Trusts lies in the external marketplace. In areas such as patient needs forecasting, competitive (Trust) intelligence, purchaser and general practice fundholder requirements, data are difficult to acquire. Finally, the evidence suggests that there is a significant gap between what is regarded as business planning practice in the NHS and what is actually applied as best practice. The report concludes that business planning in the NHS Trusts sampled appears to be an art rather than a science, and that many assumptions made by business planning managers are founded on qualitative information rather than on specific, measurable data derived from the external and internal market.

Marine Point Forecasts

Science.gov Websites

with smartphones and other mobile platforms new Marine Point Forecasts are a forecast for a specific maps providing zone/point marine forecasts Mobile, AL Eureka, CA San Francisco, CA Los Angeles, CA San

Statistical evaluation of forecasts

NASA Astrophysics Data System (ADS)

Mader, Malenka; Mader, Wolfgang; Gluckman, Bruce J.; Timmer, Jens; Schelter, Björn

2014-08-01

Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

Assessing the Effectiveness of the Cone of Probability as a Visual Means of Communicating Scientific Forecasts

NASA Astrophysics Data System (ADS)

Orlove, B. S.; Broad, K.; Meyer, R.

2010-12-01

We review the evolution, communication, and differing interpretations of the National Hurricane Center (NHC)'s "cone of uncertainty" hurricane forecast graphic, drawing on several related disciplines—cognitive psychology, visual anthropology, and risk communication theory. We examine the 2004 hurricane season, two specific hurricanes (Katrina 2005 and Ike 2008) and the 2010 hurricane season, still in progress. During the 2004 hurricane season, five named storms struck Florida. Our analysis of that season draws upon interviews with key government officials and media figures, archival research of Florida newspapers, analysis of public comments on the NHC cone of uncertainty graphic and a multiagency study of 2004 hurricane behavior. At that time, the hurricane forecast graphic was subject to misinterpretation by many members of the public. We identify several characteristics of this graphic that contributed to public misinterpretation. Residents overemphasized the specific track of the eye, failed to grasp the width of hurricanes, and generally did not recognize the timing of the passage of the hurricane. Little training was provided to emergency response managers in the interpretation of forecasts. In the following year, Katrina became a national scandal, further demonstrating the limitations of the cone as a means of leading to appropriate responses to forecasts. In the second half of the first decade of the 21st century, three major changes occurred in hurricane forecast communication: the forecasts themselves improved in terms of accuracy and lead time, the NHC made minor changes in the graphics and expanded the explanatory material that accompanies the graphics, and some efforts were made to reach out to emergency response planners and municipal officials to enhance their understanding of the forecasts and graphics. There were some improvements in the responses to Ike, though a number of deaths were due to inadequate evacuations, and property damage probably

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

48 CFR 19.1303 - Status as a qualified HUBZone small business concern.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Status as a qualified... ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Historically Underutilized Business Zone (HUBZone) Program 19.1303 Status as a qualified HUBZone small business concern. (a) Status as a qualified...

The Economic Value of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Anderson-Sumo, Tasha

Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

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

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Improving seasonal forecast through the state of large-scale climate signals

NASA Astrophysics Data System (ADS)

Samale, Chiara; Zimmerman, Brian; Giuliani, Matteo; Castelletti, Andrea; Block, Paul

2017-04-01

Increasingly uncertain hydrologic regimes are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. In fact, forecasts are usually skillful over short lead time (from hours to days), but predictability tends to decrease on longer lead times. The forecast lead time might be extended by using climate teleconnection, such as El Nino Southern Oscillation (ENSO). Despite the ENSO teleconnection is well defined in some locations such as Western USA and Australia, there is no consensus on how it can be detected and used in other river basins, particularly in Europe, Africa, and Asia. In this work, we propose the use of the Nino Index Phase Analysis for capturing the state of multiple large-scale climate signals (i.e., ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, Dipole Mode Index). This climate state information is used for distinguishing the different phases of the climate signals and for identifying relevant teleconnections between the observations of Sea Surface Temperature (SST) that mostly influence the local hydrologic conditions. The framework is applied to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Preliminary results show high correlations between SST and three to six months ahead precipitation in the Lake Como basin. This forecast represents a valuable information to partially anticipate the summer water availability, ultimately supporting the improvement of the Lake Como operations.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

The business case as a strategic tool for change.

PubMed

Weaver, Diana J; Sorrells-Jones, Jean

2007-09-01

The authors discuss the clinically focused business case, when it is and is not needed, and the knowledge and skills the nurse executive must master to use the business case effectively as a strategic tool. Necessary skills include translating nursing practice proposals into cost-effective change initiatives and marketing those changes to colleagues.

Transferral of Business Management Concepts to Universities as Ambidextrous Organisations

ERIC Educational Resources Information Center

Tahar, Sadri; Niemeyer, Cornelius; Boutellier, Roman

2011-01-01

In the context of new public management reforms, many business management concepts were transferred to universities. Most studies on the transfer of business management concepts to universities show that transfers were not as successful as expected. These studies also provide nuances as to why it is such a delicate matter. However, a basic…

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

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

Visualization of ocean forecast in BYTHOS

NASA Astrophysics Data System (ADS)

Zhuk, E.; Zodiatis, G.; Nikolaidis, A.; Stylianou, S.; Karaolia, A.

2016-08-01

The Cyprus Oceanography Center has been constantly searching for new ideas for developing and implementing innovative methods and new developments concerning the use of Information Systems in Oceanography, to suit both the Center's monitoring and forecasting products. Within the frame of this scope two major online managing and visualizing data systems have been developed and utilized, those of CYCOFOS and BYTHOS. The Cyprus Coastal Ocean Forecasting and Observing System - CYCOFOS provides a variety of operational predictions such as ultra high, high and medium resolution ocean forecasts in the Levantine Basin, offshore and coastal sea state forecasts in the Mediterranean and Black Sea, tide forecasting in the Mediterranean, ocean remote sensing in the Eastern Mediterranean and coastal and offshore monitoring. As a rich internet application, BYTHOS enables scientists to search, visualize and download oceanographic data online and in real time. The recent improving of BYTHOS system is the extension with access and visualization of CYCOFOS data and overlay forecast fields and observing data. The CYCOFOS data are stored at OPENDAP Server in netCDF format. To search, process and visualize it the php and python scripts were developed. Data visualization is achieved through Mapserver. The BYTHOS forecast access interface allows to search necessary forecasting field by recognizing type, parameter, region, level and time. Also it provides opportunity to overlay different forecast and observing data that can be used for complex analyze of sea basin aspects.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

NASA Astrophysics Data System (ADS)

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

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2017-04-01

Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts

Accuracy analysis of TDRSS demand forecasts

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

International Content as Hidden Curriculum in Business Statistics: An Overlooked Opportunity

ERIC Educational Resources Information Center

Sebastianelli, Rose; Trussler, Susan

2006-01-01

We revisit the issue of internationalizing the required course in business statistics as a means for introducing international subject matter earlier in the undergraduate business curriculum. A survey of sophomore business students indicates that their level of international knowledge is poor. The results are strikingly similar to a decade ago.…

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

PubMed Central

MARROQUÍN, BRETT; NOLEN-HOEKSEMA, SUSAN

2015-01-01

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

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

PubMed

Marroquín, Brett; Nolen-Hoeksema, Susan

2015-02-01

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

16 CFR 18.7 - Misrepresentation as to character of business.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Misrepresentation as to character of business. 18.7 Section 18.7 Commercial Practices FEDERAL TRADE COMMISSION GUIDES AND TRADE PRACTICE RULES GUIDES FOR THE NURSERY INDUSTRY § 18.7 Misrepresentation as to character of business. (a) In the sale...

Forecasting Consumer Adoption of Information Technology and Services--Lessons from Home Video Forecasting.

ERIC Educational Resources Information Center

Klopfenstein, Bruce C.

1989-01-01

Describes research that examined the strengths and weaknesses of technological forecasting methods by analyzing forecasting studies made for home video players. The discussion covers assessments and explications of correct and incorrect forecasting assumptions, and their implications for forecasting the adoption of home information technologies…

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

The negative effects of severe flood events are usually contrasted through structural measures that, however, do not fully eliminate flood risk. Non-structural measures, such as real-time flood forecasting and warning, are also required. Accurate stage/discharge future predictions with appropriate forecast lead-time are sought by decision-makers for implementing strategies to mitigate the adverse effects of floods. Traditionally, flood forecasting has been approached by using rainfall-runoff and/or flood routing modelling. Indeed, both types of forecasts, cannot be considered perfectly representing future outcomes because of lacking of a complete knowledge of involved processes (Todini, 2004). Nonetheless, although aware that model forecasts are not perfectly representing future outcomes, decision makers are de facto implicitly assuming the forecast of water level/discharge/volume, etc. as "deterministic" and coinciding with what is going to occur. Recently the concept of Predictive Uncertainty (PU) was introduced in hydrology (Krzysztofowicz, 1999), and several uncertainty processors were developed (Todini, 2008). PU is defined as the probability of occurrence of the future realization of a predictand (water level/discharge/volume) conditional on: i) prior observations and knowledge, ii) the available information obtained on the future value, typically provided by one or more forecast models. Unfortunately, PU has been frequently interpreted as a measure of lack of accuracy rather than the appropriate tool allowing to take the most appropriate decisions, given a model or several models' forecasts. With the aim to shed light on the benefits for appropriately using PU, a multi-temporal approach based on the MCP approach (Todini, 2008; Coccia and Todini, 2011) is here applied to stage forecasts at sites along the Upper Tiber River. Specifically, the STAge Forecasting-Rating Curve Model Muskingum-based (STAFOM-RCM) (Barbetta et al., 2014) along with the Rating

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

NASA Astrophysics Data System (ADS)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

48 CFR 19.306 - Protesting a firm's status as a HUBZone small business concern.

Code of Federal Regulations, 2014 CFR

2014-10-01

... as a HUBZone small business concern. 19.306 Section 19.306 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Determination of Small Business Status for Small Business Programs 19.306 Protesting a firm's status as a HUBZone small business...

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

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

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus

2016-04-01

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

Six rules for accurate effective forecasting.

PubMed

Saffo, Paul

2007-01-01

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

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Improved Dust Forecast Products for Southwest Asia Forecasters through Dust Source Database Advancements

NASA Astrophysics Data System (ADS)

Brooks, G. R.

2011-12-01

Dust storm forecasting is a critical part of military theater operations in Afghanistan and Iraq as well as other strategic areas of the globe. The Air Force Weather Agency (AFWA) has been using the Dust Transport Application (DTA) as a forecasting tool since 2001. Initially developed by The Johns Hopkins University Applied Physics Laboratory (JHUAPL), output products include dust concentration and reduction of visibility due to dust. The performance of the products depends on several factors including the underlying dust source database, treatment of soil moisture, parameterization of dust processes, and validity of the input atmospheric model data. Over many years of analysis, seasonal dust forecast biases of the DTA have been observed and documented. As these products are unique and indispensible for U.S. and NATO forces, amendments were required to provide the best forecasts possible. One of the quickest ways to scientifically address the dust concentration biases noted over time was to analyze the weaknesses in, and adjust the dust source database. Dust source database strengths and weaknesses, the satellite analysis and adjustment process, and tests which confirmed the resulting improvements in the final dust concentration and visibility products will be shown.

Socioeconomic Forecasting

DOT National Transportation Integrated Search

2012-05-01

The role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI : PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI P...

Socioeconomic forecasting.

DOT National Transportation Integrated Search

2012-05-01

The role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI : PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI P...

Improvement of inventory control and forecast according to activity-based classifications: T company as an example

NASA Astrophysics Data System (ADS)

Huang, Jui-Chan; Wu, Tzu-Jung; Chiu, Yen-Chun; Lu, Chunwei

2017-06-01

Inventory management is a major issue for all the industries. The supply of products to customers requires the readiness of the inventory. This allows rapid delivery and reduces waiting time for customers so that companies can profit from it. Any stock out or insufficiency will lead to loss of customers because their needs cannot be met. This will hurt firm profitability and market competitiveness. Inventory control is critical to retain liquidity and avoid overstocking. This is also the key to firm's survival and sustainability. To ensure an appropriate level of inventory, it is necessary to manage the inventory levels with sales forecast on an on-going basis. This paper seeks to assist Company T to improve its inventory control. Firstly, the products offered by Company T are classified into groups. The R programming language is used to stimulate and forecast future sales of different products. Different techniques are applied to manage the inventory levels according to the results of categorizations and forecasts that are consolidation of all the product items and grouping them into activity-based classifications, simulation and forecasting of future sales according to the categorization results, and formulation of different control techniques based on the simulations and forecasts. The results and the inventory management can be used to enhance the inventory control as well.

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

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

2014-01-01

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

Improving operational plume forecasts

NASA Astrophysics Data System (ADS)

Balcerak, Ernie

2012-04-01

Forecasting how plumes of particles, such as radioactive particles from a nuclear disaster, will be transported and dispersed in the atmosphere is an important but computationally challenging task. During the Fukushima nuclear disaster in Japan, operational plume forecasts were produced each day, but as the emissions continued, previous emissions were not included in the simulations used for forecasts because it became impractical to rerun the simulations each day from the beginning of the accident. Draxler and Rolph examine whether it is possible to improve plume simulation speed and flexibility as conditions and input data change. The authors use a method known as a transfer coefficient matrix approach that allows them to simulate many radionuclides using only a few generic species for the computation. Their simulations work faster by dividing the computation into separate independent segments in such a way that the most computationally time consuming pieces of the calculation need to be done only once. This makes it possible to provide real-time operational plume forecasts by continuously updating the previous simulations as new data become available. They tested their method using data from the Fukushima incident to show that it performed well. (Journal of Geophysical Research-Atmospheres, doi:10.1029/2011JD017205, 2012)

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

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

On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

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

Operational Earthquake Forecasting of Aftershocks for New England

NASA Astrophysics Data System (ADS)

Ebel, J.; Fadugba, O. I.

2015-12-01

Although the forecasting of mainshocks is not possible, recent research demonstrates that probabilistic forecasts of expected aftershock activity following moderate and strong earthquakes is possible. Previous work has shown that aftershock sequences in intraplate regions behave similarly to those in California, and thus the operational aftershocks forecasting methods that are currently employed in California can be adopted for use in areas of the eastern U.S. such as New England. In our application, immediately after a felt earthquake in New England, a forecast of expected aftershock activity for the next 7 days will be generated based on a generic aftershock activity model. Approximately 24 hours after the mainshock, the parameters of the aftershock model will be updated using the observed aftershock activity observed to that point in time, and a new forecast of expected aftershock activity for the next 7 days will be issued. The forecast will estimate the average number of weak, felt aftershocks and the average expected number of aftershocks based on the aftershock statistics of past New England earthquakes. The forecast also will estimate the probability that an earthquake that is stronger than the mainshock will take place during the next 7 days. The aftershock forecast will specify the expected aftershocks locations as well as the areas over which aftershocks of different magnitudes could be felt. The system will use web pages, email and text messages to distribute the aftershock forecasts. For protracted aftershock sequences, new forecasts will be issued on a regular basis, such as weekly. Initially, the distribution system of the aftershock forecasts will be limited, but later it will be expanded as experience with and confidence in the system grows.

Understanding the Influence of Climate Forecasts on Farmer Decisions as Planned Behavior

NASA Astrophysics Data System (ADS)

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

2006-09-01

Results of a set of four regression models applied to recent survey data of farmers in eastern Nebraska suggest the causes that drive farmer intentions of using weather and climate information and forecasts in farming decisions. The model results quantify the relative importance of attitude, social norm, perceived behavioral control, and financial capability in explaining the influence of climate-conditions information and short-term and long-term forecasts on agronomic, crop insurance, and crop marketing decisions. Attitude, serving as a proxy for the utility gained from the use of such information, had the most profound positive influence on the outcome of all the decisions, followed by norms. The norms in the community, as a proxy for the utility gained from allowing oneself to be influenced by others, played a larger role in agronomic decisions than in insurance or marketing decisions. In addition, the interaction of controllability (accuracy, availability, reliability, timeliness of weather and climate information), self-efficacy (farmer ability and understanding), and general preference for control was shown to be a substantive cause. Yet control variables also have an economic side: The farm-sales variable as a measure of financial ability and motivation intensified and clarified the role of control while also enhancing the statistical robustness of the attitude and norms variables in better clarifying how they drive the influence. Overall, the integrated model of planned behavior from social psychology and derived demand from economics, that is, the “planned demand model,” is more powerful than models based on either of these approaches alone. Taken together, these results suggest that the “human dimension” needs to be better recognized so as to improve effective use of climate and weather forecasts and information for farming decision making.

Statistical Earthquake Focal Mechanism Forecasts

NASA Astrophysics Data System (ADS)

Kagan, Y. Y.; Jackson, D. D.

2013-12-01

The new whole Earth focal mechanism forecast, based on the GCMT catalog, has been created. In the present forecast, the sum of normalized seismic moment tensors within 1000 km radius is calculated and the P- and T-axes for the focal mechanism are evaluated on the basis of the sum. Simultaneously we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms. This average angle shows tectonic complexity of a region and indicates the accuracy of the prediction. The method was originally proposed by Kagan and Jackson (1994, JGR). Recent interest by CSEP and GEM has motivated some improvements, particularly to extend the previous forecast to polar and near-polar regions. The major problem in extending the forecast is the focal mechanism calculation on a spherical surface. In the previous forecast as our average focal mechanism was computed, it was assumed that longitude lines are approximately parallel within 1000 km radius. This is largely accurate in the equatorial and near-equatorial areas. However, when one approaches the 75 degree latitude, the longitude lines are no longer parallel: the bearing (azimuthal) difference at points separated by 1000 km reach about 35 degrees. In most situations a forecast point where we calculate an average focal mechanism is surrounded by earthquakes, so a bias should not be strong due to the difference effect cancellation. But if we move into polar regions, the bearing difference could approach 180 degrees. In a modified program focal mechanisms have been projected on a plane tangent to a sphere at a forecast point. New longitude axes which are parallel in the tangent plane are corrected for the bearing difference. A comparison with the old 75S-75N forecast shows that in equatorial regions the forecasted focal mechanisms are almost the same, and the difference in the forecasted focal mechanisms rotation angle is close to zero. However, though the forecasted focal mechanisms are similar

Business as Usual? A Review of Continuing Professional Education and Adult Learning

ERIC Educational Resources Information Center

Wittnebel, Leo

2012-01-01

The commodification of education in all forms has created a lucrative trade, particularly within the realm of continuing professional education. Mandated across a wide spectrum of industries, and particularly salient in healthcare due to rapid advances in medicine and technology, professional education is said to be the vehicle that keeps…

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

2017-08-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Patching. Restitching business portfolios in dynamic markets.

PubMed

Eisenhardt, K M; Brown, S L

1999-01-01

In turbulent markets, businesses and opportunities are constantly falling out of alignment. New technologies and emerging markets create fresh opportunities. Converging markets produce more. And of course, some markets fade. In this landscape of continuous flux, it's more important to build corporate-level strategic processes that enable dynamic repositioning than it is to build any particular defensible position. That's why smart corporate strategists use patching, a process of mapping and remapping business units to create a shifting mix of highly focused, tightly aligned businesses that can respond to changing market opportunities. Patching is not just another name for reorganizing; patchers have a distinctive mindset. Traditional managers see structure as stable; patching managers believe structure is inherently temporary. Traditional managers set corporate strategy first, but patching managers keep the organization focused on the right set of business opportunities and let strategy emerge from individual businesses. Although the focus of patching is flexibility, the process itself follows a pattern. Patching changes are usually small in scale and made frequently. Patching should be done quickly; the emphasis is on getting the patch about right and fixing problems later. Patches should have a test drive before they're formalized but then be tightly scripted after they've been announced. And patching won't work without the right infrastructure: modular business units, fine-grained and complete unit-level metrics, and companywide compensation parity. The authors illustrate how patching works and point out some common stumbling blocks.

NREL to Work with 14 Additional Small Businesses as Part of the DOE Small

Science.gov Websites

Business Vouchers Program | NREL | News | NREL to Work with 14 Additional Small Businesses as Part of the DOE Small Business Vouchers Program News Release: NREL to Work with 14 Additional Small Businesses as Part of the DOE Small Business Vouchers Program May 2, 2017 The U.S. Department of Energy's

Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

NASA Astrophysics Data System (ADS)

Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

2017-04-01

ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i

Starting a business as a nurse consultant: practical considerations.

PubMed

Papp, E M

2000-03-01

Nursing experience translates well to self employment in the occupational and environmental health field. However, nurses must conduct a self assessment to determine whether owning a business is a good fit for their personality and work style. Exploring which services to offer is the next step in starting a business and consists of determining not only which service to offer (e.g., writing policies or protocols, providing clinical services) but also which type of consultation model to use (i.e., purchase of expertise, doctor/client, process consultation). Every business must have a plan. A business plan is essential to starting a business. It solidifies the entrepreneur's focus, lays the foundation for the business, provides a tool for evaluating success, and is a strong tool for soliciting financial support. The name of the business is an important consideration because it is often the first contact the customers have with the occupational health nurse consultant. The name must be descriptive, appropriate, and memorable.

The UN Decade of Education for Sustainable Development: Business as Usual in the End

ERIC Educational Resources Information Center

Huckle, John; Wals, Arjen E. J.

2015-01-01

An analysis of the literature supporting the UN Decade of Education for Sustainable Development and a sample of its key products suggests that it failed to acknowledge or challenge neoliberalism as a hegemonic force blocking transitions towards genuine sustainability. The authors argue that the rationale for the Decade was idealistic and that…

LINKS to NATIONAL WEATHER SERVICE MARINE FORECAST OFFICES

Science.gov Websites

Coastal Flooding Tsunamis 406 EPIRB's National Weather Service Marine Forecasts LINKS to NATIONAL WEATHER Marine Forecasts in text form ) Coastal NWS Forecast Offices have regionally focused marine webpages which are overflowing with information such as coastal forecasts, predicted tides, and buoy observations

Medium-range fire weather forecasts

Treesearch

J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

1991-01-01

The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

Demand forecast model based on CRM

NASA Astrophysics Data System (ADS)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

WPC Maximum Heat Index Forecasts

Science.gov Websites

Forecasts for Western US CLICK ON MAPS FOR MAXIMUM HEAT INDEX AND PROBABILITY FORECASTS FROM SUN MAY 27 2018 02 CLICK to view SAT JUN 02 forecast SUN JUN 03 CLICK to view SUN JUN 03 forecast SUN JUN 03 CLICK to view SUN JUN 03 forecast SUN JUN 03 CLICK to view SUN JUN 03 forecast SUN JUN 03 CLICK to view SUN JUN

Cheques and challenges: business performance in New Zealand general practice.

PubMed

Greatbanks, Richard; Doolan-Noble, Fiona; McKenna, Alex

2017-09-01

INTRODUCTION New Zealand general practice mainly functions as small businesses, usually owned by a single or small group of doctors. Consequently, owners often have to balance the provision of patient care with varying funding priorities, changing patient needs and the pressures of running a sustainable business. Such balancing inevitably leads to tensions developing between these factors. AIM To explore and understand these tensions and responses to them, by examining the business performance measurements used by general practice. METHODS For this study, the unit of analysis and focus were individual practices, but qualitative semi-structured interviews with general practitioners (GPs) and practice managers were used to gather the data. RESULTS All participating practices had some form of governance or board review, where high-level aggregated business performance data were presented. More sophisticated business performance measures were evident in the larger, more developed practices and in practices functioning as community trusts. Examples of such measures included doctor utilisation and efficiency, appraisal of risk, patient satisfaction with services and responses to changes in patient demand. DISCUSSION As the number of general practices based on the traditional model decrease, a corresponding increase is likely in the establishment and development of 'super practices' based on a corporatized, multi-service, single-location model. Consequently, service delivery will become increasingly complex and will drive a need for increased sophistication in how general practice measures its business performance, thus ensuring a balance between high-quality, safe patient care and the maintenance of a sustainable business.

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

Hailstorm forecast from stability indexes in Southwestern France

NASA Astrophysics Data System (ADS)

Melcón, Pablo; Merino, Andrés; Sánchez, José Luis; Dessens, Jean; Gascón, Estíbaliz; Berthet, Claude; López, Laura; García-Ortega, Eduardo

2016-04-01

Forecasting hailstorms is a difficult task because of their small spatial and temporal scales. Over recent decades, stability indexes have been commonly used in operational forecasting to provide a simplified representation of different thermodynamic characteristics of the atmosphere, regarding the onset of convective events. However, they are estimated from vertical profiles obtained by radiosondes, which are usually available only twice a day and have limited spatial representativeness. Numerical models predictions can be used to overcome these drawbacks, providing vertical profiles with higher spatiotemporal resolution. The main objective of this study is to create a tool for hail prediction in the southwest of France, one of the European regions where hailstorms have a higher incidence. The Association Nationale d'Etude et de Lutte contre les Fleáux Atmosphériques (ANELFA) maintains there a dense hailpad network in continuous operation, which has created an extensive database of hail events, used in this study as ground truth. The new technique is aimed to classify the spatial distribution of different stability indexes on hail days. These indexes were calculated from vertical profiles at 1200 UTC provided by WRF numerical model, validated with radiosonde data from Bordeaux. Binary logistic regression is used to select those indexes that best represent thermodynamic conditions related to occurrence of hail in the zone. Then, they are combined in a single algorithm that surpassed the predictive power they have when used independently. Regression equation results in hail days are used in cluster analysis to identify different spatial patterns given by the probability algorithm. This new tool can be used in operational forecasting, in combination with synoptic and mesoscale techniques, to properly define hail probability and distribution. Acknowledgements The authors would like to thank the CEPA González Díez Foundation and the University of Leon for its

Online probabilistic learning with an ensemble of forecasts

NASA Astrophysics Data System (ADS)

Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

2016-04-01

Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

Forecasting Andean rainfall and crop yield from the influence of El Nino on Pleiades visibility

PubMed

Orlove; Chiang; Cane

2000-01-06

Farmers in drought-prone regions of Andean South America have historically made observations of changes in the apparent brightness of stars in the Pleiades around the time of the southern winter solstice in order to forecast interannual variations in summer rainfall and in autumn harvests. They moderate the effect of reduced rainfall by adjusting the planting dates of potatoes, their most important crop. Here we use data on cloud cover and water vapour from satellite imagery, agronomic data from the Andean altiplano and an index of El Nino variability to analyse this forecasting method. We find that poor visibility of the Pleiades in June-caused by an increase in subvisual high cirrus clouds-is indicative of an El Nino year, which is usually linked to reduced rainfall during the growing season several months later. Our results suggest that this centuries-old method of seasonal rainfall forecasting may be based on a simple indicator of El Nino variability.

Waterspout Forecasting Method Over the Eastern Adriatic Using a High-Resolution Numerical Weather Model

NASA Astrophysics Data System (ADS)

Renko, Tanja; Ivušić, Sarah; Telišman Prtenjak, Maja; Šoljan, Vinko; Horvat, Igor

2018-03-01

In this study, a synoptic and mesoscale analysis was performed and Szilagyi's waterspout forecasting method was tested on ten waterspout events in the period of 2013-2016. Data regarding waterspout occurrences were collected from weather stations, an online survey at the official website of the National Meteorological and Hydrological Service of Croatia and eyewitness reports from newspapers and the internet. Synoptic weather conditions were analyzed using surface pressure fields, 500 hPa level synoptic charts, SYNOP reports and atmospheric soundings. For all observed waterspout events, a synoptic type was determined using the 500 hPa geopotential height chart. The occurrence of lightning activity was determined from the LINET lightning database, and waterspouts were divided into thunderstorm-related and "fair weather" ones. Mesoscale characteristics (with a focus on thermodynamic instability indices) were determined using the high-resolution (500 m grid length) mesoscale numerical weather model and model results were compared with the available observations. Because thermodynamic instability indices are usually insufficient for forecasting waterspout activity, the performance of the Szilagyi Waterspout Index (SWI) was tested using vertical atmospheric profiles provided by the mesoscale numerical model. The SWI successfully forecasted all waterspout events, even the winter events. This indicates that the Szilagyi's waterspout prognostic method could be used as a valid prognostic tool for the eastern Adriatic.

Forecasting volcanic eruptions and other material failure phenomena: An evaluation of the failure forecast method

NASA Astrophysics Data System (ADS)

Bell, Andrew F.; Naylor, Mark; Heap, Michael J.; Main, Ian G.

2011-08-01

Power-law accelerations in the mean rate of strain, earthquakes and other precursors have been widely reported prior to material failure phenomena, including volcanic eruptions, landslides and laboratory deformation experiments, as predicted by several theoretical models. The Failure Forecast Method (FFM), which linearizes the power-law trend, has been routinely used to forecast the failure time in retrospective analyses; however, its performance has never been formally evaluated. Here we use synthetic and real data, recorded in laboratory brittle creep experiments and at volcanoes, to show that the assumptions of the FFM are inconsistent with the error structure of the data, leading to biased and imprecise forecasts. We show that a Generalized Linear Model method provides higher-quality forecasts that converge more accurately to the eventual failure time, accounting for the appropriate error distributions. This approach should be employed in place of the FFM to provide reliable quantitative forecasts and estimate their associated uncertainties.

Better Forecasting for Better Planning: A Systems Approach.

ERIC Educational Resources Information Center

Austin, W. Burnet

Predictions and forecasts are the most critical features of rational planning as well as the most vulnerable to inaccuracy. Because plans are only as good as their forecasts, current planning procedures could be improved by greater forecasting accuracy. Economic factors explain and predict more than any other set of factors, making economic…

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

Monthly forecasting of agricultural pests in Switzerland

NASA Astrophysics Data System (ADS)

Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.

2012-04-01

Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the

Operational foreshock forecasting: Fifteen years after

NASA Astrophysics Data System (ADS)

Ogata, Y.

2010-12-01

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

An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

NASA Astrophysics Data System (ADS)

Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

2017-12-01

Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

Assessment of GNSS-based height data of multiple ships for measuring and forecasting great tsunamis

NASA Astrophysics Data System (ADS)

Inazu, Daisuke; Waseda, Takuji; Hibiya, Toshiyuki; Ohta, Yusaku

2016-12-01

Ship height positioning by the Global Navigation Satellite System (GNSS) was investigated for measuring and forecasting great tsunamis. We first examined GNSS height-positioning data of a navigating vessel. If we use the kinematic precise point positioning (PPP) method, tsunamis greater than 10-1 m will be detected by ship height positioning. Based on Automatic Identification System (AIS) data, we found that tens of cargo ships and tankers are usually identified to navigate over the Nankai Trough, southwest Japan. We assumed that a future Nankai Trough great earthquake tsunami will be observed by the kinematic PPP height positioning of an AIS-derived ship distribution, and examined the tsunami forecast capability of the offshore tsunami measurements based on the PPP-based ship height. A method to estimate the initial tsunami height distribution using offshore tsunami observations was used for forecasting. Tsunami forecast tests were carried out using simulated tsunami data by the PPP-based ship height of 92 cargo ships/tankers, and by currently operating deep-sea pressure and Global Positioning System (GPS) buoy observations at 71 stations over the Nankai Trough. The forecast capability using the PPP-based height of the 92 ships was shown to be comparable to or better than that using the operating offshore observatories at the 71 stations. We suppose that, immediately after the occurrence of a great earthquake, stations receiving successive ship information (AIS data) along certain areas of the coast would fail to acquire ship data due to strong ground shaking, especially near the epicenter. Such a situation would significantly deteriorate the tsunami-forecast capability using ship data. On the other hand, operational real-time analysis of seismic/geodetic data would be carried out for estimating a tsunamigenic fault model. Incorporating the seismic/geodetic fault model estimation into the tsunami forecast above possibly compensates for the deteriorated forecast

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

A simple Lagrangian forecast system with aviation forecast potential

NASA Technical Reports Server (NTRS)

Petersen, R. A.; Homan, J. H.

1983-01-01

A trajectory forecast procedure is developed which uses geopotential tendency fields obtained from a simple, multiple layer, potential vorticity conservative isentropic model. This model can objectively account for short-term advective changes in the mass field when combined with fine-scale initial analyses. This procedure for producing short-term, upper-tropospheric trajectory forecasts employs a combination of a detailed objective analysis technique, an efficient mass advection model, and a diagnostically proven trajectory algorithm, none of which require extensive computer resources. Results of initial tests are presented, which indicate an exceptionally good agreement for trajectory paths entering the jet stream and passing through an intensifying trough. It is concluded that this technique not only has potential for aiding in route determination, fuel use estimation, and clear air turbulence detection, but also provides an example of the types of short range forecasting procedures which can be applied at local forecast centers using simple algorithms and a minimum of computer resources.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

PubMed

Brownstein, John S; Chu, Shuyu; Marathe, Achla; Marathe, Madhav V; Nguyen, Andre T; Paolotti, Daniela; Perra, Nicola; Perrotta, Daniela; Santillana, Mauricio; Swarup, Samarth; Tizzoni, Michele; Vespignani, Alessandro; Vullikanti, Anil Kumar S; Wilson, Mandy L; Zhang, Qian

2017-11-01

Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates

A Simulation Optimization Approach to Epidemic Forecasting

PubMed Central

Nsoesie, Elaine O.; Beckman, Richard J.; Shashaani, Sara; Nagaraj, Kalyani S.; Marathe, Madhav V.

2013-01-01

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area. PMID:23826222

A Simulation Optimization Approach to Epidemic Forecasting.

PubMed

Nsoesie, Elaine O; Beckman, Richard J; Shashaani, Sara; Nagaraj, Kalyani S; Marathe, Madhav V

2013-01-01

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

NASA Astrophysics Data System (ADS)

De Felice, M.; Alessandri, A.; Ruti, P.

2012-12-01

Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate

Designing Writing Programs in Business and Industry.

ERIC Educational Resources Information Center

Freed, Richard C.

Current training in writing for business and industry usually takes the form of short courses. However, the short course is an inappropriate way to teach writing because it is inefficient, represents writing behaviors or strategies inappropriate for some writers, rarely allows time for adequate criticism and revision, presents too much material in…

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.

Resources and Long-Range Forecasts

ERIC Educational Resources Information Center

Smith, Waldo E.

1973-01-01

The author argues that forecasts of quick depletion of resources in the environment as a result of overpopulation and increased usage may not be free from error. Ignorance still exists in understanding the recovery mechanisms of nature. Long-range forecasts are likely to be wrong in such situations. (PS)

Who Owns My Words? Intellectual Property Rights as a Business Issue

ERIC Educational Resources Information Center

Jameson, Daphne A.

2011-01-01

Most college faculty approach plagiarism as a moral issue: a violation of the rules of the university and a violation of the behavioral standards of the academic world. However, business communication faculty can enhance students' educations by approaching plagiarism as one aspect of a larger business issue: the protection of intellectual…

Financial forecasts accuracy in Brazil's social security system.

PubMed

Silva, Carlos Patrick Alves da; Puty, Claudio Alberto Castelo Branco; Silva, Marcelino Silva da; Carvalho, Solon Venâncio de; Francês, Carlos Renato Lisboa

2017-01-01

Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for optimal policy decisions. These forecasts are being used as support for the government's proposed pension reform that plans to radically change the Brazilian Constitution insofar as Social Security is concerned. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This paper aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. We base our research on an empirical and probabilistic analysis of the official models. Our empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. Moreover, the low level of transparency in the methods impaired the replication of results published by the Brazilian Government and the use of outdated data compromises forecast results. In the theoretical analysis, based on a mathematical modeling approach, we discuss the complexity and limitations of the macroeconomic forecast through the computation of confidence intervals. We demonstrate the problems related to error measurement inherent to any forecasting process. We then extend this exercise to the computation of confidence intervals for Social Security forecasts. This mathematical exercise raises questions about the degree of reliability of the Social Security forecasts.

NWS Operational Requirements for Ensemble-Based Hydrologic Forecasts

NASA Astrophysics Data System (ADS)

Hartman, R. K.

2008-12-01

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

Pilot Project Technology Business Case: Mobile Work Packages

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

Thomas, Ken; Lawrie, Sean; Niedermuller, Josef

Performance advantages of the new pilot project technologies are widely acknowledged, but it has proven difficult for utilities to derive business cases for justifying investment in these new capabilities. Lack of a business case is often cited by utilities as a barrier to pursuing wide-scale application of digital technologies to nuclear plant work activities. The decision to move forward with funding usually hinges on demonstrating actual cost reductions that can be credited to budgets and thereby truly reduce O&M or capital costs. Technology enhancements, while enhancing work methods and making work more efficient, often fail to eliminate workload such thatmore » it changes overall staffing and material cost requirements. It is critical to demonstrate cost reductions or impacts on non-cost performance objectives in order for the business case to justify investment by nuclear operators. The Business Case Methodology (BCM) was developed in September of 2015 to frame the benefit side of II&C technologies to address the “benefit” side of the analysis—as opposed to the cost side—and how the organization evaluates discretionary projects (net present value (NPV), accounting effects of taxes, discount rates, etc.). The cost and analysis side is not particularly difficult for the organization and can usually be determined with a fair amount of precision (not withstanding implementation project cost overruns). It is in determining the “benefits” side of the analysis that utilities have more difficulty in technology projects and that is the focus of this methodology. The methodology is presented in the context of the entire process, but the tool provided is limited to determining the organizational benefits only. This report describes a the use of the BCM in building a business case for mobile work packages, which includes computer-based procedures and other automated elements of a work package. Key to those impacts will be identifying where the savings are �

Teaching ocean wave forecasting using computer-generated visualization and animation—Part 1: sea forecasting

NASA Astrophysics Data System (ADS)

Whitford, Dennis J.

2002-05-01

Ocean waves are the most recognized phenomena in oceanography. Unfortunately, undergraduate study of ocean wave dynamics and forecasting involves mathematics and physics and therefore can pose difficulties with some students because of the subject's interrelated dependence on time and space. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Computer-generated visualization and animation offer a visually intuitive and pedagogically sound medium to present geoscience, yet there are very few oceanographic examples. A two-part article series is offered to explain ocean wave forecasting using computer-generated visualization and animation. This paper, Part 1, addresses forecasting of sea wave conditions and serves as the basis for the more difficult topic of swell wave forecasting addressed in Part 2. Computer-aided visualization and animation, accompanied by oral explanation, are a welcome pedagogical supplement to more traditional methods of instruction. In this article, several MATLAB ® software programs have been written to visualize and animate development and comparison of wave spectra, wave interference, and forecasting of sea conditions. These programs also set the stage for the more advanced and difficult animation topics in Part 2. The programs are user-friendly, interactive, easy to modify, and developed as instructional tools. By using these software programs, teachers can enhance their instruction of these topics with colorful visualizations and animation without requiring an extensive background in computer programming.

Setting up your own business. Facing the future as an entrepreneur.

PubMed

Brent, N J

1990-01-01

Other areas of setting up and running a business also are important to explore, especially if the business plans to use employees. You will become an employer, and you must be familiar with rules and regulations that include areas such as the employee's right to a safe workplace, worker's compensation laws, unemployment compensation laws and tax liabilities, antidiscrimination laws, and wage and tax laws. If independent contractors are going to be used, you must recognize that well-developed contracts are a necessity. If you are going to market a new product, consult with an attorney whose practice concentrates in trademark and patent law before the product is shared with others. Being well informed about the proposed business venture, not only before its establishment but as it develops and grows, can help you be in the best position to have a successful business.

Tell It as It Is, Business Education: 7713.13.

ERIC Educational Resources Information Center

Davis, Thelma B.

In the area of business education, this pamphlet describes a course in the methods and arts of business communications, stressing oral, written, and visual proficiency in telephone techniques, letters, telegrams, telefax, posters, memos, and the interpretation of business charts and graphs. Described specifically are course guidelines, performance…

Solar and Wind Forecasting | Grid Modernization | NREL

Science.gov Websites

and Wind Forecasting Solar and Wind Forecasting As solar and wind power become more common system operators. An aerial photo of the National Wind Technology Center's PV arrays. Capabilities value of accurate forecasting Wind power visualization to direct questions and feedback during industry

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.

Students' Evaluations of a Business Simulation Game as a Learning Experience.

ERIC Educational Resources Information Center

Edwards, Keith J.

This report investigates student evaluations of a business simulation game as a learning experience in terms of specific claims which have been made for this kind of teaching technique. Ninety-nine junior college students in introductory business courses answered a questionnaire after playing the fame as an ongoing, semester-long activity. The…

Historic CH4 Records from Antarctic and Greenland Ice Cores, Antarctic Firn Data, and Archived Air Samples from Cape Grim, Tasmania

DOE Data Explorer

Etheridge, D. M. [Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia; Steele, L. P. [Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia; Francey, R. J. [Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia; Langenfelds, R. L. [Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia

2002-01-01

The Antarctic CH4 records presented here are derived from three ice cores obtained at Law Dome, East Antarctica (66°44'S, 112°50'E, 1390 meters above mean sea level). Law Dome has many qualities of an ideal ice core site for the reconstruction of past concentrations of atmospheric gases; these qualities include: negligible melting of the ice sheet surface, low concentrations of impurities, regular stratigraphic layering undisturbed by wind stress at the surface or differential ice flow at depth, and a high snow accumulation rate. Further details on the site, drilling, and cores are provided by Etheridge et al. (1998), Etheridge et al. (1996), Etheridge and Wookey (1989), and Morgan et al. (1997). The two Greenland ice cores are from the Summit region (72°34' N, 37°37' W, 3200 meters above mean sea level). Lower snow accumulation rate there results in lower air-age resolution, and measurements presented here cover only the pre-industrial period (until 1885). More details about these measurements are presented in Etheridge et al. (1998). Additionally, this site contains firn data from Core DE08-2, and archived air samples from Cape Grim, Tasmania, for comparison.

Social Indicators and Social Forecasting.

ERIC Educational Resources Information Center

Johnston, Denis F.

The paper identifies major types of social indicators and explains how they can be used in social forecasting. Social indicators are defined as statistical measures relating to major areas of social concern and/or individual well being. Examples of social indicators are projections, forecasts, outlook statements, time-series statistics, and…

Empirical prediction intervals improve energy forecasting

PubMed Central

Kaack, Lynn H.; Apt, Jay; Morgan, M. Granger; McSharry, Patrick

2017-01-01

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks. PMID:28760997

The Alliance in Motivational Enhancement Therapy and Counseling as Usual for Substance Use Problems

ERIC Educational Resources Information Center

Crits-Christoph, Paul; Gallop, Robert; Temes, Christina M.; Woody, George; Ball, Samuel A.; Martino, Steve; Carroll, Kathleen M.

2009-01-01

Data from a community-based multicenter study of motivational enhancement therapy (MET) and counseling as usual (CAU) for outpatient substance users were used to examine questions about the role of the alliance in MET and CAU. Most (94%) of the sample met diagnostic criteria for abuse or dependence (primarily alcohol and/or cocaine). Sixteen…

Space Weather Forecasting at IZMIRAN

NASA Astrophysics Data System (ADS)

Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

2017-12-01

Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

The Betting Odds Rating System: Using soccer forecasts to forecast soccer.

PubMed

Wunderlich, Fabian; Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.

The Betting Odds Rating System: Using soccer forecasts to forecast soccer

PubMed Central

Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods. PMID:29870554

Re-Visioning Science "Love and Passion in the Scientific Imagination": Art and Science

ERIC Educational Resources Information Center

Lunn, M.; Noble, A.

2008-01-01

The often portrayed media image of the scientist is a rather strange one, grim-looking scientists, usually male, poised beside incomprehensible instruments. It is little wonder that we encounter the stereotype of the bespectacled scientist; thick black rims, coke bottle lenses, roman sandals, dressed in lab coats, trousers up to their necks. A…

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

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

Digital Platforms as Factor Transforming Management Models in Businesses and Industries

NASA Astrophysics Data System (ADS)

Dimitrakiev, D.; Molodchik, A. V.

2018-05-01

Increasingly, digital platforms are built into the value chain, acting as an intermediary between the manufacturer and the consumer. The paper presents tendencies and features of business model transformation in connection with management of the new digital technologies. The limitations of traditional business models and the capabilities of business models based on digital platforms and self-organization were revealed. In the study, the viability of the new business model for the dental industry was confirmed and the new concept of the branch self-organizing control system based on the information platform, blockchain, cryptocurrency and reward of target consumer is offered, including mechanisms that make the model attractive for both the consumer and the service provider.

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Does Time Spent Online Have an Influence on Student Performance? Evidence for a Large Business Studies Class

ERIC Educational Resources Information Center

Korkofingas, Con; Macri, Joseph

2013-01-01

This paper examines, using regression modelling, whether a statistically significant relationship exists between the time spent by a student using the course website and the student's assessment performance for a large third year university business forecasting course. We utilise the online tracking system in Blackboard, a web-based software…

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

PubMed

Jones, Stephen; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

2016-12-01

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

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

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Seasonal forecasting of discharge for the Raccoon River, Iowa

NASA Astrophysics Data System (ADS)

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

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

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.

Municipal water consumption forecast accuracy

NASA Astrophysics Data System (ADS)

Fullerton, Thomas M.; Molina, Angel L.

2010-06-01