The Significance of an Excess in a Counting Experiment: Assessing the Impact of Systematic Uncertainties and the Case with a Gaussian Background

NASA Astrophysics Data System (ADS)

Vianello, Giacomo

2018-05-01

Several experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect (“on” measurement) is contrasted with a background-only observation free of the effect (“off” measurement). In counting experiments, the significance of the new source or effect can be estimated with a widely used formula from Li & Ma, which assumes that both measurements are Poisson random variables. In this paper we study three other cases: (i) the ideal case where the background measurement has no uncertainty, which can be used to study the maximum sensitivity that an instrument can achieve, (ii) the case where the background estimate b in the off measurement has an additional systematic uncertainty, and (iii) the case where b is a Gaussian random variable instead of a Poisson random variable. The latter case applies when b comes from a model fitted on archival or ancillary data, or from the interpolation of a function fitted on data surrounding the candidate new source/effect. Practitioners typically use a formula that is only valid when b is large and when its uncertainty is very small, while we derive a general formula that can be applied in all regimes. We also develop simple methods that can be used to assess how much an estimate of significance is sensitive to systematic uncertainties on the efficiency or on the background. Examples of applications include the detection of short gamma-ray bursts and of new X-ray or γ-ray sources. All the techniques presented in this paper are made available in a Python code that is ready to use.

Propagation of stage measurement uncertainties to streamflow time series

NASA Astrophysics Data System (ADS)

Horner, Ivan; Le Coz, Jérôme; Renard, Benjamin; Branger, Flora; McMillan, Hilary

2016-04-01

Streamflow uncertainties due to stage measurements errors are generally overlooked in the promising probabilistic approaches that have emerged in the last decade. We introduce an original error model for propagating stage uncertainties through a stage-discharge rating curve within a Bayesian probabilistic framework. The method takes into account both rating curve (parametric errors and structural errors) and stage uncertainty (systematic and non-systematic errors). Practical ways to estimate the different types of stage errors are also presented: (1) non-systematic errors due to instrument resolution and precision and non-stationary waves and (2) systematic errors due to gauge calibration against the staff gauge. The method is illustrated at a site where the rating-curve-derived streamflow can be compared with an accurate streamflow reference. The agreement between the two time series is overall satisfying. Moreover, the quantification of uncertainty is also satisfying since the streamflow reference is compatible with the streamflow uncertainty intervals derived from the rating curve and the stage uncertainties. Illustrations from other sites are also presented. Results are much contrasted depending on the site features. In some cases, streamflow uncertainty is mainly due to stage measurement errors. The results also show the importance of discriminating systematic and non-systematic stage errors, especially for long term flow averages. Perspectives for improving and validating the streamflow uncertainty estimates are eventually discussed.

Climateprediction.com: Public Involvement, Multi-Million Member Ensembles and Systematic Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Stainforth, D. A.; Allen, M.; Kettleborough, J.; Collins, M.; Heaps, A.; Stott, P.; Wehner, M.

2001-12-01

The climateprediction.com project is preparing to carry out the first systematic uncertainty analysis of climate forecasts using large ensembles of GCM climate simulations. This will be done by involving schools, businesses and members of the public, and utilizing the novel technology of distributed computing. Each participant will be asked to run one member of the ensemble on their PC. The model used will initially be the UK Met Office's Unified Model (UM). It will be run under Windows and software will be provided to enable those involved to view their model output as it develops. The project will use this method to carry out large perturbed physics GCM ensembles and thereby analyse the uncertainty in the forecasts from such models. Each participant/ensemble member will therefore have a version of the UM in which certain aspects of the model physics have been perturbed from their default values. Of course the non-linear nature of the system means that it will be necessary to look not just at perturbations to individual parameters in specific schemes, such as the cloud parameterization, but also to the many combinations of perturbations. This rapidly leads to the need for very large, perhaps multi-million member ensembles, which could only be undertaken using the distributed computing methodology. The status of the project will be presented and the Windows client will be demonstrated. In addition, initial results will be presented from beta test runs using a demo release for Linux PCs and Alpha workstations. Although small by comparison to the whole project, these pilot results constitute a 20-50 member perturbed physics climate ensemble with results indicating how climate sensitivity can be substantially affected by individual parameter values in the cloud scheme.

Mitigating Provider Uncertainty in Service Provision Contracts

NASA Astrophysics Data System (ADS)

Smith, Chris; van Moorsel, Aad

Uncertainty is an inherent property of open, distributed and multiparty systems. The viability of the mutually beneficial relationships which motivate these systems relies on rational decision-making by each constituent party under uncertainty. Service provision in distributed systems is one such relationship. Uncertainty is experienced by the service provider in his ability to deliver a service with selected quality level guarantees due to inherent non-determinism, such as load fluctuations and hardware failures. Statistical estimators utilized to model this non-determinism introduce additional uncertainty through sampling error. Inability of the provider to accurately model and analyze uncertainty in the quality level guarantees can result in the formation of sub-optimal service provision contracts. Emblematic consequences include loss of revenue, inefficient resource utilization and erosion of reputation and consumer trust. We propose a utility model for contract-based service provision to provide a systematic approach to optimal service provision contract formation under uncertainty. Performance prediction methods to enable the derivation of statistical estimators for quality level are introduced, with analysis of their resultant accuracy and cost.

Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at s = 13 TeV with the ATLAS detector

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

Aaboud, M.

Jet energy scale measurements and their systematic uncertainties are reported for jets measured with the ATLAS detector using proton-proton collision data with a center-of-mass energy of √ s = 13 TeV , corresponding to an integrated luminosity of 3.2 fb -1 collected during 2015 at the LHC. Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells, using the anti- k t algorithm with radius parameter R = 0.4 . We calibrate jets with a series of simulation-based corrections and in situ techniques. In situ techniques exploit the transverse momentum balance between a jet and a reference objectmore » such as a photon, Z boson, or multijet system for jets with 20 < p T < 2000 GeV and pseudorapidities of | η | < 4.5 , using both data and simulation. An uncertainty in the jet energy scale of less than 1% is found in the central calorimeter region ( | η | < 1.2 ) for jets with 100 < p T < 500 GeV . An uncertainty of about 4.5% is found for low- p T jets with p T = 20 GeV in the central region, dominated by uncertainties in the corrections for multiple proton-proton interactions. The calibration of forward jets ( | η | > 0.8 ) is derived from dijet p T balance measurements. Furthermore, for jets of p T = 80 GeV , the additional uncertainty for the forward jet calibration reaches its largest value of about 2% in the range | η | > 3.5 and in a narrow slice of 2.2 < | η | < 2.4 .« less

Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at s = 13 TeV with the ATLAS detector

DOE PAGES

Aaboud, M.

2017-10-13

Jet energy scale measurements and their systematic uncertainties are reported for jets measured with the ATLAS detector using proton-proton collision data with a center-of-mass energy of √ s = 13 TeV , corresponding to an integrated luminosity of 3.2 fb -1 collected during 2015 at the LHC. Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells, using the anti- k t algorithm with radius parameter R = 0.4 . We calibrate jets with a series of simulation-based corrections and in situ techniques. In situ techniques exploit the transverse momentum balance between a jet and a reference objectmore » such as a photon, Z boson, or multijet system for jets with 20 < p T < 2000 GeV and pseudorapidities of | η | < 4.5 , using both data and simulation. An uncertainty in the jet energy scale of less than 1% is found in the central calorimeter region ( | η | < 1.2 ) for jets with 100 < p T < 500 GeV . An uncertainty of about 4.5% is found for low- p T jets with p T = 20 GeV in the central region, dominated by uncertainties in the corrections for multiple proton-proton interactions. The calibration of forward jets ( | η | > 0.8 ) is derived from dijet p T balance measurements. Furthermore, for jets of p T = 80 GeV , the additional uncertainty for the forward jet calibration reaches its largest value of about 2% in the range | η | > 3.5 and in a narrow slice of 2.2 < | η | < 2.4 .« less

Jet energy scale measurements and their systematic uncertainties in proton-proton collisions at √{s }=13 TeV with the ATLAS detector

NASA Astrophysics Data System (ADS)

Aaboud, M.; Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagnaia, P.; Bahrasemani, H.; Baines, J. T.; Bajic, M.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska-Blenessy, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beermann, T. A.; Begalli, M.; Begel, M.; Behr, J. K.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernardi, G.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethani, A.; Bethke, S.; Bevan, A. J.; Beyer, J.; Bianchi, R. M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; Bilbao de Mendizabal, J.; Billoud, T. R. V.; Bilokon, H.; Bindi, M.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bittrich, C.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blue, A.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bolz, A. E.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Briglin, D. L.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruni, A.; Bruni, G.; Bruni, L. S.; Brunt, Bh; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burger, A. M.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Burr, J. T. P.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Cano Bret, M.; Cantero, J.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carlson, B. T.; Carminati, L.; Carney, R. M. D.; Caron, S.; Carquin, E.; Carrá, S.; Carrillo-Montoya, G. D.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castelijn, R.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Celebi, E.; Ceradini, F.; Cerda Alberich, L.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, W. S.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chiu, Y. H.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Christodoulou, V.; Chromek-Burckhart, D.; Chu, M. C.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocca, C.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Colombo, T.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Constantinescu, S.; Conti, G.; Conventi, F.; Cooke, M.; Cooper-Sarkar, A. M.; Cormier, F.; Cormier, K. J. R.; Corradi, M.; Corriveau, F.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Creager, R. A.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cueto, A.; Cuhadar Donszelmann, T.; Cukierman, A. R.; Cummings, J.; Curatolo, M.; Cúth, J.; Czirr, H.; Czodrowski, P.; D'Amen, G.; D'Auria, S.; D'Onofrio, M.; da Cunha Sargedas de Sousa, M. J.; da Via, C.; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Daubney, T.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Dawe, E.; Dawson, I.; de, K.; de Asmundis, R.; de Benedetti, A.; de Castro, S.; de Cecco, S.; de Groot, N.; de Jong, P.; de la Torre, H.; de Lorenzi, F.; de Maria, A.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vasconcelos Corga, K.; de Vivie de Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delporte, C.; Delsart, P. A.; Demarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; di Bello, F. A.; di Ciaccio, A.; di Ciaccio, L.; di Clemente, W. K.; di Donato, C.; di Girolamo, A.; di Girolamo, B.; di Micco, B.; di Nardo, R.; di Petrillo, K. F.; di Simone, A.; di Sipio, R.; di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Díez Cornell, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; Do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; El Kosseifi, R.; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Escobar, C.; Esposito, B.; Estrada Pastor, O.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Faucci Giannelli, M.; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenton, M. J.; Fenyuk, A. B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferreira de Lima, D. E.; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Förster, F. A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Franchino, S.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Freund, B.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Ganguly, S.; Gao, J.; Gao, Y.; Gao, Y. S.; Garay Walls, F. M.; García, C.; García Navarro, J. E.; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gascon Bravo, A.; Gasnikova, K.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gee, C. N. P.; Geisen, J.; Geisen, M.; Geisler, M. P.; Gellerstedt, K.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; Gentsos, C.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giannetti, P.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Goncalves Gama, R.; Goncalves Pinto Firmino da Costa, J.; Gonella, G.; Gonella, L.; Gongadze, A.; González de La Hoz, S.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. 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L.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Pluth, D.; Podberezko, P.; Poettgen, R.; Poggi, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Ponomarenko, D.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Poulard, G.; Poulsen, T.; Poveda, J.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proklova, N.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puri, A.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rangel-Smith, C.; Rashid, T.; Raspopov, S.; Ratti, M. G.; Rauch, D. M.; Rauscher, F.; Rave, S.; Ravinovich, I.; Rawling, J. H.; Raymond, M.; Read, A. L.; Readioff, N. P.; Reale, M.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reed, R. G.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reiss, A.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Resseguie, E. D.; Rettie, S.; Reynolds, E.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ripellino, G.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Roberts, R. T.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Rocco, E.; Roda, C.; Rodina, Y.; Rodriguez Bosca, S.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; Røhne, O.; Roloff, J.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Rosati, S.; Rosbach, K.; Rose, P.; Rosien, N.-A.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salazar Loyola, J. E.; Salek, D.; Sales de Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sánchez, J.; Sanchez Martinez, V.; Sanchez Pineda, A.; Sandaker, H.; Sandbach, R. L.; Sander, C. O.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Santoyo Castillo, I.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sato, K.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sideras Haddad, E.; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Siral, I.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, J. W.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sopczak, A.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spieker, T. M.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Stark, S. H.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Suruliz, K.; Suster, C. J. E.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Swift, S. P.; Sykora, I.; Sykora, T.; Ta, D.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takasugi, E. H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teixeira-Dias, P.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turgeman, D.; Turk Cakir, I.; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valdes Santurio, E.; Valentinetti, S.; Valero, A.; Valéry, L.; Valkar, S.; Vallier, A.; Valls Ferrer, J. A.; van den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vetterli, M. C.; Viaux Maira, N.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamatani, M.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, D. R.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration

2017-10-01

Jet energy scale measurements and their systematic uncertainties are reported for jets measured with the ATLAS detector using proton-proton collision data with a center-of-mass energy of √{s }=13 TeV , corresponding to an integrated luminosity of 3.2 fb-1 collected during 2015 at the LHC. Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells, using the anti-kt algorithm with radius parameter R =0.4 . Jets are calibrated with a series of simulation-based corrections and in situ techniques. In situ techniques exploit the transverse momentum balance between a jet and a reference object such as a photon, Z boson, or multijet system for jets with 20 uncertainty in the jet energy scale of less than 1% is found in the central calorimeter region (|η |<1.2 ) for jets with 100 uncertainty of about 4.5% is found for low-pT jets with pT=20 GeV in the central region, dominated by uncertainties in the corrections for multiple proton-proton interactions. The calibration of forward jets (|η |>0.8 ) is derived from dijet pT balance measurements. For jets of pT=80 GeV , the additional uncertainty for the forward jet calibration reaches its largest value of about 2% in the range |η |>3.5 and in a narrow slice of 2.2 <|η |<2.4 .

Systematic evaluation of an atomic clock at 2 × 10−18 total uncertainty

PubMed Central

Nicholson, T.L.; Campbell, S.L.; Hutson, R.B.; Marti, G.E.; Bloom, B.J.; McNally, R.L.; Zhang, W.; Barrett, M.D.; Safronova, M.S.; Strouse, G.F.; Tew, W.L.; Ye, J.

2015-01-01

The pursuit of better atomic clocks has advanced many research areas, providing better quantum state control, new insights in quantum science, tighter limits on fundamental constant variation and improved tests of relativity. The record for the best stability and accuracy is currently held by optical lattice clocks. Here we take an important step towards realizing the full potential of a many-particle clock with a state-of-the-art stable laser. Our 87Sr optical lattice clock now achieves fractional stability of 2.2 × 10−16 at 1 s. With this improved stability, we perform a new accuracy evaluation of our clock, reducing many systematic uncertainties that limited our previous measurements, such as those in the lattice ac Stark shift, the atoms' thermal environment and the atomic response to room-temperature blackbody radiation. Our combined measurements have reduced the total uncertainty of the JILA Sr clock to 2.1 × 10−18 in fractional frequency units. PMID:25898253

Transportable Optical Lattice Clock with 7×10^{-17} Uncertainty.

PubMed

Koller, S B; Grotti, J; Vogt, St; Al-Masoudi, A; Dörscher, S; Häfner, S; Sterr, U; Lisdat, Ch

2017-02-17

We present a transportable optical clock (TOC) with ^{87}Sr. Its complete characterization against a stationary lattice clock resulted in a systematic uncertainty of 7.4×10^{-17}, which is currently limited by the statistics of the determination of the residual lattice light shift, and an instability of 1.3×10^{-15}/sqrt[τ] with an averaging time τ in seconds. Measurements confirm that the systematic uncertainty can be reduced to below the design goal of 1×10^{-17}. To our knowledge, these are the best uncertainties and instabilities reported for any transportable clock to date. For autonomous operation, the TOC has been installed in an air-conditioned car trailer. It is suitable for chronometric leveling with submeter resolution as well as for intercontinental cross-linking of optical clocks, which is essential for a redefinition of the International System of Units (SI) second. In addition, the TOC will be used for high precision experiments for fundamental science that are commonly tied to precise frequency measurements and its development is an important step to space-borne optical clocks.

Optimal design and uncertainty quantification in blood flow simulations for congenital heart disease

NASA Astrophysics Data System (ADS)

Marsden, Alison

2009-11-01

Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. Recent advances include increasingly complex geometries, physiological flow conditions, and fluid structure interaction. However inputs to these simulations, including medical image data, catheter-derived pressures and material properties, can have significant uncertainties associated with them. For simulations to predict clinically useful and reliable output information, it is necessary to quantify the effects of input uncertainties on outputs of interest. In addition, blood flow simulation tools can now be efficiently coupled to shape optimization algorithms for surgery design applications, and these tools should incorporate uncertainty information. We present a unified framework to systematically and efficient account for uncertainties in simulations using adaptive stochastic collocation. In addition, we present a framework for derivative-free optimization of cardiovascular geometries, and layer these tools to perform optimization under uncertainty. These methods are demonstrated using simulations and surgery optimization to improve hemodynamics in pediatric cardiology applications.

Assessing uncertainties in land cover projections.

PubMed

Alexander, Peter; Prestele, Reinhard; Verburg, Peter H; Arneth, Almut; Baranzelli, Claudia; Batista E Silva, Filipe; Brown, Calum; Butler, Adam; Calvin, Katherine; Dendoncker, Nicolas; Doelman, Jonathan C; Dunford, Robert; Engström, Kerstin; Eitelberg, David; Fujimori, Shinichiro; Harrison, Paula A; Hasegawa, Tomoko; Havlik, Petr; Holzhauer, Sascha; Humpenöder, Florian; Jacobs-Crisioni, Chris; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Lavalle, Carlo; Lenton, Tim; Liu, Jiayi; Meiyappan, Prasanth; Popp, Alexander; Powell, Tom; Sands, Ronald D; Schaldach, Rüdiger; Stehfest, Elke; Steinbuks, Jevgenijs; Tabeau, Andrzej; van Meijl, Hans; Wise, Marshall A; Rounsevell, Mark D A

2017-02-01

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover. © 2016 John Wiley & Sons Ltd.

Additional challenges for uncertainty analysis in river engineering

NASA Astrophysics Data System (ADS)

Berends, Koen; Warmink, Jord; Hulscher, Suzanne

2016-04-01

The management of rivers for improving safety, shipping and environment requires conscious effort on the part of river managers. River engineers design hydraulic works to tackle various challenges, from increasing flow conveyance to ensuring minimal water depths for environmental flow and inland shipping. Last year saw the completion of such large scale river engineering in the 'Room for the River' programme for the Dutch Rhine River system, in which several dozen of human interventions were built to increase flood safety. Engineering works in rivers are not completed in isolation from society. Rather, their benefits - increased safety, landscaping beauty - and their disadvantages - expropriation, hindrance - directly affect inhabitants. Therefore river managers are required to carefully defend their plans. The effect of engineering works on river dynamics is being evaluated using hydraulic river models. Two-dimensional numerical models based on the shallow water equations provide the predictions necessary to make decisions on designs and future plans. However, like all environmental models, these predictions are subject to uncertainty. In recent years progress has been made in the identification of the main sources of uncertainty for hydraulic river models. Two of the most important sources are boundary conditions and hydraulic roughness (Warmink et al. 2013). The result of these sources of uncertainty is that the identification of single, deterministic prediction model is a non-trivial task. This is this is a well-understood problem in other fields as well - most notably hydrology - and known as equifinality. However, the particular case of human intervention modelling with hydraulic river models compounds the equifinality case. The model that provides the reference baseline situation is usually identified through calibration and afterwards modified for the engineering intervention. This results in two distinct models, the evaluation of which yields the effect of

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, Vivianaluxa; Vienna, John D.; Kim, Dong-Sang

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints (Vienna et al. 2016), and uncertainty descriptions on projected Hanford glass mass. The maximum allowable WOL was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of IHLW glass when no uncertainties were taken into accound. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increase in estimatedmore » glass mass 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). ILAW mass was predicted to be 282,350 MT without uncertainty and with weaste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MTG. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, V.; Kim, D. S.; Vienna, J. D.

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints, and uncertainty descriptions on projected Hanford glass mass. The maximum allowable waste oxide loading (WOL) was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of immobilized high-level waste (IHLW) glass when no uncertainties were taken into account. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increasemore » in estimated glass mass of 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). The immobilized low-activity waste (ILAW) mass was predicted to be 282,350 MT without uncertainty and with waste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MT. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

Determination of electron beam polarization using electron detector in Compton polarimeter with less than 1% statistical and systematic uncertainty

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

Narayan, Amrendra

2015-05-01

The Q-weak experiment aims to measure the weak charge of proton with a precision of 4.2%. The proposed precision on weak charge required a 2.5% measurement of the parity violating asymmetry in elastic electron - proton scattering. Polarimetry was the largest experimental contribution to this uncertainty and a new Compton polarimeter was installed in Hall C at Jefferson Lab to make the goal achievable. In this polarimeter the electron beam collides with green laser light in a low gain Fabry-Perot Cavity; the scattered electrons are detected in 4 planes of a novel diamond micro strip detector while the back scatteredmore » photons are detected in lead tungstate crystals. This diamond micro-strip detector is the first such device to be used as a tracking detector in a nuclear and particle physics experiment. The diamond detectors are read out using custom built electronic modules that include a preamplifier, a pulse shaping amplifier and a discriminator for each detector micro-strip. We use field programmable gate array based general purpose logic modules for event selection and histogramming. Extensive Monte Carlo simulations and data acquisition simulations were performed to estimate the systematic uncertainties. Additionally, the Moller and Compton polarimeters were cross calibrated at low electron beam currents using a series of interleaved measurements. In this dissertation, we describe all the subsystems of the Compton polarimeter with emphasis on the electron detector. We focus on the FPGA based data acquisition system built by the author and the data analysis methods implemented by the author. The simulations of the data acquisition and the polarimeter that helped rigorously establish the systematic uncertainties of the polarimeter are also elaborated, resulting in the first sub 1% measurement of low energy (?1 GeV) electron beam polarization with a Compton electron detector. We have demonstrated that diamond based micro-strip detectors can be used for

Determination of electron beam polarization using electron detector in Compton polarimeter with less than 1% statistical and systematic uncertainty

NASA Astrophysics Data System (ADS)

Narayan, Amrendra

The Q-weak experiment aims to measure the weak charge of proton with a precision of 4.2%. The proposed precision on weak charge required a 2.5% measurement of the parity violating asymmetry in elastic electron - proton scattering. Polarimetry was the largest experimental contribution to this uncertainty and a new Compton polarimeter was installed in Hall C at Jefferson Lab to make the goal achievable. In this polarimeter the electron beam collides with green laser light in a low gain Fabry-Perot Cavity; the scattered electrons are detected in 4 planes of a novel diamond micro strip detector while the back scattered photons are detected in lead tungstate crystals. This diamond micro-strip detector is the first such device to be used as a tracking detector in a nuclear and particle physics experiment. The diamond detectors are read out using custom built electronic modules that include a preamplifier, a pulse shaping amplifier and a discriminator for each detector micro-strip. We use field programmable gate array based general purpose logic modules for event selection and histogramming. Extensive Monte Carlo simulations and data acquisition simulations were performed to estimate the systematic uncertainties. Additionally, the Moller and Compton polarimeters were cross calibrated at low electron beam currents using a series of interleaved measurements. In this dissertation, we describe all the subsystems of the Compton polarimeter with emphasis on the electron detector. We focus on the FPGA based data acquisition system built by the author and the data analysis methods implemented by the author. The simulations of the data acquisition and the polarimeter that helped rigorously establish the systematic uncertainties of the polarimeter are also elaborated, resulting in the first sub 1% measurement of low energy (~1GeV) electron beam polarization with a Compton electron detector. We have demonstrated that diamond based micro-strip detectors can be used for tracking in a

Control of systematic uncertainties in the storage ring search for an electric dipole moment by measuring the electric quadrupole moment

NASA Astrophysics Data System (ADS)

Magiera, Andrzej

2017-09-01

Measurements of electric dipole moment (EDM) for light hadrons with use of a storage ring have been proposed. The expected effect is very small, therefore various subtle effects need to be considered. In particular, interaction of particle's magnetic dipole moment and electric quadrupole moment with electromagnetic field gradients can produce an effect of a similar order of magnitude as that expected for EDM. This paper describes a very promising method employing an rf Wien filter, allowing to disentangle that contribution from the genuine EDM effect. It is shown that both these effects could be separated by the proper setting of the rf Wien filter frequency and phase. In the EDM measurement the magnitude of systematic uncertainties plays a key role and they should be under strict control. It is shown that particles' interaction with field gradients offers also the possibility to estimate global systematic uncertainties with the precision necessary for an EDM measurement with the planned accuracy.

Transportable Optical Lattice Clock with 7 ×10-17 Uncertainty

NASA Astrophysics Data System (ADS)

Koller, S. B.; Grotti, J.; Vogt, St.; Al-Masoudi, A.; Dörscher, S.; Häfner, S.; Sterr, U.; Lisdat, Ch.

2017-02-01

We present a transportable optical clock (TOC) with Sr 87 . Its complete characterization against a stationary lattice clock resulted in a systematic uncertainty of 7.4 ×10-17, which is currently limited by the statistics of the determination of the residual lattice light shift, and an instability of 1.3 ×10-15/√{τ } with an averaging time τ in seconds. Measurements confirm that the systematic uncertainty can be reduced to below the design goal of 1 ×10-17. To our knowledge, these are the best uncertainties and instabilities reported for any transportable clock to date. For autonomous operation, the TOC has been installed in an air-conditioned car trailer. It is suitable for chronometric leveling with submeter resolution as well as for intercontinental cross-linking of optical clocks, which is essential for a redefinition of the International System of Units (SI) second. In addition, the TOC will be used for high precision experiments for fundamental science that are commonly tied to precise frequency measurements and its development is an important step to space-borne optical clocks.

Systematic Analysis Of Ocean Colour Uncertainties

NASA Astrophysics Data System (ADS)

Lavender, Samantha

2013-12-01

This paper reviews current research into the estimation of uncertainties as a pixel-based measure to aid non- specialist users of remote sensing products. An example MERIS image, captured on the 28 March 2012, was processed with above-water atmospheric correction code. This was initially based on both the Antoine & Morel Standard Atmospheric Correction, with Bright Pixel correction component, and Doerffer Neural Network coastal water's approach. It's showed that analysis of the atmospheric by-products yield important information about the separation of the atmospheric and in-water signals, helping to sign-post possible uncertainties in the atmospheric correction results. Further analysis has concentrated on implementing a ‘simplistic' atmospheric correction so that the impact of changing the input auxiliary data can be analysed; the influence of changing surface pressure is demonstrated. Future work will focus on automating the analysis, so that the methodology can be implemented within an operational system.

Uncertainty quantification of effective nuclear interactions

DOE PAGES

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

2016-03-02

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

Uncertainty quantification of effective nuclear interactions

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

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

Optimal test selection for prediction uncertainty reduction

DOE PAGES

Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

2016-12-02

Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

Can reduction of uncertainties in cervix cancer brachytherapy potentially improve clinical outcome?

PubMed

Nesvacil, Nicole; Tanderup, Kari; Lindegaard, Jacob C; Pötter, Richard; Kirisits, Christian

2016-09-01

The aim of this study was to quantify the impact of different types and magnitudes of dosimetric uncertainties in cervix cancer brachytherapy (BT) on tumour control probability (TCP) and normal tissue complication probability (NTCP) curves. A dose-response simulation study was based on systematic and random dose uncertainties and TCP/NTCP models for CTV and rectum. Large patient cohorts were simulated assuming different levels of dosimetric uncertainties. TCP and NTCP were computed, based on the planned doses, the simulated dose uncertainty, and an underlying TCP/NTCP model. Systematic uncertainties of 3-20% and random uncertainties with a 5-30% standard deviation per BT fraction were analysed. Systematic dose uncertainties of 5% lead to a 1% decrease/increase of TCP/NTCP, while random uncertainties of 10% had negligible impact on the dose-response curve at clinically relevant dose levels for target and OAR. Random OAR dose uncertainties of 30% resulted in an NTCP increase of 3-4% for planned doses of 70-80Gy EQD2. TCP is robust to dosimetric uncertainties when dose prescription is in the more flat region of the dose-response curve at doses >75Gy. For OARs, improved clinical outcome is expected by reduction of uncertainties via sophisticated dose delivery and treatment verification. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

First Reprocessing of Southern Hemisphere ADditional OZonesondes Profile Records: 3. Uncertainty in Ozone Profile and Total Column

NASA Astrophysics Data System (ADS)

Witte, Jacquelyn C.; Thompson, Anne M.; Smit, Herman G. J.; Vömel, Holger; Posny, Françoise; Stübi, Rene

2018-03-01

Reprocessed ozonesonde data from eight SHADOZ (Southern Hemisphere ADditional OZonesondes) sites have been used to derive the first analysis of uncertainty estimates for both profile and total column ozone (TCO). The ozone uncertainty is a composite of the uncertainties of the individual terms in the ozone partial pressure (PO3) equation, those being the ozone sensor current, background current, internal pump temperature, pump efficiency factors, conversion efficiency, and flow rate. Overall, PO3 uncertainties (ΔPO3) are within 15% and peak around the tropopause (15 ± 3 km) where ozone is a minimum and ΔPO3 approaches the measured signal. The uncertainty in the background and sensor currents dominates the overall ΔPO3 in the troposphere including the tropopause region, while the uncertainties in the conversion efficiency and flow rate dominate in the stratosphere. Seasonally, ΔPO3 is generally a maximum in the March-May, with the exception of SHADOZ sites in Asia, for which the highest ΔPO3 occurs in September-February. As a first approach, we calculate sonde TCO uncertainty (ΔTCO) by integrating the profile ΔPO3 and adding the ozone residual uncertainty, derived from the McPeters and Labow (2012, doi:10.1029/2011JD017006) 1σ ozone mixing ratios. Overall, ΔTCO are within ±15 Dobson units (DU), representing 5-6% of the TCO. Total Ozone Mapping Spectrometer and Ozone Monitoring Instrument (TOMS and OMI) satellite overpasses are generally within the sonde ΔTCO. However, there is a discontinuity between TOMS v8.6 (1998 to September 2004) and OMI (October 2004-2016) TCO on the order of 10 DU that accounts for the significant 16 DU overall difference observed between sonde and TOMS. By comparison, the sonde-OMI absolute difference for the eight stations is only 4 DU.

Quantification of model uncertainty in aerosol optical thickness retrieval from Ozone Monitoring Instrument (OMI) measurements

NASA Astrophysics Data System (ADS)

Määttä, A.; Laine, M.; Tamminen, J.; Veefkind, J. P.

2013-09-01

We study uncertainty quantification in remote sensing of aerosols in the atmosphere with top of the atmosphere reflectance measurements from the nadir-viewing Ozone Monitoring Instrument (OMI). Focus is on the uncertainty in aerosol model selection of pre-calculated aerosol models and on the statistical modelling of the model inadequacies. The aim is to apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness (AOT) retrieval by propagating model selection and model error related uncertainties more realistically. We utilise Bayesian model selection and model averaging methods for the model selection problem and use Gaussian processes to model the smooth systematic discrepancies from the modelled to observed reflectance. The systematic model error is learned from an ensemble of operational retrievals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud free, over land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques. The method is demonstrated with four examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara dessert dust. The presented statistical methodology is general; it is not restricted to this particular satellite retrieval application.

Addressing Systematic Errors in Correlation Tracking on HMI Magnetograms

NASA Astrophysics Data System (ADS)

Mahajan, Sushant S.; Hathaway, David H.; Munoz-Jaramillo, Andres; Martens, Petrus C.

2017-08-01

Correlation tracking in solar magnetograms is an effective method to measure the differential rotation and meridional flow on the solar surface. However, since the tracking accuracy required to successfully measure meridional flow is very high, small systematic errors have a noticeable impact on measured meridional flow profiles. Additionally, the uncertainties of this kind of measurements have been historically underestimated, leading to controversy regarding flow profiles at high latitudes extracted from measurements which are unreliable near the solar limb.Here we present a set of systematic errors we have identified (and potential solutions), including bias caused by physical pixel sizes, center-to-limb systematics, and discrepancies between measurements performed using different time intervals. We have developed numerical techniques to get rid of these systematic errors and in the process improve the accuracy of the measurements by an order of magnitude.We also present a detailed analysis of uncertainties in these measurements using synthetic magnetograms and the quantification of an upper limit below which meridional flow measurements cannot be trusted as a function of latitude.

Uncertainty Propagation in OMFIT

NASA Astrophysics Data System (ADS)

Smith, Sterling; Meneghini, Orso; Sung, Choongki

2017-10-01

A rigorous comparison of power balance fluxes and turbulent model fluxes requires the propagation of uncertainties in the kinetic profiles and their derivatives. Making extensive use of the python uncertainties package, the OMFIT framework has been used to propagate covariant uncertainties to provide an uncertainty in the power balance calculation from the ONETWO code, as well as through the turbulent fluxes calculated by the TGLF code. The covariant uncertainties arise from fitting 1D (constant on flux surface) density and temperature profiles and associated random errors with parameterized functions such as a modified tanh. The power balance and model fluxes can then be compared with quantification of the uncertainties. No effort is made at propagating systematic errors. A case study will be shown for the effects of resonant magnetic perturbations on the kinetic profiles and fluxes at the top of the pedestal. A separate attempt at modeling the random errors with Monte Carlo sampling will be compared to the method of propagating the fitting function parameter covariant uncertainties. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656.

Photoneutron cross sections for 59Co : Systematic uncertainties of data from various experiments

NASA Astrophysics Data System (ADS)

Varlamov, V. V.; Davydov, A. I.; Ishkhanov, B. S.

2017-09-01

Data on partial photoneutron reaction cross sections (γ ,1n), (γ ,2n), and (γ ,3n) for 59Co obtained in two experiments carried out at Livermore (USA) were analyzed. The sources of radiation in both experiments were the monoenergetic photon beams from the annihilation in flight of relativistic positrons. The total yield was sorted by the neutron multiplicity, taking into account the difference in the neutron energy spectra for different multiplicity. The two quoted studies differ in the method of determining the neutron. Significant systematic disagreements between the results of the two experiments exist. They are considered to be caused by large systematic uncertainties in partial cross sections, since they do not satisfy physical criteria for reliability of the data. To obtain reliable cross sections of partial and total photoneutron reactions a new method combining experimental data and theoretical evaluation was used. It is based on the experimental neutron yield cross section which is rather independent of neutron multiplicity and the transitional neutron multiplicity functions of the combined photonucleon reaction model (CPNRM). The model transitional multiplicity functions were used for the decomposition of the neutron yield cross section into the contributions of partial reactions. The results of the new evaluation noticeably differ from the partial cross sections obtained in the two experimental studies are under discussion.

Uncertainties and Systematic Effects on the estimate of stellar masses in high z galaxies

NASA Astrophysics Data System (ADS)

Salimbeni, S.; Fontana, A.; Giallongo, E.; Grazian, A.; Menci, N.; Pentericci, L.; Santini, P.

2009-05-01

We discuss the uncertainties and the systematic effects that exist in the estimates of the stellar masses of high redshift galaxies, using broad band photometry, and how they affect the deduced galaxy stellar mass function. We use at this purpose the latest version of the GOODS-MUSIC catalog. In particular, we discuss the impact of different synthetic models, of the assumed initial mass function and of the selection band. Using Chariot & Bruzual 2007 and Maraston 2005 models we find masses lower than those obtained from Bruzual & Chariot 2003 models. In addition, we find a slight trend as a function of the mass itself comparing these two mass determinations with that from Bruzual & Chariot 2003 models. As consequence, the derived galaxy stellar mass functions show diverse shapes, and their slope depends on the assumed models. Despite these differences, the overall results and scenario is observed in all these cases. The masses obtained with the assumption of the Chabrier initial mass function are in average 0.24 dex lower than those from the Salpeter assumption, at all redshifts, causing a shift of galaxy stellar mass function of the same amount. Finally, using a 4.5 μm-selected sample instead of a Ks-selected one, we add a new population of highly absorbed, dusty galaxies at z~=2-3 of relatively low masses, yielding stronger constraints on the slope of the galaxy stellar mass function at lower masses.

SU-E-CAMPUS-J-05: Quantitative Investigation of Random and Systematic Uncertainties From Hardware and Software Components in the Frameless 6DBrainLAB ExacTrac System

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

Keeling, V; Jin, H; Hossain, S

2014-06-15

Purpose: To evaluate setup accuracy and quantify individual systematic and random errors for the various hardware and software components of the frameless 6D-BrainLAB ExacTrac system. Methods: 35 patients with cranial lesions, some with multiple isocenters (50 total lesions treated in 1, 3, 5 fractions), were investigated. All patients were simulated with a rigid head-and-neck mask and the BrainLAB localizer. CT images were transferred to the IPLAN treatment planning system where optimized plans were generated using stereotactic reference frame based on the localizer. The patients were setup initially with infrared (IR) positioning ExacTrac system. Stereoscopic X-ray images (XC: X-ray Correction) weremore » registered to their corresponding digitally-reconstructed-radiographs, based on bony anatomy matching, to calculate 6D-translational and rotational (Lateral, Longitudinal, Vertical, Pitch, Roll, Yaw) shifts. XC combines systematic errors of the mask, localizer, image registration, frame, and IR. If shifts were below tolerance (0.7 mm translational and 1 degree rotational), treatment was initiated; otherwise corrections were applied and additional X-rays were acquired to verify patient position (XV: X-ray Verification). Statistical analysis was used to extract systematic and random errors of the different components of the 6D-ExacTrac system and evaluate the cumulative setup accuracy. Results: Mask systematic errors (translational; rotational) were the largest and varied from one patient to another in the range (−15 to 4mm; −2.5 to 2.5degree) obtained from mean of XC for each patient. Setup uncertainty in IR positioning (0.97,2.47,1.62mm;0.65,0.84,0.96degree) was extracted from standard-deviation of XC. Combined systematic errors of the frame and localizer (0.32,−0.42,−1.21mm; −0.27,0.34,0.26degree) was extracted from mean of means of XC distributions. Final patient setup uncertainty was obtained from the standard deviations of XV (0.57,0.77,0.67mm,0

Calibration procedure for a laser triangulation scanner with uncertainty evaluation

NASA Astrophysics Data System (ADS)

Genta, Gianfranco; Minetola, Paolo; Barbato, Giulio

2016-11-01

Most of low cost 3D scanning devices that are nowadays available on the market are sold without a user calibration procedure to correct measurement errors related to changes in environmental conditions. In addition, there is no specific international standard defining a procedure to check the performance of a 3D scanner along time. This paper aims at detailing a thorough methodology to calibrate a 3D scanner and assess its measurement uncertainty. The proposed procedure is based on the use of a reference ball plate and applied to a triangulation laser scanner. Experimental results show that the metrological performance of the instrument can be greatly improved by the application of the calibration procedure that corrects systematic errors and reduces the device's measurement uncertainty.

Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Stephens, Julia; Hubbard, Erin; Walter, Joel; McElroy, Tyler

2016-01-01

This paper presents methods and results of a detailed measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The statistical methods and engineering judgments used to estimate elemental uncertainties are described. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A detailed description of the Monte Carlo method as applied for this analysis is provided. Detailed uncertainty results for the uncertainty in average free stream Mach number as well as other variables of interest are provided. All results are presented as random (variation in observed values about a true value), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty. The largest sources contributing to uncertainty are determined and potential improvement opportunities for the facility are investigated.

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

NASA Astrophysics Data System (ADS)

Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The

2015-11-01

While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.

Uncertainty in monitoring E. coli concentrations in streams and stormwater runoff

NASA Astrophysics Data System (ADS)

Harmel, R. D.; Hathaway, J. M.; Wagner, K. L.; Wolfe, J. E.; Karthikeyan, R.; Francesconi, W.; McCarthy, D. T.

2016-03-01

Microbial contamination of surface waters, a substantial public health concern throughout the world, is typically identified by fecal indicator bacteria such as Escherichia coli. Thus, monitoring E. coli concentrations is critical to evaluate current conditions, determine restoration effectiveness, and inform model development and calibration. An often overlooked component of these monitoring and modeling activities is understanding the inherent random and systematic uncertainty present in measured data. In this research, a review and subsequent analysis was performed to identify, document, and analyze measurement uncertainty of E. coli data collected in stream flow and stormwater runoff as individual discrete samples or throughout a single runoff event. Data on the uncertainty contributed by sample collection, sample preservation/storage, and laboratory analysis in measured E. coli concentrations were compiled and analyzed, and differences in sampling method and data quality scenarios were compared. The analysis showed that: (1) manual integrated sampling produced the lowest random and systematic uncertainty in individual samples, but automated sampling typically produced the lowest uncertainty when sampling throughout runoff events; (2) sample collection procedures often contributed the highest amount of uncertainty, although laboratory analysis introduced substantial random uncertainty and preservation/storage introduced substantial systematic uncertainty under some scenarios; and (3) the uncertainty in measured E. coli concentrations was greater than that of sediment and nutrients, but the difference was not as great as may be assumed. This comprehensive analysis of uncertainty in E. coli concentrations measured in streamflow and runoff should provide valuable insight for designing E. coli monitoring projects, reducing uncertainty in quality assurance efforts, regulatory and policy decision making, and fate and transport modeling.

Uncertainty Analysis and Order-by-Order Optimization of Chiral Nuclear Interactions

DOE PAGES

Carlsson, Boris; Forssen, Christian; Fahlin Strömberg, D.; ...

2016-02-24

Chiral effective field theory ( ΧEFT) provides a systematic approach to describe low-energy nuclear forces. Moreover, EFT is able to provide well-founded estimates of statistical and systematic uncertainties | although this unique advantage has not yet been fully exploited. We ll this gap by performing an optimization and statistical analysis of all the low-energy constants (LECs) up to next-to-next-to-leading order. Our optimization protocol corresponds to a simultaneous t to scattering and bound-state observables in the pion-nucleon, nucleon-nucleon, and few-nucleon sectors, thereby utilizing the full model capabilities of EFT. Finally, we study the effect on other observables by demonstrating forward-error-propagation methodsmore » that can easily be adopted by future works. We employ mathematical optimization and implement automatic differentiation to attain e cient and machine-precise first- and second-order derivatives of the objective function with respect to the LECs. This is also vital for the regression analysis. We use power-counting arguments to estimate the systematic uncertainty that is inherent to EFT and we construct chiral interactions at different orders with quantified uncertainties. Statistical error propagation is compared with Monte Carlo sampling showing that statistical errors are in general small compared to systematic ones. In conclusion, we find that a simultaneous t to different sets of data is critical to (i) identify the optimal set of LECs, (ii) capture all relevant correlations, (iii) reduce the statistical uncertainty, and (iv) attain order-by-order convergence in EFT. Furthermore, certain systematic uncertainties in the few-nucleon sector are shown to get substantially magnified in the many-body sector; in particlar when varying the cutoff in the chiral potentials. The methodology and results presented in this Paper open a new frontier for uncertainty quantification in ab initio nuclear theory.« less

Uncertainties in climate data sets

NASA Technical Reports Server (NTRS)

Mcguirk, James P.

1992-01-01

Climate diagnostics are constructed from either analyzed fields or from observational data sets. Those that have been commonly used are normally considered ground truth. However, in most of these collections, errors and uncertainties exist which are generally ignored due to the consistency of usage over time. Examples of uncertainties and errors are described in NMC and ECMWF analyses and in satellite observational sets-OLR, TOVS, and SMMR. It is suggested that these errors can be large, systematic, and not negligible in climate analysis.

Measurement Uncertainty

NASA Astrophysics Data System (ADS)

Koch, Michael

Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a

Defense Additive Manufacturing: DOD Needs to Systematically Track Department-wide 3D Printing Efforts

DTIC Science & Technology

2015-10-01

Clip Additively Manufactured • The Navy installed a 3D printer aboard the USS Essex to demonstrate the ability to additively develop and produce...desired result and vision to have the capability on the fleet. These officials stated that the Navy plans to install 3D printers on two additional...DEFENSE ADDITIVE MANUFACTURING DOD Needs to Systematically Track Department-wide 3D Printing Efforts Report to

Quantifying Mixed Uncertainties in Cyber Attacker Payoffs

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

Chatterjee, Samrat; Halappanavar, Mahantesh; Tipireddy, Ramakrishna

Representation and propagation of uncertainty in cyber attacker payoffs is a key aspect of security games. Past research has primarily focused on representing the defender’s beliefs about attacker payoffs as point utility estimates. More recently, within the physical security domain, attacker payoff uncertainties have been represented as Uniform and Gaussian probability distributions, and intervals. Within cyber-settings, continuous probability distributions may still be appropriate for addressing statistical (aleatory) uncertainties where the defender may assume that the attacker’s payoffs differ over time. However, systematic (epistemic) uncertainties may exist, where the defender may not have sufficient knowledge or there is insufficient information aboutmore » the attacker’s payoff generation mechanism. Such epistemic uncertainties are more suitably represented as probability boxes with intervals. In this study, we explore the mathematical treatment of such mixed payoff uncertainties.« less

Climate impacts on human livelihoods: where uncertainty matters in projections of water availability

NASA Astrophysics Data System (ADS)

Lissner, T. K.; Reusser, D. E.; Schewe, J.; Lakes, T.; Kropp, J. P.

2014-03-01

Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target-measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models as well as greenhouse gas scenarios are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure Adequate Human livelihood conditions for wEll-being And Development (AHEAD). Based on a transdisciplinary sample of influential concepts addressing human well-being, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows identifying and differentiating uncertainty of climate and impact model projections. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions - and where it is not. The results indicate that in many countries today, livelihood conditions are compromised by water scarcity. However, more often, AHEAD fulfilment is limited through other elements. Moreover, the analysis shows that for 44 out of 111 countries, the water-specific uncertainty ranges are

A systematic uncertainty analysis of an evaluative fate and exposure model.

PubMed

Hertwich, E G; McKone, T E; Pease, W S

2000-08-01

Multimedia fate and exposure models are widely used to regulate the release of toxic chemicals, to set cleanup standards for contaminated sites, and to evaluate emissions in life-cycle assessment. CalTOX, one of these models, is used to calculate the potential dose, an outcome that is combined with the toxicity of the chemical to determine the Human Toxicity Potential (HTP), used to aggregate and compare emissions. The comprehensive assessment of the uncertainty in the potential dose calculation in this article serves to provide the information necessary to evaluate the reliability of decisions based on the HTP A framework for uncertainty analysis in multimedia risk assessment is proposed and evaluated with four types of uncertainty. Parameter uncertainty is assessed through Monte Carlo analysis. The variability in landscape parameters is assessed through a comparison of potential dose calculations for different regions in the United States. Decision rule uncertainty is explored through a comparison of the HTP values under open and closed system boundaries. Model uncertainty is evaluated through two case studies, one using alternative formulations for calculating the plant concentration and the other testing the steady state assumption for wet deposition. This investigation shows that steady state conditions for the removal of chemicals from the atmosphere are not appropriate and result in an underestimate of the potential dose for 25% of the 336 chemicals evaluated.

Uncertainty Analysis in 3D Equilibrium Reconstruction

DOE PAGES

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

2018-02-21

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

Uncertainty Analysis in 3D Equilibrium Reconstruction

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

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

New Assignment of Mass Values and Uncertainties to NIST Working Standards

PubMed Central

Davis, Richard S.

1990-01-01

For some time it had been suspected that values assigned to NIST working standards of mass were some 0.17 mg/kg larger than mass values based on artifacts representing mass in the International System of Units (SI). This relatively small offset, now confirmed, has had minimal scientific or technological significance. The discrepancy was removed on January 1, 1990. We document the history of the discrepancy, the studies which allow its removal, and the methods in place to limit its effect and prevent its recurrence. For routine calibrations, we believe that our working standards now have a long-term stability of 0.033 mg/kg (3σ) with respect to the national prototype kilograms of the United States. We provisionally admit an additional uncertainty of 0.09 mg/kg (3σ), systematic to all NIST mass measurements, which represents the possible offset of our primary standards from standards maintained by the Bureau International des Poids et Mesures (BIPM). This systematic uncertainty may be significantly reduced after analysis of results from the 3rd verification of national prototype kilograms, which is now underway. PMID:28179759

Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Climate impacts on human livelihoods: where uncertainty matters in projections of water availability

NASA Astrophysics Data System (ADS)

Lissner, T. K.; Reusser, D. E.; Schewe, J.; Lakes, T.; Kropp, J. P.

2014-10-01

Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions - and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the

The uncertainty of reference standards--a guide to understanding factors impacting uncertainty, uncertainty calculations, and vendor certifications.

PubMed

Gates, Kevin; Chang, Ning; Dilek, Isil; Jian, Huahua; Pogue, Sherri; Sreenivasan, Uma

2009-10-01

Certified solution standards are widely used in forensic toxicological, clinical/diagnostic, and environmental testing. Typically, these standards are purchased as ampouled solutions with a certified concentration. Vendors present concentration and uncertainty differently on their Certificates of Analysis. Understanding the factors that impact uncertainty and which factors have been considered in the vendor's assignment of uncertainty are critical to understanding the accuracy of the standard and the impact on testing results. Understanding these variables is also important for laboratories seeking to comply with ISO/IEC 17025 requirements and for those preparing reference solutions from neat materials at the bench. The impact of uncertainty associated with the neat material purity (including residual water, residual solvent, and inorganic content), mass measurement (weighing techniques), and solvent addition (solution density) on the overall uncertainty of the certified concentration is described along with uncertainty calculations.

Numerical Uncertainty Quantification for Radiation Analysis Tools

NASA Technical Reports Server (NTRS)

Anderson, Brooke; Blattnig, Steve; Clowdsley, Martha

2007-01-01

Recently a new emphasis has been placed on engineering applications of space radiation analyses and thus a systematic effort of Verification, Validation and Uncertainty Quantification (VV&UQ) of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. There are two sources of uncertainty in geometric discretization addressed in this paper that need to be quantified in order to understand the total uncertainty in estimating space radiation exposures. One source of uncertainty is in ray tracing, as the number of rays increase the associated uncertainty decreases, but the computational expense increases. Thus, a cost benefit analysis optimizing computational time versus uncertainty is needed and is addressed in this paper. The second source of uncertainty results from the interpolation over the dose vs. depth curves that is needed to determine the radiation exposure. The question, then, is what is the number of thicknesses that is needed to get an accurate result. So convergence testing is performed to quantify the uncertainty associated with interpolating over different shield thickness spatial grids.

Characterizing spatial uncertainty when integrating social data in conservation planning.

PubMed

Lechner, A M; Raymond, C M; Adams, V M; Polyakov, M; Gordon, A; Rhodes, J R; Mills, M; Stein, A; Ives, C D; Lefroy, E C

2014-12-01

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches. © 2014 Society for Conservation Biology.

Projecting future air pollution-related mortality under a changing climate: progress, uncertainties and research needs.

PubMed

Madaniyazi, Lina; Guo, Yuming; Yu, Weiwei; Tong, Shilu

2015-02-01

Climate change may affect mortality associated with air pollutants, especially for fine particulate matter (PM2.5) and ozone (O3). Projection studies of such kind involve complicated modelling approaches with uncertainties. We conducted a systematic review of researches and methods for projecting future PM2.5-/O3-related mortality to identify the uncertainties and optimal approaches for handling uncertainty. A literature search was conducted in October 2013, using the electronic databases: PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search was limited to peer-reviewed journal articles published in English from January 1980 to September 2013. Fifteen studies fulfilled the inclusion criteria. Most studies reported that an increase of climate change-induced PM2.5 and O3 may result in an increase in mortality. However, little research has been conducted in developing countries with high emissions and dense populations. Additionally, health effects induced by PM2.5 may dominate compared to those caused by O3, but projection studies of PM2.5-related mortality are fewer than those of O3-related mortality. There is a considerable variation in approaches of scenario-based projection researches, which makes it difficult to compare results. Multiple scenarios, models and downscaling methods have been used to reduce uncertainties. However, few studies have discussed what the main source of uncertainties is and which uncertainty could be most effectively reduced. Projecting air pollution-related mortality requires a systematic consideration of assumptions and uncertainties, which will significantly aid policymakers in efforts to manage potential impacts of PM2.5 and O3 on mortality in the context of climate change. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Exploring Uncertainty with Projectile Launchers

ERIC Educational Resources Information Center

Orzel, Chad; Reich, Gary; Marr, Jonathan

2012-01-01

The proper choice of a measurement technique that minimizes systematic and random uncertainty is an essential part of experimental physics. These issues are difficult to teach in the introductory laboratory, though. Because most experiments involve only a single measurement technique, students are often unable to make a clear distinction between…

Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

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

Li, Weixuan; Lian, Jianming; Engel, Dave

2017-07-27

This paper presents a general uncertainty quantification (UQ) framework that provides a systematic analysis of the uncertainty involved in the modeling of a control system, and helps to improve the performance of a control strategy.

Instrumentation-related uncertainty of reflectance and transmittance measurements with a two-channel spectrophotometer.

PubMed

Peest, Christian; Schinke, Carsten; Brendel, Rolf; Schmidt, Jan; Bothe, Karsten

2017-01-01

Spectrophotometers are operated in numerous fields of science and industry for a variety of applications. In order to provide confidence for the measured data, analyzing the associated uncertainty is valuable. However, the uncertainty of the measurement results is often unknown or reduced to sample-related contributions. In this paper, we describe our approach for the systematic determination of the measurement uncertainty of the commercially available two-channel spectrophotometer Agilent Cary 5000 in accordance with the Guide to the expression of uncertainty in measurements. We focus on the instrumentation-related uncertainty contributions rather than the specific application and thus outline a general procedure which can be adapted for other instruments. Moreover, we discover a systematic signal deviation due to the inertia of the measurement amplifier and develop and apply a correction procedure. Thereby we increase the usable dynamic range of the instrument by more than one order of magnitude. We present methods for the quantification of the uncertainty contributions and combine them into an uncertainty budget for the device.

ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING

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

Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.

2011-04-20

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can bemore » applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.« less

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

NASA Astrophysics Data System (ADS)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and

Clinical Uncertainties, Health Service Challenges, and Ethical Complexities of HIV “Test-and-Treat”: A Systematic Review

PubMed Central

Shah, Kavita R.; Sarma, Karthik V.; Mahajan, Anish P.

2013-01-01

Despite the HIV “test-and-treat” strategy’s promise, questions about its clinical rationale, operational feasibility, and ethical appropriateness have led to vigorous debate in the global HIV community. We performed a systematic review of the literature published between January 2009 and May 2012 using PubMed, SCOPUS, Global Health, Web of Science, BIOSIS, Cochrane CENTRAL, EBSCO Africa-Wide Information, and EBSCO CINAHL Plus databases to summarize clinical uncertainties, health service challenges, and ethical complexities that may affect the test-and-treat strategy’s success. A thoughtful approach to research and implementation to address clinical and health service questions and meaningful community engagement regarding ethical complexities may bring us closer to safe, feasible, and effective test-and-treat implementation. PMID:23597344

Decision Making Under Uncertainty

DTIC Science & Technology

2010-11-01

A sound approach to rational decision making requires a decision maker to establish decision objectives, identify alternatives, and evaluate those...often violate the axioms of rationality when making decisions under uncertainty. The systematic description of such observations may lead to the...which leads to “anchoring” on the initial value. The fact that individuals have been shown to deviate from rationality when making decisions

CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics

NASA Astrophysics Data System (ADS)

Joudaki, Shahab; Blake, Chris; Heymans, Catherine; Choi, Ami; Harnois-Deraps, Joachim; Hildebrandt, Hendrik; Joachimi, Benjamin; Johnson, Andrew; Mead, Alexander; Parkinson, David; Viola, Massimo; van Waerbeke, Ludovic

2017-02-01

We investigate the impact of astrophysical systematics on cosmic shear cosmological parameter constraints from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) and the concordance with cosmic microwave background measurements by Planck. We present updated CFHTLenS cosmic shear tomography measurements extended to degree scales using a covariance calibrated by a new suite of N-body simulations. We analyse these measurements with a new model fitting pipeline, accounting for key systematic uncertainties arising from intrinsic galaxy alignments, baryonic effects in the non-linear matter power spectrum, and photometric redshift uncertainties. We examine the impact of the systematic degrees of freedom on the cosmological parameter constraints, both independently and jointly. When the systematic uncertainties are considered independently, the intrinsic alignment amplitude is the only degree of freedom that is substantially preferred by the data. When the systematic uncertainties are considered jointly, there is no consistently strong preference in favour of the more complex models. We quantify the level of concordance between the CFHTLenS and Planck data sets by employing two distinct data concordance tests, grounded in Bayesian evidence and information theory. We find that the two data concordance tests largely agree with one another and that the level of concordance between the CFHTLenS and Planck data sets is sensitive to the exact details of the systematic uncertainties included in our analysis, ranging from decisive discordance to substantial concordance as the treatment of the systematic uncertainties becomes more conservative. The least conservative scenario is the one most favoured by the cosmic shear data, but it is also the one that shows the greatest degree of discordance with Planck. The data and analysis code are publicly available at https://github.com/sjoudaki/cfhtlens_revisited.

Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters

NASA Astrophysics Data System (ADS)

Liman, Julian; Schröder, Marc; Fennig, Karsten; Andersson, Axel; Hollmann, Rainer

2018-03-01

Latent heat flux (LHF) is one of the main contributors to the global energy budget. As the density of in situ LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology (version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground reference to be bias-free, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50 W m-2 with a global mean of 25 W m-2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are on the order of 60 % (25 %). From an

Leaf area index uncertainty estimates for model-data fusion applications

Treesearch

Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger

2011-01-01

Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...

Community health nursing practices in contexts of poverty, uncertainty and unpredictability: a systematization of personal experiences.

PubMed

Laperrière, Hélène

2007-01-01

Several years of professional nursing practices, while living in the poorest neighbourhoods in the outlying areas of Brazil's Amazon region, have led the author to develop a better understanding of marginalized populations. Providing care to people with leprosy and sex workers in riverside communities has taken place in conditions of uncertainty, insecurity, unpredictability and institutional violence. The question raised is how we can develop community health nursing practices in this context. A systematization of personal experiences based on popular education is used and analyzed as a way of learning by obtaining scientific knowledge through critical analysis of field practices. Ties of solidarity and belonging developed in informal, mutual-help action groups are promising avenues for research and the development of knowledge in health promotion, prevention and community care and a necessary contribution to national public health programmers.

Strategy under uncertainty.

PubMed

Courtney, H; Kirkland, J; Viguerie, P

1997-01-01

At the heart of the traditional approach to strategy lies the assumption that by applying a set of powerful analytic tools, executives can predict the future of any business accurately enough to allow them to choose a clear strategic direction. But what happens when the environment is so uncertain that no amount of analysis will allow us to predict the future? What makes for a good strategy in highly uncertain business environments? The authors, consultants at McKinsey & Company, argue that uncertainty requires a new way of thinking about strategy. All too often, they say, executives take a binary view: either they underestimate uncertainty to come up with the forecasts required by their companies' planning or capital-budging processes, or they overestimate it, abandon all analysis, and go with their gut instinct. The authors outline a new approach that begins by making a crucial distinction among four discrete levels of uncertainty that any company might face. They then explain how a set of generic strategies--shaping the market, adapting to it, or reserving the right to play at a later time--can be used in each of the four levels. And they illustrate how these strategies can be implemented through a combination of three basic types of actions: big bets, options, and no-regrets moves. The framework can help managers determine which analytic tools can inform decision making under uncertainty--and which cannot. At a broader level, it offers executives a discipline for thinking rigorously and systematically about uncertainty and its implications for strategy.

Multi-thresholds for fault isolation in the presence of uncertainties.

PubMed

Touati, Youcef; Mellal, Mohamed Arezki; Benazzouz, Djamel

2016-05-01

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

An Assessment of the Systematic Uncertainty in Present and Future Tests of the Lense-Thirring Effect with Satellite Laser Ranging

NASA Astrophysics Data System (ADS)

Iorio, Lorenzo

2009-12-01

We deal with the attempts to measure the Lense-Thirring effect with the Satellite Laser Ranging (SLR) technique applied to the existing LAGEOS and LAGEOS II terrestrial satellites and to the recently approved LARES spacecraft. According to general relativity, a central spinning body of mass M and angular momentum S like the Earth generates a gravitomagnetic field which induces small secular precessions of the orbit of a test particle geodesically moving around it. Extracting this signature from the data is a demanding task because of many classical orbital perturbations having the same pattern as the gravitomagnetic one, like those due to the centrifugal oblateness of the Earth which represents a major source of systematic bias. The first issue addressed here is: are the so far published evaluations of the systematic uncertainty induced by the bad knowledge of the even zonal harmonic coefficients J ℓ of the multipolar expansion of the Earth’s geopotential reliable and realistic? Our answer is negative. Indeed, if the differences Δ J ℓ among the even zonals estimated in different Earth’s gravity field global solutions from the dedicated GRACE mission are assumed for the uncertainties δ J ℓ instead of using their covariance sigmas σ_{J_{ell}} , it turns out that the systematic uncertainty δ μ in the Lense-Thirring test with the nodes Ω of LAGEOS and LAGEOS II may be up to 3 to 4 times larger than in the evaluations so far published (5-10%) based on the use of the sigmas of one model at a time separately. The second issue consists of the possibility of using a different approach in extracting the relativistic signature of interest from the LAGEOS-type data. The third issue is the possibility of reaching a realistic total accuracy of 1% with LAGEOS, LAGEOS II and LARES, which should be launched in November 2009 with a VEGA rocket. While LAGEOS and LAGEOS II fly at altitudes of about 6000 km, LARES will be likely placed at an altitude of 1450 km. Thus

Uncertainties in the deprojection of the observed bar properties

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

Zou, Yanfei; Shen, Juntai; Li, Zhao-Yu, E-mail: jshen@shao.ac.cn

2014-08-10

In observations, it is important to deproject the two fundamental quantities characterizing a bar, i.e., its length (a) and ellipticity (e), to face-on values before any careful analyses. However, systematic estimation on the uncertainties of the commonly used deprojection methods is still lacking. Simulated galaxies are well suited in this study. We project two simulated barred galaxies onto a two-dimensional (2D) plane with different bar orientations and disk inclination angles (i). Bar properties are measured and deprojected with the popular deprojection methods in the literature. Generally speaking, deprojection uncertainties increase with increasing i. All of the deprojection methods behave badlymore » when i is larger than 60°, due to the vertical thickness of the bar. Thus, future statistical studies of barred galaxies should exclude galaxies more inclined than 60°. At moderate inclination angles (i ≤ 60°), 2D deprojection methods (analytical and image stretching), and Fourier-based methods (Fourier decomposition and bar-interbar contrast) perform reasonably well with uncertainties ∼10% in both the bar length and ellipticity, whereas the uncertainties of the one-dimensional (1D) analytical deprojection can be as high as 100% in certain extreme cases. We find that different bar measurement methods show systematic differences in the deprojection uncertainties. We further discuss the deprojection uncertainty factors with the emphasis on the most important one, i.e., the three-dimensional structure of the bar itself. We construct two triaxial toy bar models that can qualitatively reproduce the results of the 1D and 2D analytical deprojections; they confirm that the vertical thickness of the bar is the main source of uncertainties.« less

Particle Dark Matter constraints: the effect of Galactic uncertainties

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

Benito, Maria; Bernal, Nicolás; Iocco, Fabio

2017-02-01

Collider, space, and Earth based experiments are now able to probe several extensions of the Standard Model of particle physics which provide viable dark matter candidates. Direct and indirect dark matter searches rely on inputs of astrophysical nature, such as the local dark matter density or the shape of the dark matter density profile in the target in object. The determination of these quantities is highly affected by astrophysical uncertainties. The latter, especially those for our own Galaxy, are ill-known, and often not fully accounted for when analyzing the phenomenology of particle physics models. In this paper we present amore » systematic, quantitative estimate of how astrophysical uncertainties on Galactic quantities (such as the local galactocentric distance, circular velocity, or the morphology of the stellar disk and bulge) propagate to the determination of the phenomenology of particle physics models, thus eventually affecting the determination of new physics parameters. We present results in the context of two specific extensions of the Standard Model (the Singlet Scalar and the Inert Doublet) that we adopt as case studies for their simplicity in illustrating the magnitude and impact of such uncertainties on the parameter space of the particle physics model itself. Our findings point toward very relevant effects of current Galactic uncertainties on the determination of particle physics parameters, and urge a systematic estimate of such uncertainties in more complex scenarios, in order to achieve constraints on the determination of new physics that realistically include all known uncertainties.« less

Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for internal dosimetry. Volume 2: Appendices

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

Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.

1998-04-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library ofmore » uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on internal dosimetry, (4) short biographies of the experts, and (5) the aggregated results of their responses.« less

Hard Constraints in Optimization Under Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2008-01-01

This paper proposes a methodology for the analysis and design of systems subject to parametric uncertainty where design requirements are specified via hard inequality constraints. Hard constraints are those that must be satisfied for all parameter realizations within a given uncertainty model. Uncertainty models given by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles, are the focus of this paper. These models, which are also quite practical, allow for a rigorous mathematical treatment within the proposed framework. Hard constraint feasibility is determined by sizing the largest uncertainty set for which the design requirements are satisfied. Analytically verifiable assessments of robustness are attained by comparing this set with the actual uncertainty model. Strategies that enable the comparison of the robustness characteristics of competing design alternatives, the description and approximation of the robust design space, and the systematic search for designs with improved robustness are also proposed. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, this methodology is applicable to a broad range of engineering problems.

Host Model Uncertainty in Aerosol Radiative Effects: the AeroCom Prescribed Experiment and Beyond

NASA Astrophysics Data System (ADS)

Stier, Philip; Schutgens, Nick; Bian, Huisheng; Boucher, Olivier; Chin, Mian; Ghan, Steven; Huneeus, Nicolas; Kinne, Stefan; Lin, Guangxing; Myhre, Gunnar; Penner, Joyce; Randles, Cynthia; Samset, Bjorn; Schulz, Michael; Yu, Hongbin; Zhou, Cheng; Bellouin, Nicolas; Ma, Xiaoyan; Yu, Fangqun; Takemura, Toshihiko

2013-04-01

Anthropogenic and natural aerosol radiative effects are recognized to affect global and regional climate. Multi-model "diversity" in estimates of the aerosol radiative effect is often perceived as a measure of the uncertainty in modelling aerosol itself. However, current aerosol models vary considerably in model components relevant for the calculation of aerosol radiative forcings and feedbacks and the associated "host-model uncertainties" are generally convoluted with the actual uncertainty in aerosol modelling. In the AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in eleven participating models. Host model errors in aerosol radiative forcing are largest in regions of uncertain host model components, such as stratocumulus cloud decks or areas with poorly constrained surface albedos, such as sea ice. Our results demonstrate that host model uncertainties are an important component of aerosol forcing uncertainty that require further attention. However, uncertainties in aerosol radiative effects also include short-term and long-term feedback processes that will be systematically explored in future intercomparison studies. Here we will present an overview of the proposals for discussion and results from early scoping studies.

The Scientific Basis of Uncertainty Factors Used in Setting Occupational Exposure Limits.

PubMed

Dankovic, D A; Naumann, B D; Maier, A; Dourson, M L; Levy, L S

2015-01-01

The uncertainty factor concept is integrated into health risk assessments for all aspects of public health practice, including by most organizations that derive occupational exposure limits. The use of uncertainty factors is predicated on the assumption that a sufficient reduction in exposure from those at the boundary for the onset of adverse effects will yield a safe exposure level for at least the great majority of the exposed population, including vulnerable subgroups. There are differences in the application of the uncertainty factor approach among groups that conduct occupational assessments; however, there are common areas of uncertainty which are considered by all or nearly all occupational exposure limit-setting organizations. Five key uncertainties that are often examined include interspecies variability in response when extrapolating from animal studies to humans, response variability in humans, uncertainty in estimating a no-effect level from a dose where effects were observed, extrapolation from shorter duration studies to a full life-time exposure, and other insufficiencies in the overall health effects database indicating that the most sensitive adverse effect may not have been evaluated. In addition, a modifying factor is used by some organizations to account for other remaining uncertainties-typically related to exposure scenarios or accounting for the interplay among the five areas noted above. Consideration of uncertainties in occupational exposure limit derivation is a systematic process whereby the factors applied are not arbitrary, although they are mathematically imprecise. As the scientific basis for uncertainty factor application has improved, default uncertainty factors are now used only in the absence of chemical-specific data, and the trend is to replace them with chemical-specific adjustment factors whenever possible. The increased application of scientific data in the development of uncertainty factors for individual chemicals also has

Model uncertainty of various settlement estimation methods in shallow tunnels excavation; case study: Qom subway tunnel

NASA Astrophysics Data System (ADS)

Khademian, Amir; Abdollahipour, Hamed; Bagherpour, Raheb; Faramarzi, Lohrasb

2017-10-01

In addition to the numerous planning and executive challenges, underground excavation in urban areas is always followed by certain destructive effects especially on the ground surface; ground settlement is the most important of these effects for which estimation there exist different empirical, analytical and numerical methods. Since geotechnical models are associated with considerable model uncertainty, this study characterized the model uncertainty of settlement estimation models through a systematic comparison between model predictions and past performance data derived from instrumentation. To do so, the amount of surface settlement induced by excavation of the Qom subway tunnel was estimated via empirical (Peck), analytical (Loganathan and Poulos) and numerical (FDM) methods; the resulting maximum settlement value of each model were 1.86, 2.02 and 1.52 cm, respectively. The comparison of these predicted amounts with the actual data from instrumentation was employed to specify the uncertainty of each model. The numerical model outcomes, with a relative error of 3.8%, best matched the reality and the analytical method, with a relative error of 27.8%, yielded the highest level of model uncertainty.

Individuals’ Uncertainty about Future Social Security Benefits and Portfolio Choice

PubMed Central

Delavande, Adeline

2013-01-01

Summary Little is known about the degree to which individuals are uncertain about their future Social Security benefits, how this varies within the U.S. population, and whether this uncertainty influences financial decisions related to retirement planning. To illuminate these issues, we present empirical evidence from the Health and Retirement Study Internet Survey and document systematic variation in respondents’ uncertainty about their future Social Security benefits by individual characteristics. We find that respondents with higher levels of uncertainty about future benefits hold a smaller share of their wealth in stocks. PMID:23914049

Simple uncertainty propagation for early design phase aircraft sizing

NASA Astrophysics Data System (ADS)

Lenz, Annelise

Many designers and systems analysts are aware of the uncertainty inherent in their aircraft sizing studies; however, few incorporate methods to address and quantify this uncertainty. Many aircraft design studies use semi-empirical predictors based on a historical database and contain uncertainty -- a portion of which can be measured and quantified. In cases where historical information is not available, surrogate models built from higher-fidelity analyses often provide predictors for design studies where the computational cost of directly using the high-fidelity analyses is prohibitive. These surrogate models contain uncertainty, some of which is quantifiable. However, rather than quantifying this uncertainty, many designers merely include a safety factor or design margin in the constraints to account for the variability between the predicted and actual results. This can become problematic if a designer does not estimate the amount of variability correctly, which then can result in either an "over-designed" or "under-designed" aircraft. "Under-designed" and some "over-designed" aircraft will likely require design changes late in the process and will ultimately require more time and money to create; other "over-designed" aircraft concepts may not require design changes, but could end up being more costly than necessary. Including and propagating uncertainty early in the design phase so designers can quantify some of the errors in the predictors could help mitigate the extent of this additional cost. The method proposed here seeks to provide a systematic approach for characterizing a portion of the uncertainties that designers are aware of and propagating it throughout the design process in a procedure that is easy to understand and implement. Using Monte Carlo simulations that sample from quantified distributions will allow a systems analyst to use a carpet plot-like approach to make statements like: "The aircraft is 'P'% likely to weigh 'X' lbs or less, given the

Ensembles vs. information theory: supporting science under uncertainty

NASA Astrophysics Data System (ADS)

Nearing, Grey S.; Gupta, Hoshin V.

2018-05-01

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

NASA Astrophysics Data System (ADS)

Brogniez, Helene; English, Stephen; Mahfouf, Jean-Francois; Behrendt, Andreas; Berg, Wesley; Boukabara, Sid; Buehler, Stefan Alexander; Chambon, Philippe; Gambacorta, Antonia; Geer, Alan; Ingram, William; Kursinski, E. Robert; Matricardi, Marco; Odintsova, Tatyana A.; Payne, Vivienne H.; Thorne, Peter W.; Tretyakov, Mikhail Yu.; Wang, Junhong

2016-05-01

Several recent studies have observed systematic differences between measurements in the 183.31 GHz water vapor line by space-borne sounders and calculations using radiative transfer models, with inputs from either radiosondes (radiosonde observations, RAOBs) or short-range forecasts by numerical weather prediction (NWP) models. This paper discusses all the relevant categories of observation-based or model-based data, quantifies their uncertainties and separates biases that could be common to all causes from those attributable to a particular cause. Reference observations from radiosondes, Global Navigation Satellite System (GNSS) receivers, differential absorption lidar (DIAL) and Raman lidar are thus overviewed. Biases arising from their calibration procedures, NWP models and data assimilation, instrument biases and radiative transfer models (both the models themselves and the underlying spectroscopy) are presented and discussed. Although presently no single process in the comparisons seems capable of explaining the observed structure of bias, recommendations are made in order to better understand the causes.

Using Principal Component and Tidal Analysis as a Quality Metric for Detecting Systematic Heading Uncertainty in Long-Term Acoustic Doppler Current Profiler Data

NASA Astrophysics Data System (ADS)

Morley, M. G.; Mihaly, S. F.; Dewey, R. K.; Jeffries, M. A.

2015-12-01

Ocean Networks Canada (ONC) operates the NEPTUNE and VENUS cabled ocean observatories to collect data on physical, chemical, biological, and geological ocean conditions over multi-year time periods. Researchers can download real-time and historical data from a large variety of instruments to study complex earth and ocean processes from their home laboratories. Ensuring that the users are receiving the most accurate data is a high priority at ONC, requiring quality assurance and quality control (QAQC) procedures to be developed for all data types. While some data types have relatively straightforward QAQC tests, such as scalar data range limits that are based on expected observed values or measurement limits of the instrument, for other data types the QAQC tests are more comprehensive. Long time series of ocean currents from Acoustic Doppler Current Profilers (ADCP), stitched together from multiple deployments over many years is one such data type where systematic data biases are more difficult to identify and correct. Data specialists at ONC are working to quantify systematic compass heading uncertainty in long-term ADCP records at each of the major study sites using the internal compass, remotely operated vehicle bearings, and more analytical tools such as principal component analysis (PCA) to estimate the optimal instrument alignments. In addition to using PCA, some work has been done to estimate the main components of the current at each site using tidal harmonic analysis. This paper describes the key challenges and presents preliminary PCA and tidal analysis approaches used by ONC to improve long-term observatory current measurements.

Multidimensional analysis of fast-spectrum material replacement measurements for systematic estimation of cross section uncertainties

NASA Technical Reports Server (NTRS)

Klann, P. G.; Lantz, E.; Mayo, W. T.

1973-01-01

A series of central core and core-reflector interface sample replacement experiments for 16 materials performed in the NASA heavy-metal-reflected, fast spectrum critical assembly (NCA) were analyzed in four and 13 groups using the GAM 2 cross-section set. The individual worths obtained by TDSN and DOT multidimensional transport theory calculations showed significant differences from the experimental results. These were attributed to cross-section uncertainties in the GAM 2 cross sections. Simultaneous analysis of the measured and calculated sample worths permitted separation of the worths into capture and scattering components which systematically provided fast spectrum averaged correction factors to the magnitudes of the GAM 2 absorption and scattering cross sections. Several Los Alamos clean critical assemblies containing Oy, Ta, and Mo as well as one of the NCA compositions were reanalyzed using the corrected cross sections. In all cases the eigenvalues were significantly improved and were recomputed to within 1 percent of the experimental eigenvalue. A comparable procedure may be used for ENDF cross sections when these are available.

Uncertainty quantification for optical model parameters

DOE PAGES

Lovell, A. E.; Nunes, F. M.; Sarich, J.; ...

2017-02-21

Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of our work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fitmore » and create corresponding 95% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. Here, we study a number of reactions involving neutron and deuteron projectiles with energies in the range of 5–25 MeV/u, on targets with mass A=12–208. We investigate the correlations between the parameters in the fit. The case of deuterons on 12C is discussed in detail: the elastic-scattering fit and the prediction of 12C(d,p) 13C transfer angular distributions, using both uncorrelated and correlated χ 2 minimization functions. The general features for all cases are compiled in a systematic manner to identify trends. This work shows that, in many cases, the correlated χ 2 functions (in comparison to the uncorrelated χ 2 functions) provide a more natural parameterization of the process. These correlated functions do, however, produce broader confidence bands. Further optimization may require improvement in the models themselves and/or more information included in the fit.« less

Compound risk judgment in tasks with both idiosyncratic and systematic risk: The "Robust Beauty" of additive probability integration.

PubMed

Sundh, Joakim; Juslin, Peter

2018-02-01

In this study, we explore how people integrate risks of assets in a simulated financial market into a judgment of the conjunctive risk that all assets decrease in value, both when assets are independent and when there is a systematic risk present affecting all assets. Simulations indicate that while mental calculation according to naïve application of probability theory is best when the assets are independent, additive or exemplar-based algorithms perform better when systematic risk is high. Considering that people tend to intuitively approach compound probability tasks using additive heuristics, we expected the participants to find it easiest to master tasks with high systematic risk - the most complex tasks from the standpoint of probability theory - while they should shift to probability theory or exemplar memory with independence between the assets. The results from 3 experiments confirm that participants shift between strategies depending on the task, starting off with the default of additive integration. In contrast to results in similar multiple cue judgment tasks, there is little evidence for use of exemplar memory. The additive heuristics also appear to be surprisingly context-sensitive, with limited generalization across formally very similar tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Not Normal: the uncertainties of scientific measurements

PubMed Central

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student’s t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply. PMID:28280557

Not Normal: the uncertainties of scientific measurements

NASA Astrophysics Data System (ADS)

Bailey, David C.

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.

Uncertainty quantification and validation of 3D lattice scaffolds for computer-aided biomedical applications.

PubMed

Gorguluarslan, Recep M; Choi, Seung-Kyum; Saldana, Christopher J

2017-07-01

A methodology is proposed for uncertainty quantification and validation to accurately predict the mechanical response of lattice structures used in the design of scaffolds. Effective structural properties of the scaffolds are characterized using a developed multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process and minimize the experimental cost, high-resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling method facilitates the process of determining homogenized strut properties to reduce the computational cost of the detailed simulation model for the scaffold. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also developed to assess the predictive capability of the stochastic upscaling method used at the strut level and lattice structure level. In comparison with physical compression test results, the proposed methodology of linking the uncertainty quantification with the multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure with minimal experimental cost by accounting for the uncertainties induced by the additive manufacturing process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

The Crucial Role of Error Correlation for Uncertainty Modeling of CFD-Based Aerodynamics Increments

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.; Walker, Eric L.

2011-01-01

The Ares I ascent aerodynamics database for Design Cycle 3 (DAC-3) was built from wind-tunnel test results and CFD solutions. The wind tunnel results were used to build the baseline response surfaces for wind-tunnel Reynolds numbers at power-off conditions. The CFD solutions were used to build increments to account for Reynolds number effects. We calculate the validation errors for the primary CFD code results at wind tunnel Reynolds number power-off conditions and would like to be able to use those errors to predict the validation errors for the CFD increments. However, the validation errors are large compared to the increments. We suggest a way forward that is consistent with common practice in wind tunnel testing which is to assume that systematic errors in the measurement process and/or the environment will subtract out when increments are calculated, thus making increments more reliable with smaller uncertainty than absolute values of the aerodynamic coefficients. A similar practice has arisen for the use of CFD to generate aerodynamic database increments. The basis of this practice is the assumption of strong correlation of the systematic errors inherent in each of the results used to generate an increment. The assumption of strong correlation is the inferential link between the observed validation uncertainties at wind-tunnel Reynolds numbers and the uncertainties to be predicted for flight. In this paper, we suggest a way to estimate the correlation coefficient and demonstrate the approach using code-to-code differences that were obtained for quality control purposes during the Ares I CFD campaign. Finally, since we can expect the increments to be relatively small compared to the baseline response surface and to be typically of the order of the baseline uncertainty, we find that it is necessary to be able to show that the correlation coefficients are close to unity to avoid overinflating the overall database uncertainty with the addition of the increments.

Uncertainties in scaling factors for ab initio vibrational zero-point energies

NASA Astrophysics Data System (ADS)

Irikura, Karl K.; Johnson, Russell D.; Kacker, Raghu N.; Kessel, Rüdiger

2009-03-01

Vibrational zero-point energies (ZPEs) determined from ab initio calculations are often scaled by empirical factors. An empirical scaling factor partially compensates for the effects arising from vibrational anharmonicity and incomplete treatment of electron correlation. These effects are not random but are systematic. We report scaling factors for 32 combinations of theory and basis set, intended for predicting ZPEs from computed harmonic frequencies. An empirical scaling factor carries uncertainty. We quantify and report, for the first time, the uncertainties associated with scaling factors for ZPE. The uncertainties are larger than generally acknowledged; the scaling factors have only two significant digits. For example, the scaling factor for B3LYP/6-31G(d) is 0.9757±0.0224 (standard uncertainty). The uncertainties in the scaling factors lead to corresponding uncertainties in predicted ZPEs. The proposed method for quantifying the uncertainties associated with scaling factors is based upon the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. We also present a new reference set of 60 diatomic and 15 polyatomic "experimental" ZPEs that includes estimated uncertainties.

Accounting for uncertainty in the quantification of the environmental impacts of Canadian pig farming systems.

PubMed

Mackenzie, S G; Leinonen, I; Ferguson, N; Kyriazakis, I

2015-06-01

The objective of the study was to develop a life cycle assessment (LCA) for pig farming systems that would account for uncertainty and variability in input data and allow systematic environmental impact comparisons between production systems. The environmental impacts of commercial pig production for 2 regions in Canada (Eastern and Western) were compared using a cradle-to-farm gate LCA. These systems had important contrasting characteristics such as typical feed ingredients used, herd performance, and expected emission factors from manure management. The study used detailed production data supplied by the industry and incorporated uncertainty/variation in all major aspects of the system including life cycle inventory data for feed ingredients, animal performance, energy inputs, and emission factors. The impacts were defined using 5 metrics-global warming potential, acidification potential, eutrophication potential (EP), abiotic resource use, and nonrenewable energy use-and were expressed per kilogram carcass weight at farm gate. Eutrophication potential was further separated into marine EP (MEP) and freshwater EP (FEP). Uncertainties in the model inputs were separated into 2 types: uncertainty in the data used to describe the system (α uncertainties) and uncertainty in impact calculations or background data that affects all systems equally (β uncertainties). The impacts of pig production in the 2 regions were systematically compared based on the differences in the systems (α uncertainties). The method of ascribing uncertainty influenced the outcomes. In eastern systems, EP, MEP, and FEP were lower (P < 0.05) when assuming that all uncertainty in the emission factors for leaching from manure application was β. This was mainly due to increased EP resulting from field emissions for typical ingredients in western diets. When uncertainty in these emission factors was assumed to be α, only FEP was lower in eastern systems (P < 0.05). The environmental impacts for

Judgment under emotional certainty and uncertainty: the effects of specific emotions on information processing.

PubMed

Tiedens, L Z; Linton, S

2001-12-01

The authors argued that emotions characterized by certainty appraisals promote heuristic processing, whereas emotions characterized by uncertainty appraisals result in systematic processing. The 1st experiment demonstrated that the certainty associated with an emotion affects the certainty experienced in subsequent situations. The next 3 experiments investigated effects on processing of emotions associated with certainty and uncertainty. Compared with emotions associated with uncertainty, emotions associated with certainty resulted in greater reliance on the expertise of a source of a persuasive message in Experiment 2, more stereotyping in Experiment 3, and less attention to argument quality in Experiment 4. In contrast to previous theories linking valence and processing, these findings suggest that the certainty appraisal content of emotions is also important in determining whether people engage in systematic or heuristic processing.

An integrated modeling approach to support management decisions of coupled groundwater-agricultural systems under multiple uncertainties

NASA Astrophysics Data System (ADS)

Hagos Subagadis, Yohannes; Schütze, Niels; Grundmann, Jens

2015-04-01

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational frameworks. Such integrative research to link different knowledge domains faces several practical challenges. Such complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. A fuzzy-stochastic multiple criteria decision analysis tool was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrated physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approach is applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structure. Results from the developed method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach has enabled to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. Sensitivity analysis applied within the developed tool has shown that the decision makers' risk aversion and risk taking attitude may yield in different ranking of decision alternatives. The developed approach can be applied to address the complexities and uncertainties inherent in water resources systems to support management decisions, while serving as a platform for stakeholder participation.

Judgment under Uncertainty: Heuristics and Biases.

PubMed

Tversky, A; Kahneman, D

1974-09-27

This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.

Reducing Uncertainties in Neutron-Induced Fission Cross Sections Using a Time Projection Chamber

NASA Astrophysics Data System (ADS)

Manning, Brett; Niffte Collaboration

2015-10-01

Neutron-induced fission cross sections for actinides have long been of great interest for nuclear energy and stockpile stewardship. Traditionally, measurements were performed using fission chambers which provided limited information about the detected fission events. For the case of 239Pu(n,f), sensitivity studies have shown a need for more precise measurements. Recently the Neutron Induced Fission Fragment Tracking Experiment (NIFFTE) has developed the fission Time Projection Chamber (fissionTPC) to measure fission cross sections to better than 1% uncertainty by providing 3D tracking of fission fragments. The fissionTPC collected data to calculate the 239Pu(n,f) cross section at the Weapons Neutron Research facility at the Los Alamos Neutron Science Center during the 2014 run cycle. Preliminary analysis has been focused on studying particle identification and target and beam non-uniformities to reduce the uncertainty on the cross section. Additionally, the collaboration is investigating other systematic errors that could not be well studied with a traditional fission chamber. LA-UR-15-24906.

Uncertainty in assessing value of oncology treatments.

PubMed

Mullins, C Daniel; Montgomery, Russ; Tunis, Sean

2010-01-01

Patients, clinicians, payers, and policymakers face an environment of significant evidentiary uncertainty as they attempt to achieve maximum value, or the greatest level of benefit possible at a given level of cost in their respective health care decisions. This is particularly true in the area of oncology, for which published evidence from clinical trials is often incongruent with real-world patient care, and a substantial portion of clinical use is for off-label indications that have not been systematically evaluated. It is this uncertainty in the knowledge of the clinical harms and benefits associated with oncology treatments that prevents postregulatory decision makers from making accurate assessments of the value of these treatments. Because of the incentives inherent in the clinical research enterprise, randomized control trials (RCTs) are designed for the specific purpose of regulatory approval and maximizing market penetration. The pursuit of these goals results in RCT study designs that achieve maximal internal validity at the expense of generalizability to diverse real-world patient populations that may have significant comorbidities and other clinically mitigating factors. As such, systematic reviews for the purposes of coverage and treatment decisions often find relevant and high-quality evidence to be limited or nonexistent. For a number of reasons, including frequent off-label use of medications and the expedited approval process for cancer drugs by the U.S. Food and Drug Administration, this situation is exacerbated in the area of oncology. This paper investigates the convergence of incentives and circumstances that lead to widespread uncertainty in oncology and proposes new paradigms for clinical research, including pragmatic clinical trials, methodological guidance, and coverage with evidence development. Each of these initiatives would support the design of clinical research that is more informative for postregulatory decision makers, and would

Systematic uncertainties in RF-based measurement of superconducting cavity quality factors

DOE PAGES

Holzbauer, J. P.; Pischalnikov, Yu.; Sergatskov, D. A.; ...

2016-05-10

Q 0 determinations based on RF power measurements are subject to at least three potentially large systematic effects that have not been previously appreciated. Here, instrumental factors that can systematically bias RF based measurements of Q 0 are quantified and steps that can be taken to improve the determination of Q 0 are discussed.

What is the clinical effectiveness and cost-effectiveness of conservative interventions for tendinopathy? An overview of systematic reviews of clinical effectiveness and systematic review of economic evaluations.

PubMed

Long, Linda; Briscoe, Simon; Cooper, Chris; Hyde, Chris; Crathorne, Louise

2015-01-01

Lateral elbow tendinopathy (LET) is a common complaint causing characteristic pain in the lateral elbow and upper forearm, and tenderness of the forearm extensor muscles. It is thought to be an overuse injury and can have a major impact on the patient's social and professional life. The condition is challenging to treat and prone to recurrent episodes. The average duration of a typical episode ranges from 6 to 24 months, with most (89%) reporting recovery by 1 year. This systematic review aims to summarise the evidence concerning the clinical effectiveness and cost-effectiveness of conservative interventions for LET. A comprehensive search was conducted from database inception to 2012 in a range of databases including MEDLINE, EMBASE and Cochrane Databases. We conducted an overview of systematic reviews to summarise the current evidence concerning the clinical effectiveness and a systematic review for the cost-effectiveness of conservative interventions for LET. We identified additional randomised controlled trials (RCTs) that could contribute further evidence to existing systematic reviews. We searched MEDLINE, EMBASE, Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health Literature, Web of Science, The Cochrane Library and other important databases from inception to January 2013. A total of 29 systematic reviews published since 2003 matched our inclusion criteria. These were quality appraised using the Assessment of Multiple Systematic Reviews (AMSTAR) checklist; five were considered high quality and evaluated using a Grading of Recommendations, Assessment, Development and Evaluation approach. A total of 36 RCTs were identified that were not included in a systematic review and 29 RCTs were identified that had only been evaluated in an included systematic review of intermediate/low quality. These were then mapped to existing systematic reviews where further evidence could provide updates. Two economic evaluations were identified

Parameterization of Model Validating Sets for Uncertainty Bound Optimizations. Revised

NASA Technical Reports Server (NTRS)

Lim, K. B.; Giesy, D. P.

2000-01-01

Given measurement data, a nominal model and a linear fractional transformation uncertainty structure with an allowance on unknown but bounded exogenous disturbances, easily computable tests for the existence of a model validating uncertainty set are given. Under mild conditions, these tests are necessary and sufficient for the case of complex, nonrepeated, block-diagonal structure. For the more general case which includes repeated and/or real scalar uncertainties, the tests are only necessary but become sufficient if a collinearity condition is also satisfied. With the satisfaction of these tests, it is shown that a parameterization of all model validating sets of plant models is possible. The new parameterization is used as a basis for a systematic way to construct or perform uncertainty tradeoff with model validating uncertainty sets which have specific linear fractional transformation structure for use in robust control design and analysis. An illustrative example which includes a comparison of candidate model validating sets is given.

SU-E-T-292: Sensitivity of Fractionated Lung IMPT Treatments to Setup Uncertainties and Motion Effects

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

Dowdell, S; Grassberger, C; Paganetti, H

2014-06-01

Purpose: Evaluate the sensitivity of intensity-modulated proton therapy (IMPT) lung treatments to systematic and random setup uncertainties combined with motion effects. Methods: Treatment plans with single-field homogeneity restricted to ±20% (IMPT-20%) were compared to plans with no restriction (IMPT-full). 4D Monte Carlo simulations were performed for 10 lung patients using the patient CT geometry with either ±5mm systematic or random setup uncertainties applied over a 35 × 2.5Gy(RBE) fractionated treatment course. Intra-fraction, inter-field and inter-fraction motions were investigated. 50 fractionated treatments with systematic or random setup uncertainties applied to each fraction were generated for both IMPT delivery methods and threemore » energy-dependent spot sizes (big spots - BS σ=18-9mm, intermediate spots - IS σ=11-5mm, small spots - SS σ=4-2mm). These results were compared to a Monte Carlo recalculation of the original treatment plan, with results presented as the difference in EUD (ΔEUD), V{sub 95} (ΔV{sub 95}) and target homogeneity (ΔD{sub 1}–D{sub 99}) between the 4D simulations and the Monte Carlo calculation on the planning CT. Results: The standard deviations in the ΔEUD were 1.95±0.47(BS), 1.85±0.66(IS) and 1.31±0.35(SS) times higher in IMPT-full compared to IMPT-20% when ±5mm systematic setup uncertainties were applied. The ΔV{sub 95} variations were also 1.53±0.26(BS), 1.60±0.50(IS) and 1.38±0.38(SS) times higher for IMPT-full. For random setup uncertainties, the standard deviations of the ΔEUD from 50 simulated fractionated treatments were 1.94±0.90(BS), 2.13±1.08(IS) and 1.45±0.57(SS) times higher in IMPTfull compared to IMPT-20%. For all spot sizes considered, the ΔD{sub 1}-D{sub 99} coincided within the uncertainty limits for the two IMPT delivery methods, with the mean value always higher for IMPT-full. Statistical analysis showed significant differences between the IMPT-full and IMPT-20% dose distributions

When is enough evidence enough? - Using systematic decision analysis and value-of-information analysis to determine the need for further evidence.

PubMed

Siebert, Uwe; Rochau, Ursula; Claxton, Karl

2013-01-01

Decision analysis (DA) and value-of-information (VOI) analysis provide a systematic, quantitative methodological framework that explicitly considers the uncertainty surrounding the currently available evidence to guide healthcare decisions. In medical decision making under uncertainty, there are two fundamental questions: 1) What decision should be made now given the best available evidence (and its uncertainty)?; 2) Subsequent to the current decision and given the magnitude of the remaining uncertainty, should we gather further evidence (i.e., perform additional studies), and if yes, which studies should be undertaken (e.g., efficacy, side effects, quality of life, costs), and what sample sizes are needed? Using the currently best available evidence, VoI analysis focuses on the likelihood of making a wrong decision if the new intervention is adopted. The value of performing further studies and gathering additional evidence is based on the extent to which the additional information will reduce this uncertainty. A quantitative framework allows for the valuation of the additional information that is generated by further research, and considers the decision maker's objectives and resource constraints. Claxton et al. summarise: "Value of information analysis can be used to inform a range of policy questions including whether a new technology should be approved based on existing evidence, whether it should be approved but additional research conducted or whether approval should be withheld until the additional evidence becomes available." [Claxton K. Value of information entry in Encyclopaedia of Health Economics, Elsevier, forthcoming 2014.] The purpose of this tutorial is to introduce the framework of systematic VoI analysis to guide further research. In our tutorial article, we explain the theoretical foundations and practical methods of decision analysis and value-of-information analysis. To illustrate, we use a simple case example of a foot ulcer (e.g., with

Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

2012-01-01

Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 14% (312 K) over desert and 17% (320 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.52% (26 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 1017 K under the most severe conditions.

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

Proton and neutron electromagnetic form factors and uncertainties

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

Ye, Zhihong; Arrington, John; Hill, Richard J.

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

Proton and neutron electromagnetic form factors and uncertainties

DOE PAGES

Ye, Zhihong; Arrington, John; Hill, Richard J.; ...

2017-12-06

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

Uncertainty Modeling for Structural Control Analysis and Synthesis

NASA Technical Reports Server (NTRS)

Campbell, Mark E.; Crawley, Edward F.

1996-01-01

The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.

Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for deposited material and external doses. Volume 2: Appendices

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

Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.

1997-12-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library ofmore » uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA deposited material and external dose models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on deposited material and external doses, (4) short biographies of the experts, and (5) the aggregated results of their responses.« less

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Uncertainty Calculations in the First Introductory Physics Laboratory

NASA Astrophysics Data System (ADS)

Rahman, Shafiqur

2005-03-01

Uncertainty in a measured quantity is an integral part of reporting any experimental data. Consequently, Introductory Physics laboratories at many institutions require that students report the values of the quantities being measured as well as their uncertainties. Unfortunately, given that there are three main ways of calculating uncertainty, each suitable for particular situations (which is usually not explained in the lab manual), this is also an area that students feel highly confused about. It frequently generates large number of complaints in the end-of-the semester course evaluations. Students at some institutions are not asked to calculate uncertainty at all, which gives them a fall sense of the nature of experimental data. Taking advantage of the increased sophistication in the use of computers and spreadsheets that students are coming to college with, we have completely restructured our first Introductory Physics Lab to address this problem. Always in the context of a typical lab, we now systematically and sequentially introduce the various ways of calculating uncertainty including a theoretical understanding as opposed to a cookbook approach, all within the context of six three-hour labs. Complaints about the lab in student evaluations have dropped by 80%. * supported by a grant from A. V. Davis Foundation

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

NASA Technical Reports Server (NTRS)

Mukherjee, Avijit; Grabbe, Shon; Sridhar, Banavar

2011-01-01

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

Lost in Translation: Piloting a Novel Framework to Assess the Challenges in Translating Scientific Uncertainty From Empirical Findings to WHO Policy Statements

PubMed Central

Benmarhnia, Tarik; Huang, Jonathan Y.; Jones, Catherine M.

2017-01-01

contribute to uncertainty regarding recommendations were neglected, including the state of evidence informing background questions (prevalence, mechanisms, or burden or distributions of health problems) and little assessment of generalizability, alternate interventions, and additional outcomes not captured by systematic review. These other factors often form a basis for providing policy recommendations, particularly in the absence of a strong evidence base for intervention effects. Consequently, they should also be subject to stringent and systematic evaluation criteria. We suggest that more effort is needed to systematically acknowledge (1) when evidence is missing, conflicting, or equivocal, (2) what normative considerations were also employed, and (3) how additional evidence may be accrued. PMID:29179291

Communicating uncertainties in earth sciences in view of user needs

NASA Astrophysics Data System (ADS)

de Vries, Wim; Kros, Hans; Heuvelink, Gerard

2014-05-01

uncertain model parameters (parametric variability). These uncertainties can be quantified by uncertainty propagation methods such as Monte Carlo simulation methods. Examples of intrinsic uncertainties that generally cannot be expressed in mathematical terms are errors or biases in: • Results of experiments and observations due to inadequate sampling and errors in analyzing data in the laboratory and even in data reporting. • Results of (laboratory) experiments that are limited to a specific domain or performed under circumstances that differ from field circumstances. • Model structure, due to lack of knowledge of the underlying processes. Structural uncertainty, which may cause model inadequacy/ bias, is inherent in model approaches since models are approximations of reality. Intrinsic uncertainties often occur in an emerging field where ongoing new findings, either experiments or field observations of new model findings, challenge earlier work. In this context, climate scientists working within the IPCC have adopted a lexicon to communicate confidence in their findings, ranging from "very high", "high", "medium", "low" and "very low" confidence. In fact, there are also statistical methods to gain insight in uncertainties in model predictions due to model assumptions (i.e. model structural error). Examples are comparing model results with independent observations or a systematic intercomparison of predictions from multiple models. In the latter case, Bayesian model averaging techniques can be used, in which each model considered gets an assigned prior probability of being the 'true' model. This approach works well with statistical (regression) models, but extension to physically-based models is cumbersome. An alternative is the use of state-space models in which structural errors are represent as (additive) noise terms. In this presentation, we focus on approaches that are relevant at the science - policy interface, including multiple scientific disciplines and

Uncertainty in tsunami sediment transport modeling

USGS Publications Warehouse

Jaffe, Bruce E.; Goto, Kazuhisa; Sugawara, Daisuke; Gelfenbaum, Guy R.; La Selle, SeanPaul M.

2016-01-01

Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. We explore sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami, study site, available input data, sediment grain size, and model. Although uncertainty has the potential to be large, published case studies indicate that both forward and inverse tsunami sediment transport models perform well enough to be useful for deciphering tsunami characteristics, including size, from deposits. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and development of hybrid modeling approaches to exploit the strengths of forward and inverse models.

Reducing model uncertainty effects in flexible manipulators through the addition of passive damping

NASA Technical Reports Server (NTRS)

Alberts, T. E.

1987-01-01

An important issue in the control of practical systems is the effect of model uncertainty on closed loop performance. This is of particular concern when flexible structures are to be controlled, due to the fact that states associated with higher frequency vibration modes are truncated in order to make the control problem tractable. Digital simulations of a single-link manipulator system are employed to demonstrate that passive damping added to the flexible member reduces adverse effects associated with model uncertainty. A controller was designed based on a model including only one flexible mode. This controller was applied to larger order systems to evaluate the effects of modal truncation. Simulations using a Linear Quadratic Regulator (LQR) design assuming full state feedback illustrate the effect of control spillover. Simulations of a system using output feedback illustrate the destabilizing effect of observation spillover. The simulations reveal that the system with passive damping is less susceptible to these effects than the untreated case.

Quantifying model uncertainty in seasonal Arctic sea-ice forecasts

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin

2017-04-01

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 post-processing 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.

Reprocessed Southern Hemisphere Additional Ozonesondes (SHADOZ) Profiles (1998-2016): Method, Uncertainties and Comparisons with Satellite Total Ozone

NASA Astrophysics Data System (ADS)

Stauffer, R. M.; Thompson, A. M.; Witte, J. C.; Johnson, B.; Smit, H. G. J.

2017-12-01

The SHADOZ network was assembled to validate a new generation of ozone-monitoring satellites and to better characterize the vertical structure of tropical stratospheric and tropospheric ozone. Beginning with nine stations in 1998, more than 7000 ozone and P-T-U profiles are available from 14 stations that have operated for at least a decade. In the past two years the SHADOZ records have been reprocessed to adjust for inconsistencies caused by varying ozonesonde instruments and operating techniques. We have followed consensus-based guidelines given by the international ozonesonde community and will release new records that include first estimates of uncertainties in the ozonesonde instrument system. The ozone uncertainty is a composite of uncertainties of the individual terms in the ozone partial pressure (PO3) equation, i.e., ozone sensor current, background current, internal pump temperature, pump efficiency factors, conversion efficiency, and flow-rate. Overall, SHADOZ PO3 uncertainties are 15% or less and peak around the tropopause (15±3km) where the ozone current can approach the detection limit of the sensor. The sonde total column ozone (TCO) uncertainty is estimated at ±15 DU or 5% of typical tropical TCO. When sonde-derived TCO is compared to overpasses from the EP/TOMS, OMI and OMPS satellites that cover 1998-2016, sonde-satellite offsets at 12 stations are 2% or less (Figure), well within the uncertainty of both satellite and sonde. This agreement is much improved over our earlier SHADOZ evaluations (2003, 2007 and 2012). Reprocessing has also led to more uniform stratospheric column amounts across sites within +19 degrees latitude and reduced profile bias.

Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

2013-01-01

Uncertainties in the retrievals of microwaveland-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors inthe retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

Adaptive robust fault-tolerant control for linear MIMO systems with unmatched uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Kangkang; Jiang, Bin; Yan, Xing-Gang; Mao, Zehui

2017-10-01

In this paper, two novel fault-tolerant control design approaches are proposed for linear MIMO systems with actuator additive faults, multiplicative faults and unmatched uncertainties. For time-varying multiplicative and additive faults, new adaptive laws and additive compensation functions are proposed. A set of conditions is developed such that the unmatched uncertainties are compensated by actuators in control. On the other hand, for unmatched uncertainties with their projection in unmatched space being not zero, based on a (vector) relative degree condition, additive functions are designed to compensate for the uncertainties from output channels in the presence of actuator faults. The developed fault-tolerant control schemes are applied to two aircraft systems to demonstrate the efficiency of the proposed approaches.

Monte Carlo analysis of uncertainty propagation in a stratospheric model. 2: Uncertainties due to reaction rates

NASA Technical Reports Server (NTRS)

Stolarski, R. S.; Butler, D. M.; Rundel, R. D.

1977-01-01

A concise stratospheric model was used in a Monte-Carlo analysis of the propagation of reaction rate uncertainties through the calculation of an ozone perturbation due to the addition of chlorine. Two thousand Monte-Carlo cases were run with 55 reaction rates being varied. Excellent convergence was obtained in the output distributions because the model is sensitive to the uncertainties in only about 10 reactions. For a 1 ppby chlorine perturbation added to a 1.5 ppby chlorine background, the resultant 1 sigma uncertainty on the ozone perturbation is a factor of 1.69 on the high side and 1.80 on the low side. The corresponding 2 sigma factors are 2.86 and 3.23. Results are also given for the uncertainties, due to reaction rates, in the ambient concentrations of stratospheric species.

Systematic in J-PARC/Hyper-K

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

Minamino, Akihiro

The Hyper-Kamiokande (Hyper-K) detector is a next generation underground water Chrenkov detector. The J-PARC to Hyper-K experiment has good potential for precision measurements of neutrino oscillation parameters and discovery reach for CP violation in the lepton sector. With a total exposure of 10 years to a neutrino beam produced by the 750 kW J-PARC proton synchrotron, it is expected that the CP phase δ can be determined to better than 18 degree for all possible values of δ if sin{sup 2} 2θ{sub 13} > 0.03 and the mass hierarchy is known. Control of systematic uncertainties is critical to make maximummore » use of the Hyper-K potential. Based on learning from T2K experience, a strategy to reduce systematic uncertainties in J-PARC/Hyper-K are developed.« less

Error and Uncertainty Quantification in the Numerical Simulation of Complex Fluid Flows

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2010-01-01

The failure of numerical simulation to predict physical reality is often a direct consequence of the compounding effects of numerical error arising from finite-dimensional approximation and physical model uncertainty resulting from inexact knowledge and/or statistical representation. In this topical lecture, we briefly review systematic theories for quantifying numerical errors and restricted forms of model uncertainty occurring in simulations of fluid flow. A goal of this lecture is to elucidate both positive and negative aspects of applying these theories to practical fluid flow problems. Finite-element and finite-volume calculations of subsonic and hypersonic fluid flow are presented to contrast the differing roles of numerical error and model uncertainty. for these problems.

Using spatial uncertainty to manipulate the size of the attention focus.

PubMed

Huang, Dan; Xue, Linyan; Wang, Xin; Chen, Yao

2016-09-01

Preferentially processing behaviorally relevant information is vital for primate survival. In visuospatial attention studies, manipulating the spatial extent of attention focus is an important question. Although many studies have claimed to successfully adjust attention field size by either varying the uncertainty about the target location (spatial uncertainty) or adjusting the size of the cue orienting the attention focus, no systematic studies have assessed and compared the effectiveness of these methods. We used a multiple cue paradigm with 2.5° and 7.5° rings centered around a target position to measure the cue size effect, while the spatial uncertainty levels were manipulated by changing the number of cueing positions. We found that spatial uncertainty had a significant impact on reaction time during target detection, while the cue size effect was less robust. We also carefully varied the spatial scope of potential target locations within a small or large region and found that this amount of variation in spatial uncertainty can also significantly influence target detection speed. Our results indicate that adjusting spatial uncertainty is more effective than varying cue size when manipulating attention field size.

Computation and visualization of uncertainty in surgical navigation.

PubMed

Simpson, Amber L; Ma, Burton; Vasarhelyi, Edward M; Borschneck, Dan P; Ellis, Randy E; James Stewart, A

2014-09-01

Surgical displays do not show uncertainty information with respect to the position and orientation of instruments. Data is presented as though it were perfect; surgeons unaware of this uncertainty could make critical navigational mistakes. The propagation of uncertainty to the tip of a surgical instrument is described and a novel uncertainty visualization method is proposed. An extensive study with surgeons has examined the effect of uncertainty visualization on surgical performance with pedicle screw insertion, a procedure highly sensitive to uncertain data. It is shown that surgical performance (time to insert screw, degree of breach of pedicle, and rotation error) is not impeded by the additional cognitive burden imposed by uncertainty visualization. Uncertainty can be computed in real time and visualized without adversely affecting surgical performance, and the best method of uncertainty visualization may depend upon the type of navigation display. Copyright © 2013 John Wiley & Sons, Ltd.

Quantifying and Qualifying USGS ShakeMap Uncertainty

USGS Publications Warehouse

Wald, David J.; Lin, Kuo-Wan; Quitoriano, Vincent

2008-01-01

We describe algorithms for quantifying and qualifying uncertainties associated with USGS ShakeMap ground motions. The uncertainty values computed consist of latitude/longitude grid-based multiplicative factors that scale the standard deviation associated with the ground motion prediction equation (GMPE) used within the ShakeMap algorithm for estimating ground motions. The resulting grid-based 'uncertainty map' is essential for evaluation of losses derived using ShakeMaps as the hazard input. For ShakeMap, ground motion uncertainty at any point is dominated by two main factors: (i) the influence of any proximal ground motion observations, and (ii) the uncertainty of estimating ground motions from the GMPE, most notably, elevated uncertainty due to initial, unconstrained source rupture geometry. The uncertainty is highest for larger magnitude earthquakes when source finiteness is not yet constrained and, hence, the distance to rupture is also uncertain. In addition to a spatially-dependant, quantitative assessment, many users may prefer a simple, qualitative grading for the entire ShakeMap. We developed a grading scale that allows one to quickly gauge the appropriate level of confidence when using rapidly produced ShakeMaps as part of the post-earthquake decision-making process or for qualitative assessments of archived or historical earthquake ShakeMaps. We describe an uncertainty letter grading ('A' through 'F', for high to poor quality, respectively) based on the uncertainty map. A middle-range ('C') grade corresponds to a ShakeMap for a moderate-magnitude earthquake suitably represented with a point-source location. Lower grades 'D' and 'F' are assigned for larger events (M>6) where finite-source dimensions are not yet constrained. The addition of ground motion observations (or observed macroseismic intensities) reduces uncertainties over data-constrained portions of the map. Higher grades ('A' and 'B') correspond to ShakeMaps with constrained fault dimensions

A review of uncertainty in in situ measurements and data sets of sea surface temperature

NASA Astrophysics Data System (ADS)

Kennedy, John J.

2014-03-01

Archives of in situ sea surface temperature (SST) measurements extend back more than 160 years. Quality of the measurements is variable, and the area of the oceans they sample is limited, especially early in the record and during the two world wars. Measurements of SST and the gridded data sets that are based on them are used in many applications so understanding and estimating the uncertainties are vital. The aim of this review is to give an overview of the various components that contribute to the overall uncertainty of SST measurements made in situ and of the data sets that are derived from them. In doing so, it also aims to identify current gaps in understanding. Uncertainties arise at the level of individual measurements with both systematic and random effects and, although these have been extensively studied, refinement of the error models continues. Recent improvements have been made in the understanding of the pervasive systematic errors that affect the assessment of long-term trends and variability. However, the adjustments applied to minimize these systematic errors are uncertain and these uncertainties are higher before the 1970s and particularly large in the period surrounding the Second World War owing to a lack of reliable metadata. The uncertainties associated with the choice of statistical methods used to create globally complete SST data sets have been explored using different analysis techniques, but they do not incorporate the latest understanding of measurement errors, and they want for a fair benchmark against which their skill can be objectively assessed. These problems can be addressed by the creation of new end-to-end SST analyses and by the recovery and digitization of data and metadata from ship log books and other contemporary literature.

Reusable launch vehicle model uncertainties impact analysis

NASA Astrophysics Data System (ADS)

Chen, Jiaye; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

Reusable launch vehicle(RLV) has the typical characteristics of complex aerodynamic shape and propulsion system coupling, and the flight environment is highly complicated and intensely changeable. So its model has large uncertainty, which makes the nominal system quite different from the real system. Therefore, studying the influences caused by the uncertainties on the stability of the control system is of great significance for the controller design. In order to improve the performance of RLV, this paper proposes the approach of analyzing the influence of the model uncertainties. According to the typical RLV, the coupling dynamic and kinematics models are built. Then different factors that cause uncertainties during building the model are analyzed and summed up. After that, the model uncertainties are expressed according to the additive uncertainty model. Choosing the uncertainties matrix's maximum singular values as the boundary model, and selecting the uncertainties matrix's norm to show t how much the uncertainty factors influence is on the stability of the control system . The simulation results illustrate that the inertial factors have the largest influence on the stability of the system, and it is necessary and important to take the model uncertainties into consideration before the designing the controller of this kind of aircraft( like RLV, etc).

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Technical Reports Server (NTRS)

Novack, Steven D.; Rogers, Jim; Al Hassan, Mohammad; Hark, Frank

2016-01-01

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk, and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results, and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods, such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty, are rendered obsolete, since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods. This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper describes how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Technical Reports Server (NTRS)

Novack, Steven D.; Rogers, Jim; Hark, Frank; Al Hassan, Mohammad

2016-01-01

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty are rendered obsolete since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods.This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper shows how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Dark Energy Survey Year 1 results: cross-correlation redshifts - methods and systematics characterization

NASA Astrophysics Data System (ADS)

Gatti, M.; Vielzeuf, P.; Davis, C.; Cawthon, R.; Rau, M. M.; DeRose, J.; De Vicente, J.; Alarcon, A.; Rozo, E.; Gaztanaga, E.; Hoyle, B.; Miquel, R.; Bernstein, G. M.; Bonnett, C.; Carnero Rosell, A.; Castander, F. J.; Chang, C.; da Costa, L. N.; Gruen, D.; Gschwend, J.; Hartley, W. G.; Lin, H.; MacCrann, N.; Maia, M. A. G.; Ogando, R. L. C.; Roodman, A.; Sevilla-Noarbe, I.; Troxel, M. A.; Wechsler, R. H.; Asorey, J.; Davis, T. M.; Glazebrook, K.; Hinton, S. R.; Lewis, G.; Lidman, C.; Macaulay, E.; Möller, A.; O'Neill, C. R.; Sommer, N. E.; Uddin, S. A.; Yuan, F.; Zhang, B.; Abbott, T. M. C.; Allam, S.; Annis, J.; Bechtol, K.; Brooks, D.; Burke, D. L.; Carollo, D.; Carrasco Kind, M.; Carretero, J.; Cunha, C. E.; D'Andrea, C. B.; DePoy, D. L.; Desai, S.; Eifler, T. F.; Evrard, A. E.; Flaugher, B.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gerdes, D. W.; Goldstein, D. A.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; Hoormann, J. K.; Jain, B.; James, D. J.; Jarvis, M.; Jeltema, T.; Johnson, M. W. G.; Johnson, M. D.; Krause, E.; Kuehn, K.; Kuhlmann, S.; Kuropatkin, N.; Li, T. S.; Lima, M.; Marshall, J. L.; Melchior, P.; Menanteau, F.; Nichol, R. C.; Nord, B.; Plazas, A. A.; Reil, K.; Rykoff, E. S.; Sako, M.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sheldon, E.; Smith, M.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Tucker, B. E.; Tucker, D. L.; Vikram, V.; Walker, A. R.; Weller, J.; Wester, W.; Wolf, R. C.

2018-06-01

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

Asteroseismic modelling of solar-type stars: internal systematics from input physics and surface correction methods

NASA Astrophysics Data System (ADS)

Nsamba, B.; Campante, T. L.; Monteiro, M. J. P. F. G.; Cunha, M. S.; Rendle, B. M.; Reese, D. R.; Verma, K.

2018-04-01

Asteroseismic forward modelling techniques are being used to determine fundamental properties (e.g. mass, radius, and age) of solar-type stars. The need to take into account all possible sources of error is of paramount importance towards a robust determination of stellar properties. We present a study of 34 solar-type stars for which high signal-to-noise asteroseismic data is available from multi-year Kepler photometry. We explore the internal systematics on the stellar properties, that is, associated with the uncertainty in the input physics used to construct the stellar models. In particular, we explore the systematics arising from: (i) the inclusion of the diffusion of helium and heavy elements; and (ii) the uncertainty in solar metallicity mixture. We also assess the systematics arising from (iii) different surface correction methods used in optimisation/fitting procedures. The systematics arising from comparing results of models with and without diffusion are found to be 0.5%, 0.8%, 2.1%, and 16% in mean density, radius, mass, and age, respectively. The internal systematics in age are significantly larger than the statistical uncertainties. We find the internal systematics resulting from the uncertainty in solar metallicity mixture to be 0.7% in mean density, 0.5% in radius, 1.4% in mass, and 6.7% in age. The surface correction method by Sonoi et al. and Ball & Gizon's two-term correction produce the lowest internal systematics among the different correction methods, namely, ˜1%, ˜1%, ˜2%, and ˜8% in mean density, radius, mass, and age, respectively. Stellar masses obtained using the surface correction methods by Kjeldsen et al. and Ball & Gizon's one-term correction are systematically higher than those obtained using frequency ratios.

Summary of long-baseline systematics session at CETUP*2014

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

Cherdack, Daniel; Worcester, Elizabeth

2015-10-15

A session studying systematics in long-baseline neutrino oscillation physics was held July 14-18, 2014 as part of CETUP* 2014. Systematic effects from flux normalization and modeling, modeling of cross sections and nuclear interactions, and far detector effects were addressed. Experts presented the capabilities of existing and planned tools. A program of study to determine estimates of and requirements for the size of these effects was designed. This document summarizes the results of the CETUP* systematics workshop and the current status of systematic uncertainty studies in long-baseline neutrino oscillation measurements.

A review of uncertainty visualization within the IPCC reports

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Reusser, Dominik; Wrobel, Markus

2015-04-01

Results derived from climate model simulations confront non-expert users with a variety of uncertainties. This gives rise to the challenge that the scientific information must be communicated such that it can be easily understood, however, the complexity of the science behind is still incorporated. With respect to the assessment reports of the IPCC, the situation is even more complicated, because heterogeneous sources and multiple types of uncertainties need to be compiled together. Within this work, we systematically (1) analyzed the visual representation of uncertainties in the IPCC AR4 and AR5 reports, and (2) executed a questionnaire to evaluate how different user groups such as decision-makers and teachers understand these uncertainty visualizations. Within the first step, we classified visual uncertainty metaphors for spatial, temporal and abstract representations. As a result, we clearly identified a high complexity of the IPCC visualizations compared to standard presentation graphics, sometimes even integrating two or more uncertainty classes / measures together with the "certain" (mean) information. Further we identified complex written uncertainty explanations within image captions even within the "summary reports for policy makers". In the second step, based on these observations, we designed a questionnaire to investigate how non-climate experts understand these visual representations of uncertainties, how visual uncertainty coding might hinder the perception of the "non-uncertain" data, and if alternatives for certain IPCC visualizations exist. Within the talk/poster, we will present first results from this questionnaire. Summarizing, we identified a clear trend towards complex images within the latest IPCC reports, with a tendency to incorporate as much as possible information into the visual representations, resulting in proprietary, non-standard graphic representations that are not necessarily easy to comprehend on one glimpse. We conclude that

Water supply infrastructure planning under multiple uncertainties: A differentiated approach

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K.

2017-12-01

Many water planners face increased pressure on water supply systems from increasing demands from population and economic growth in combination with uncertain water supply. Supply uncertainty arises from short-term climate variability and long-term climate change as well as uncertainty in groundwater availability. Social and economic uncertainties - such as sectoral competition for water, food and energy security, urbanization, and environmental protection - compound physical uncertainty. Further, the varying risk aversion of stakeholders and water managers makes it difficult to assess the necessity of expensive infrastructure investments to reduce risk. We categorize these uncertainties on two dimensions: whether they can be updated over time by collecting additional information, and whether the uncertainties can be described probabilistically or are "deep" uncertainties whose likelihood is unknown. Based on this, we apply a decision framework that combines simulation for probabilistic uncertainty, scenario analysis for deep uncertainty, and multi-stage decision analysis for uncertainties that are reduced over time with additional information. In light of these uncertainties and the investment costs of large infrastructure, we propose the assessment of staged, modular infrastructure and information updating as a hedge against risk. We apply this framework to cases in Melbourne, Australia and Riyadh, Saudi Arabia. Melbourne is a surface water system facing uncertain population growth and variable rainfall and runoff. A severe drought from 1997 to 2009 prompted investment in a 150 MCM/y reverse osmosis desalination plan with a capital cost of 3.5 billion. Our analysis shows that flexible design in which a smaller portion of capacity is developed initially with the option to add modular capacity in the future can mitigate uncertainty and reduce the expected lifetime costs by up to 1 billion. In Riyadh, urban water use relies on fossil groundwater aquifers and

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together

Modelling ecosystem service flows under uncertainty with stochiastic SPAN

USGS Publications Warehouse

Johnson, Gary W.; Snapp, Robert R.; Villa, Ferdinando; Bagstad, Kenneth J.

2012-01-01

Ecosystem service models are increasingly in demand for decision making. However, the data required to run these models are often patchy, missing, outdated, or untrustworthy. Further, communication of data and model uncertainty to decision makers is often either absent or unintuitive. In this work, we introduce a systematic approach to addressing both the data gap and the difficulty in communicating uncertainty through a stochastic adaptation of the Service Path Attribution Networks (SPAN) framework. The SPAN formalism assesses ecosystem services through a set of up to 16 maps, which characterize the services in a study area in terms of flow pathways between ecosystems and human beneficiaries. Although the SPAN algorithms were originally defined deterministically, we present them here in a stochastic framework which combines probabilistic input data with a stochastic transport model in order to generate probabilistic spatial outputs. This enables a novel feature among ecosystem service models: the ability to spatially visualize uncertainty in the model results. The stochastic SPAN model can analyze areas where data limitations are prohibitive for deterministic models. Greater uncertainty in the model inputs (including missing data) should lead to greater uncertainty expressed in the model’s output distributions. By using Bayesian belief networks to fill data gaps and expert-provided trust assignments to augment untrustworthy or outdated information, we can account for uncertainty in input data, producing a model that is still able to run and provide information where strictly deterministic models could not. Taken together, these attributes enable more robust and intuitive modelling of ecosystem services under uncertainty.

Detailed Uncertainty Analysis for Ares I Ascent Aerodynamics Wind Tunnel Database

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.; Hanke, Jeremy L.; Walker, Eric L.; Houlden, Heather P.

2008-01-01

A detailed uncertainty analysis for the Ares I ascent aero 6-DOF wind tunnel database is described. While the database itself is determined using only the test results for the latest configuration, the data used for the uncertainty analysis comes from four tests on two different configurations at the Boeing Polysonic Wind Tunnel in St. Louis and the Unitary Plan Wind Tunnel at NASA Langley Research Center. Four major error sources are considered: (1) systematic errors from the balance calibration curve fits and model + balance installation, (2) run-to-run repeatability, (3) boundary-layer transition fixing, and (4) tunnel-to-tunnel reproducibility.

Sources of uncertainty in flood inundation maps

USGS Publications Warehouse

Bales, J.D.; Wagner, C.R.

2009-01-01

Flood inundation maps typically have been used to depict inundated areas for floods having specific exceedance levels. The uncertainty associated with the inundation boundaries is seldom quantified, in part, because all of the sources of uncertainty are not recognized and because data available to quantify uncertainty seldom are available. Sources of uncertainty discussed in this paper include hydrologic data used for hydraulic model development and validation, topographic data, and the hydraulic model. The assumption of steady flow, which typically is made to produce inundation maps, has less of an effect on predicted inundation at lower flows than for higher flows because more time typically is required to inundate areas at high flows than at low flows. Difficulties with establishing reasonable cross sections that do not intersect and that represent water-surface slopes in tributaries contribute additional uncertainties in the hydraulic modelling. As a result, uncertainty in the flood inundation polygons simulated with a one-dimensional model increases with distance from the main channel.

Quantification and propagation of disciplinary uncertainty via Bayesian statistics

NASA Astrophysics Data System (ADS)

Mantis, George Constantine

2002-08-01

Several needs exist in the military, commercial, and civil sectors for new hypersonic systems. These needs remain unfulfilled, due in part to the uncertainty encountered in designing these systems. This uncertainty takes a number of forms, including disciplinary uncertainty, that which is inherent in the analytical tools utilized during the design process. Yet, few efforts to date empower the designer with the means to account for this uncertainty within the disciplinary analyses. In the current state-of-the-art in design, the effects of this unquantifiable uncertainty significantly increase the risks associated with new design efforts. Typically, the risk proves too great to allow a given design to proceed beyond the conceptual stage. To that end, the research encompasses the formulation and validation of a new design method, a systematic process for probabilistically assessing the impact of disciplinary uncertainty. The method implements Bayesian Statistics theory to quantify this source of uncertainty, and propagate its effects to the vehicle system level. Comparison of analytical and physical data for existing systems, modeled a priori in the given analysis tools, leads to quantification of uncertainty in those tools' calculation of discipline-level metrics. Then, after exploration of the new vehicle's design space, the quantified uncertainty is propagated probabilistically through the design space. This ultimately results in the assessment of the impact of disciplinary uncertainty on the confidence in the design solution: the final shape and variability of the probability functions defining the vehicle's system-level metrics. Although motivated by the hypersonic regime, the proposed treatment of uncertainty applies to any class of aerospace vehicle, just as the problem itself affects the design process of any vehicle. A number of computer programs comprise the environment constructed for the implementation of this work. Application to a single

Fission cross section uncertainties with the NIFFTE TPC

NASA Astrophysics Data System (ADS)

Sangiorgio, Samuele; Niffte Collaboration

2014-09-01

Nuclear data such as neutron-induced fission cross sections play a fundamental role in nuclear energy and defense applications. In recent years, understanding of these systems has become increasingly dependent upon advanced simulation and modeling, where uncertainties in nuclear data propagate in the expected performances of existing and future systems. It is important therefore that uncertainties in nuclear data are minimized and fully understood. For this reason, the Neutron Induced Fission Fragment Tracking Experiment (NIFFTE) uses a Time Projection Chamber (TPC) to measure energy-differential (n,f) cross sections with unprecedented precision. The presentation will discuss how the capabilities of the NIFFTE TPC allow to directly measures systematic uncertainties in fission cross sections, in particular for what concerns fission-fragment identification, and target and beam uniformity. Preliminary results from recent analysis of 238U/235U and 239Pu/235U data collected with the TPC will be presented. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Uncertainty and risk in wildland fire management: a review.

PubMed

Thompson, Matthew P; Calkin, Dave E

2011-08-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making. Published by Elsevier Ltd.

Reviews and syntheses: Revisiting the boron systematics of aragonite and their application to coral calcification

NASA Astrophysics Data System (ADS)

DeCarlo, Thomas M.; Holcomb, Michael; McCulloch, Malcolm T.

2018-05-01

The isotopic and elemental systematics of boron in aragonitic coral skeletons have recently been developed as a proxy for the carbonate chemistry of the coral extracellular calcifying fluid. With knowledge of the boron isotopic fractionation in seawater and the B/Ca partition coefficient (KD) between aragonite and seawater, measurements of coral skeleton δ11B and B/Ca can potentially constrain the full carbonate system. Two sets of abiogenic aragonite precipitation experiments designed to quantify KD have recently made possible the application of this proxy system. However, while different KD formulations have been proposed, there has not yet been a comprehensive analysis that considers both experimental datasets and explores the implications for interpreting coral skeletons. Here, we evaluate four potential KD formulations: three previously presented in the literature and one newly developed. We assess how well each formulation reconstructs the known fluid carbonate chemistry from the abiogenic experiments, and we evaluate the implications for deriving the carbonate chemistry of coral calcifying fluid. Three of the KD formulations performed similarly when applied to abiogenic aragonites precipitated from seawater and to coral skeletons. Critically, we find that some uncertainty remains in understanding the mechanism of boron elemental partitioning between aragonite and seawater, and addressing this question should be a target of additional abiogenic precipitation experiments. Despite this, boron systematics can already be applied to quantify the coral calcifying fluid carbonate system, although uncertainties associated with the proxy system should be carefully considered for each application. Finally, we present a user-friendly computer code that calculates coral calcifying fluid carbonate chemistry, including propagation of uncertainties, given inputs of boron systematics measured in coral skeleton.

Confronting dynamics and uncertainty in optimal decision making for conservation

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2013-01-01

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a

Confronting dynamics and uncertainty in optimal decision making for conservation

NASA Astrophysics Data System (ADS)

Williams, Byron K.; Johnson, Fred A.

2013-06-01

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a

Estimating uncertainty of Full Waveform Inversion with Ensemble-based methods

NASA Astrophysics Data System (ADS)

Thurin, J.; Brossier, R.; Métivier, L.

2017-12-01

Uncertainty estimation is one key feature of tomographic applications for robust interpretation. However, this information is often missing in the frame of large scale linearized inversions, and only the results at convergence are shown, despite the ill-posed nature of the problem. This issue is common in the Full Waveform Inversion community.While few methodologies have already been proposed in the literature, standard FWI workflows do not include any systematic uncertainty quantifications methods yet, but often try to assess the result's quality through cross-comparison with other results from seismic or comparison with other geophysical data. With the development of large seismic networks/surveys, the increase in computational power and the more and more systematic application of FWI, it is crucial to tackle this problem and to propose robust and affordable workflows, in order to address the uncertainty quantification problem faced for near surface targets, crustal exploration, as well as regional and global scales.In this work (Thurin et al., 2017a,b), we propose an approach which takes advantage of the Ensemble Transform Kalman Filter (ETKF) proposed by Bishop et al., (2001), in order to estimate a low-rank approximation of the posterior covariance matrix of the FWI problem, allowing us to evaluate some uncertainty information of the solution. Instead of solving the FWI problem through a Bayesian inversion with the ETKF, we chose to combine a conventional FWI, based on local optimization, and the ETKF strategies. This scheme allows combining the efficiency of local optimization for solving large scale inverse problems and make the sampling of the local solution space possible thanks to its embarrassingly parallel property. References:Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly weather review, 129(3), 420-436.Thurin, J., Brossier, R. and Métivier, L

An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

NASA Astrophysics Data System (ADS)

McLean, N. M.; Bowring, J. F.; Bowring, S. A.

2011-06-01

High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of calculated U-Pb dates. As analytical techniques have advanced, formerly small sources of uncertainty are increasingly important, and thus previous simplifications for data reduction and uncertainty propagation are no longer valid. Although notable previous efforts have treated propagation of correlated uncertainties for the U-Pb system, the equations, uncertainties, and correlations have been limited in number and subject to simplification during propagation through intermediary calculations. We derive and present a transparent U-Pb data reduction algorithm that transforms raw isotopic data and measured or assumed laboratory parameters into the isotopic ratios and dates geochronologists interpret without making assumptions about the relative size of sample components. To propagate uncertainties and their correlations, we describe, in detail, a linear algebraic algorithm that incorporates all input uncertainties and correlations without limiting or simplifying covariance terms to propagate them though intermediate calculations. Finally, a weighted mean algorithm is presented that utilizes matrix elements from the uncertainty propagation algorithm to propagate random and systematic uncertainties for data comparison between other U-Pb labs and other geochronometers. The linear uncertainty propagation algorithms are verified with Monte Carlo simulations of several typical analyses. We propose that our algorithms be considered by the community for implementation to improve the collaborative science envisioned by the EARTHTIME initiative.

Uncertainty and sensitivity analysis for photovoltaic system modeling.

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

Hansen, Clifford W.; Pohl, Andrew Phillip; Jordan, Dirk

2013-12-01

We report an uncertainty and sensitivity analysis for modeling DC energy from photovoltaic systems. We consider two systems, each comprised of a single module using either crystalline silicon or CdTe cells, and located either at Albuquerque, NM, or Golden, CO. Output from a PV system is predicted by a sequence of models. Uncertainty in the output of each model is quantified by empirical distributions of each model's residuals. We sample these distributions to propagate uncertainty through the sequence of models to obtain an empirical distribution for each PV system's output. We considered models that: (1) translate measured global horizontal, directmore » and global diffuse irradiance to plane-of-array irradiance; (2) estimate effective irradiance from plane-of-array irradiance; (3) predict cell temperature; and (4) estimate DC voltage, current and power. We found that the uncertainty in PV system output to be relatively small, on the order of 1% for daily energy. Four alternative models were considered for the POA irradiance modeling step; we did not find the choice of one of these models to be of great significance. However, we observed that the POA irradiance model introduced a bias of upwards of 5% of daily energy which translates directly to a systematic difference in predicted energy. Sensitivity analyses relate uncertainty in the PV system output to uncertainty arising from each model. We found that the residuals arising from the POA irradiance and the effective irradiance models to be the dominant contributors to residuals for daily energy, for either technology or location considered. This analysis indicates that efforts to reduce the uncertainty in PV system output should focus on improvements to the POA and effective irradiance models.« less

Use of SUSA in Uncertainty and Sensitivity Analysis for INL VHTR Coupled Codes

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

Gerhard Strydom

2010-06-01

The need for a defendable and systematic Uncertainty and Sensitivity approach that conforms to the Code Scaling, Applicability, and Uncertainty (CSAU) process, and that could be used for a wide variety of software codes, was defined in 2008.The GRS (Gesellschaft für Anlagen und Reaktorsicherheit) company of Germany has developed one type of CSAU approach that is particularly well suited for legacy coupled core analysis codes, and a trial version of their commercial software product SUSA (Software for Uncertainty and Sensitivity Analyses) was acquired on May 12, 2010. This interim milestone report provides an overview of the current status of themore » implementation and testing of SUSA at the INL VHTR Project Office.« less

The deuteron-radius puzzle is alive: A new analysis of nuclear structure uncertainties

NASA Astrophysics Data System (ADS)

Hernandez, O. J.; Ekström, A.; Nevo Dinur, N.; Ji, C.; Bacca, S.; Barnea, N.

2018-03-01

To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange correction and the corresponding experimentally inferred value by Pohl et al. [1] remain. The present result is consistent with our previous estimate, although the discrepancy is reduced from 2.6 σ to about 2 σ. The error analysis includes statistic as well as systematic uncertainties stemming from the use of nucleon-nucleon interactions derived from chiral effective field theory at various orders. We therefore conclude that nuclear theory uncertainty is more likely not the source of the discrepancy.

Quantifying uncertainty in discharge measurements: A new approach

USGS Publications Warehouse

Kiang, J.E.; Cohn, T.A.; Mason, R.R.

2009-01-01

The accuracy of discharge measurements using velocity meters and the velocity-area method is typically assessed based on empirical studies that may not correspond to conditions encountered in practice. In this paper, a statistical approach for assessing uncertainty based on interpolated variance estimation (IVE) is introduced. The IVE method quantifies all sources of random uncertainty in the measured data. This paper presents results employing data from sites where substantial over-sampling allowed for the comparison of IVE-estimated uncertainty and observed variability among repeated measurements. These results suggest that the IVE approach can provide approximate estimates of measurement uncertainty. The use of IVE to estimate the uncertainty of a discharge measurement would provide the hydrographer an immediate determination of uncertainty and help determine whether there is a need for additional sampling in problematic river cross sections. ?? 2009 ASCE.

Systematic errors in long baseline oscillation experiments

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

Harris, Deborah A.; /Fermilab

This article gives a brief overview of long baseline neutrino experiments and their goals, and then describes the different kinds of systematic errors that are encountered in these experiments. Particular attention is paid to the uncertainties that come about because of imperfect knowledge of neutrino cross sections and more generally how neutrinos interact in nuclei. Near detectors are planned for most of these experiments, and the extent to which certain uncertainties can be reduced by the presence of near detectors is also discussed.

Asteroseismic modelling of solar-type stars: internal systematics from input physics and surface correction methods

NASA Astrophysics Data System (ADS)

Nsamba, B.; Campante, T. L.; Monteiro, M. J. P. F. G.; Cunha, M. S.; Rendle, B. M.; Reese, D. R.; Verma, K.

2018-07-01

Asteroseismic forward modelling techniques are being used to determine fundamental properties (e.g. mass, radius, and age) of solar-type stars. The need to take into account all possible sources of error is of paramount importance towards a robust determination of stellar properties. We present a study of 34 solar-type stars for which high signal-to-noise asteroseismic data are available from multiyear Kepler photometry. We explore the internal systematics on the stellar properties, that is associated with the uncertainty in the input physics used to construct the stellar models. In particular, we explore the systematics arising from (i) the inclusion of the diffusion of helium and heavy elements; (ii) the uncertainty in solar metallicity mixture; and (iii) different surface correction methods used in optimization/fitting procedures. The systematics arising from comparing results of models with and without diffusion are found to be 0.5 per cent, 0.8 per cent, 2.1 per cent, and 16 per cent in mean density, radius, mass, and age, respectively. The internal systematics in age are significantly larger than the statistical uncertainties. We find the internal systematics resulting from the uncertainty in solar metallicity mixture to be 0.7 per cent in mean density, 0.5 per cent in radius, 1.4 per cent in mass, and 6.7 per cent in age. The surface correction method by Sonoi et al. and Ball & Gizon's two-term correction produce the lowest internal systematics among the different correction methods, namely, ˜1 per cent, ˜1 per cent, ˜2 per cent, and ˜8 per cent in mean density, radius, mass, and age, respectively. Stellar masses obtained using the surface correction methods by Kjeldsen et al. and Ball & Gizon's one-term correction are systematically higher than those obtained using frequency ratios.

Do systematic reviews on pediatric topics need special methodological considerations?

PubMed

Farid-Kapadia, Mufiza; Askie, Lisa; Hartling, Lisa; Contopoulos-Ioannidis, Despina; Bhutta, Zulfiqar A; Soll, Roger; Moher, David; Offringa, Martin

2017-03-06

Systematic reviews are key tools to enable decision making by healthcare providers and policymakers. Despite the availability of the evidence based Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA-2009 and PRISMA-P 2015) statements that were developed to improve the transparency and quality of reporting of systematic reviews, uncertainty on how to deal with pediatric-specific methodological challenges of systematic reviews impairs decision-making in child health. In this paper, we identify methodological challenges specific to the design, conduct and reporting of pediatric systematic reviews, and propose a process to address these challenges. One fundamental decision at the outset of a systematic review is whether to focus on a pediatric population only, or to include both adult and pediatric populations. Both from the policy and patient care point of view, the appropriateness of interventions and comparators administered to pre-defined pediatric age subgroup is critical. Decisions need to be based on the biological plausibility of differences in treatment effects across the developmental trajectory in children. Synthesis of evidence from different trials is often impaired by the use of outcomes and measurement instruments that differ between trials and are neither relevant nor validated in the pediatric population. Other issues specific to pediatric systematic reviews include lack of pediatric-sensitive search strategies and inconsistent choices of pediatric age subgroups in meta-analyses. In addition to these methodological issues generic to all pediatric systematic reviews, special considerations are required for reviews of health care interventions' safety and efficacy in neonatology, global health, comparative effectiveness interventions and individual participant data meta-analyses. To date, there is no standard approach available to overcome this problem. We propose to develop a consensus-based checklist of essential items which

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

Aerosol-type retrieval and uncertainty quantification from OMI data

NASA Astrophysics Data System (ADS)

Kauppi, Anu; Kolmonen, Pekka; Laine, Marko; Tamminen, Johanna

2017-11-01

We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model

A Probabilistic Framework for Quantifying Mixed Uncertainties in Cyber Attacker Payoffs

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

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.

Quantification and propagation of uncertainties in cyber attacker payoffs is a key aspect within multiplayer, stochastic security games. These payoffs may represent penalties or rewards associated with player actions and are subject to various sources of uncertainty, including: (1) cyber-system state, (2) attacker type, (3) choice of player actions, and (4) cyber-system state transitions over time. Past research has primarily focused on representing defender beliefs about attacker payoffs as point utility estimates. More recently, within the physical security domain, attacker payoff uncertainties have been represented as Uniform and Gaussian probability distributions, and mathematical intervals. For cyber-systems, probability distributions may helpmore » address statistical (aleatory) uncertainties where the defender may assume inherent variability or randomness in the factors contributing to the attacker payoffs. However, systematic (epistemic) uncertainties may exist, where the defender may not have sufficient knowledge or there is insufficient information about the attacker’s payoff generation mechanism. Such epistemic uncertainties are more suitably represented as generalizations of probability boxes. This paper explores the mathematical treatment of such mixed payoff uncertainties. A conditional probabilistic reasoning approach is adopted to organize the dependencies between a cyber-system’s state, attacker type, player actions, and state transitions. This also enables the application of probabilistic theories to propagate various uncertainties in the attacker payoffs. An example implementation of this probabilistic framework and resulting attacker payoff distributions are discussed. A goal of this paper is also to highlight this uncertainty quantification problem space to the cyber security research community and encourage further advancements in this area.« less

Decoherence effect on quantum-memory-assisted entropic uncertainty relations

NASA Astrophysics Data System (ADS)

Ming, Fei; Wang, Dong; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

2018-01-01

Uncertainty principle significantly provides a bound to predict precision of measurement with regard to any two incompatible observables, and thereby plays a nontrivial role in quantum precision measurement. In this work, we observe the dynamical features of the quantum-memory-assisted entropic uncertainty relations (EUR) for a pair of incompatible measurements in an open system characterized by local generalized amplitude damping (GAD) noises. Herein, we derive the dynamical evolution of the entropic uncertainty with respect to the measurement affecting by the canonical GAD noises when particle A is initially entangled with quantum memory B. Specifically, we examine the dynamics of EUR in the frame of three realistic scenarios: one case is that particle A is affected by environmental noise (GAD) while particle B as quantum memory is free from any noises, another case is that particle B is affected by the external noise while particle A is not, and the last case is that both of the particles suffer from the noises. By analytical methods, it turns out that the uncertainty is not full dependent of quantum correlation evolution of the composite system consisting of A and B, but the minimal conditional entropy of the measured subsystem. Furthermore, we present a possible physical interpretation for the behavior of the uncertainty evolution by means of the mixedness of the observed system; we argue that the uncertainty might be dramatically correlated with the systematic mixedness. Furthermore, we put forward a simple and effective strategy to reduce the measuring uncertainty of interest upon quantum partially collapsed measurement. Therefore, our explorations might offer an insight into the dynamics of the entropic uncertainty relation in a realistic system, and be of importance to quantum precision measurement during quantum information processing.

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms - Part 2: Ozone DIAL uncertainty budget

NASA Astrophysics Data System (ADS)

Leblanc, Thierry; Sica, Robert J.; van Gijsel, Joanna A. E.; Godin-Beekmann, Sophie; Haefele, Alexander; Trickl, Thomas; Payen, Guillaume; Liberti, Gianluigi

2016-08-01

A standardized approach for the definition, propagation, and reporting of uncertainty in the ozone differential absorption lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One essential aspect of the proposed approach is the propagation in parallel of all independent uncertainty components through the data processing chain before they are combined together to form the ozone combined standard uncertainty. The independent uncertainty components contributing to the overall budget include random noise associated with signal detection, uncertainty due to saturation correction, background noise extraction, the absorption cross sections of O3, NO2, SO2, and O2, the molecular extinction cross sections, and the number densities of the air, NO2, and SO2. The expression of the individual uncertainty components and their step-by-step propagation through the ozone differential absorption lidar (DIAL) processing chain are thoroughly estimated. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which requires knowledge of the covariance matrix when the lidar signal is vertically filtered. In addition, the covariance terms must be taken into account if the same detection hardware is shared by the lidar receiver channels at the absorbed and non-absorbed wavelengths. The ozone uncertainty budget is presented as much as possible in a generic form (i.e., as a function of instrument performance and wavelength) so that all NDACC ozone DIAL investigators across the network can estimate, for their own instrument and in a straightforward manner, the expected impact of each reviewed uncertainty component. In addition, two actual examples of full uncertainty budget are provided, using nighttime measurements from the tropospheric ozone DIAL located at the Jet Propulsion Laboratory (JPL) Table Mountain Facility, California, and nighttime measurements from the JPL

A model-averaging method for assessing groundwater conceptual model uncertainty.

PubMed

Ye, Ming; Pohlmann, Karl F; Chapman, Jenny B; Pohll, Greg M; Reeves, Donald M

2010-01-01

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model-averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.

Large Uncertainty in Estimating pCO2 From Carbonate Equilibria in Lakes

NASA Astrophysics Data System (ADS)

Golub, Malgorzata; Desai, Ankur R.; McKinley, Galen A.; Remucal, Christina K.; Stanley, Emily H.

2017-11-01

Most estimates of carbon dioxide (CO2) evasion from freshwaters rely on calculating partial pressure of aquatic CO2 (pCO2) from two out of three CO2-related parameters using carbonate equilibria. However, the pCO2 uncertainty has not been systematically evaluated across multiple lake types and equilibria. We quantified random errors in pH, dissolved inorganic carbon, alkalinity, and temperature from the North Temperate Lakes Long-Term Ecological Research site in four lake groups across a broad gradient of chemical composition. These errors were propagated onto pCO2 calculated from three carbonate equilibria, and for overlapping observations, compared against uncertainties in directly measured pCO2. The empirical random errors in CO2-related parameters were mostly below 2% of their median values. Resulting random pCO2 errors ranged from ±3.7% to ±31.5% of the median depending on alkalinity group and choice of input parameter pairs. Temperature uncertainty had a negligible effect on pCO2. When compared with direct pCO2 measurements, all parameter combinations produced biased pCO2 estimates with less than one third of total uncertainty explained by random pCO2 errors, indicating that systematic uncertainty dominates over random error. Multidecadal trend of pCO2 was difficult to reconstruct from uncertain historical observations of CO2-related parameters. Given poor precision and accuracy of pCO2 estimates derived from virtually any combination of two CO2-related parameters, we recommend direct pCO2 measurements where possible. To achieve consistently robust estimates of CO2 emissions from freshwater components of terrestrial carbon balances, future efforts should focus on improving accuracy and precision of CO2-related parameters (including direct pCO2) measurements and associated pCO2 calculations.

The effect of additional physiotherapy to hospital inpatients outside of regular business hours: a systematic review.

PubMed

Brusco, Natasha K; Paratz, Jennifer

2006-12-01

Provision of out of regular business hours (OBH) physiotherapy to hospital inpatients is widespread in the hospital setting. This systematic review evaluated the effect of additional OBH physiotherapy services on patient length of stay (LOS), pulmonary complications, discharge destination, discharge mobility status, quality of life, cost saving, adverse events, and mortality compared with physiotherapy only within regular business hours. A literature search was completed on databases with citation tracking using key words. Two reviewers completed data extraction and quality assessment independently by using modified scales for historical cohorts and case control studies as well as the PEDro scale for randomized controlled trials and quasi-randomised controlled trials. This search identified nine articles of low to medium quality. Four reported a significant reduction in LOS associated with additional OBH physiotherapy, with two articles reporting overall significance and two reporting only for specific subgroups. Two studies reported significant reduction in pulmonary complications for two different patient groups in an intensive care unit (ICU) with additional OBH physiotherapy. Three studies accounted for discharge destination and/or discharge mobility status with no significant difference reported. Quality of life, adverse events, and mortality were not reported in any studies. Cost savings were considered in three studies, with two reporting a cost saving. This systematic review was unable to conclude that the provision of additional OBH physiotherapy made significant improvement to patient outcomes for all subgroups of inpatients. One study in critical care reported that overnight physiotherapy decreased LOS and reduced pulmonary complications of patients in the ICU. However, the studies in the area of orthopaedics, neurology, postcardiac surgery, and rheumatology, which all considered additional daytime weekend physiotherapy intervention, did not provide

Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

DOE PAGES

Gatti, M.

2018-02-22

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhoodmore » Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics in future analyses.« less

Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

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

Gatti, M.

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhoodmore » Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics in future analyses.« less

How well do elderly people cope with uncertainty in a learning task?

PubMed

Chasseigne, G; Grau, S; Mullet, E; Cama, V

1999-11-01

The relation between age, task complexity and learning performance in a Multiple Cue Probability Learning task was studied by systematically varying the level of uncertainty present in the task, keeping constant the direction of relationships. Four age groups were constituted: young adults (mean age = 21), middle-aged adults (45), elderly people (69) and very elderly people (81). Five uncertainty levels were considered: predictability = 0.96, 0.80, 0.64, 0.48, and 0.32. All relationships involved were direct ones. A strong effect of uncertainty on 'control', a measure of the subject's consistency with respect to a linear model, was found. This effect was essentially a linear one. To each decrement in predictability of the task corresponded an equal decrement in participants' level of control. This level of decrement was the same, regardless of the age of the participant. It can be concluded that elderly people cope with uncertainty in probability learning tasks as well as young adults.

Toward a definition of intolerance of uncertainty: a review of factor analytical studies of the Intolerance of Uncertainty Scale.

PubMed

Birrell, Jane; Meares, Kevin; Wilkinson, Andrew; Freeston, Mark

2011-11-01

Since its emergence in the early 1990s, a narrow but concentrated body of research has developed examining the role of intolerance of uncertainty (IU) in worry, and yet we still know little about its phenomenology. In an attempt to clarify our understanding of this construct, this paper traces the way in which our understanding and definition of IU have evolved throughout the literature. This paper also aims to further our understanding of IU by exploring the latent variables measures by the Intolerance of Uncertainty Scale (IUS; Freeston, Rheaume, Letarte, Dugas & Ladouceur, 1994). A review of the literature surrounding IU confirmed that the current definitions are categorical and lack specificity. A critical review of existing factor analytic studies was carried out in order to determine the underlying factors measured by the IUS. Systematic searches yielded 9 papers for review. Two factors with 12 consistent items emerged throughout the exploratory studies, and the stability of models containing these two factors was demonstrated in subsequent confirmatory studies. It is proposed that these factors represent (i) desire for predictability and an active engagement in seeking certainty, and (ii) paralysis of cognition and action in the face of uncertainty. It is suggested that these factors may represent approach and avoidance responses to uncertainty. Further research is required to confirm the construct validity of these factors and to determine the stability of this structure within clinical samples. Copyright © 2011 Elsevier Ltd. All rights reserved.

Intrinsic uncertainty on the nature of dark energy

NASA Astrophysics Data System (ADS)

Valkenburg, Wessel; Kunz, Martin; Marra, Valerio

2013-12-01

We argue that there is an intrinsic noise on measurements of the equation of state parameter w = p/ρ from large-scale structure around us. The presence of the large-scale structure leads to an ambiguity in the definition of the background universe and thus there is a maximal precision with which we can determine the equation of state of dark energy. To study the uncertainty due to local structure, we model density perturbations stemming from a standard inflationary power spectrum by means of the exact Lemaître-Tolman-Bondi solution of Einstein’s equation, and show that the usual distribution of matter inhomogeneities in a ΛCDM cosmology causes a variation of w - as inferred from distance measures - of several percent. As we observe only one universe, or equivalently because of the cosmic variance, this uncertainty is systematic in nature.

Uncertainty in Measurement: A Review of Monte Carlo Simulation Using Microsoft Excel for the Calculation of Uncertainties Through Functional Relationships, Including Uncertainties in Empirically Derived Constants

PubMed Central

Farrance, Ian; Frenkel, Robert

2014-01-01

The Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides the basic framework for evaluating uncertainty in measurement. The GUM however does not always provide clearly identifiable procedures suitable for medical laboratory applications, particularly when internal quality control (IQC) is used to derive most of the uncertainty estimates. The GUM modelling approach requires advanced mathematical skills for many of its procedures, but Monte Carlo simulation (MCS) can be used as an alternative for many medical laboratory applications. In particular, calculations for determining how uncertainties in the input quantities to a functional relationship propagate through to the output can be accomplished using a readily available spreadsheet such as Microsoft Excel. The MCS procedure uses algorithmically generated pseudo-random numbers which are then forced to follow a prescribed probability distribution. When IQC data provide the uncertainty estimates the normal (Gaussian) distribution is generally considered appropriate, but MCS is by no means restricted to this particular case. With input variations simulated by random numbers, the functional relationship then provides the corresponding variations in the output in a manner which also provides its probability distribution. The MCS procedure thus provides output uncertainty estimates without the need for the differential equations associated with GUM modelling. The aim of this article is to demonstrate the ease with which Microsoft Excel (or a similar spreadsheet) can be used to provide an uncertainty estimate for measurands derived through a functional relationship. In addition, we also consider the relatively common situation where an empirically derived formula includes one or more ‘constants’, each of which has an empirically derived numerical value. Such empirically derived ‘constants’ must also have associated uncertainties which propagate through the functional

Uncertainty in measurement: a review of monte carlo simulation using microsoft excel for the calculation of uncertainties through functional relationships, including uncertainties in empirically derived constants.

PubMed

Farrance, Ian; Frenkel, Robert

2014-02-01

The Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides the basic framework for evaluating uncertainty in measurement. The GUM however does not always provide clearly identifiable procedures suitable for medical laboratory applications, particularly when internal quality control (IQC) is used to derive most of the uncertainty estimates. The GUM modelling approach requires advanced mathematical skills for many of its procedures, but Monte Carlo simulation (MCS) can be used as an alternative for many medical laboratory applications. In particular, calculations for determining how uncertainties in the input quantities to a functional relationship propagate through to the output can be accomplished using a readily available spreadsheet such as Microsoft Excel. The MCS procedure uses algorithmically generated pseudo-random numbers which are then forced to follow a prescribed probability distribution. When IQC data provide the uncertainty estimates the normal (Gaussian) distribution is generally considered appropriate, but MCS is by no means restricted to this particular case. With input variations simulated by random numbers, the functional relationship then provides the corresponding variations in the output in a manner which also provides its probability distribution. The MCS procedure thus provides output uncertainty estimates without the need for the differential equations associated with GUM modelling. The aim of this article is to demonstrate the ease with which Microsoft Excel (or a similar spreadsheet) can be used to provide an uncertainty estimate for measurands derived through a functional relationship. In addition, we also consider the relatively common situation where an empirically derived formula includes one or more 'constants', each of which has an empirically derived numerical value. Such empirically derived 'constants' must also have associated uncertainties which propagate through the functional relationship

Does taping in addition to physiotherapy improve the outcomes in subacromial impingement syndrome? A systematic review.

PubMed

Saracoglu, Ismail; Emuk, Yusuf; Taspinar, Ferruh

2018-04-01

Taping is used with or without other interventions for many purposes, especially to manage pain and improve functional activity in patients with shoulder pain. The aim of this review was to determine whether any taping technique in addition to physiotherapy care is more effective than physiotherapy care alone in patients with shoulder impingement syndrome. A systematic search of Cochrane Database of Systematic Review, MEDLINE (EBSCO), Physiotherapy Evidence Database (PEDro), CINAHL (EBSCO), PUBMED, AMED, EMBASE (OVID), The Kinesio Kinesio® Taping Method, Kinesio® Tex Tape UK and International websites ( www.kinesiotaping.co.uk ; www.kinesiotaping.com ) was conducted to June 2015. The outcome measures were pain, disability, range of motion and muscle strength. As data were not suitable for meta-analysis, narrative synthesis were applied. Three randomized controlled trials and one controlled trial (135 patients) were included. The results were conflicting and weak on the effectiveness of taping as an adjunct therapy for improvement of pain, disability, range of motion and muscle strength. Clinical taping in addition to physiotherapy interventions (e.g. exercise, electrotherapy, and manual therapy) might be an optional modality for managing patients with shoulder impingement syndrome, especially for the initial stage of the treatment; however, we need further robust, placebo controlled and consistent studies to prove whether it is more effective than physiotherapy interventions without taping.

Uncertainty Analysis in Large Area Aboveground Biomass Mapping

NASA Astrophysics Data System (ADS)

Baccini, A.; Carvalho, L.; Dubayah, R.; Goetz, S. J.; Friedl, M. A.

2011-12-01

Satellite and aircraft-based remote sensing observations are being more frequently used to generate spatially explicit estimates of aboveground carbon stock of forest ecosystems. Because deforestation and forest degradation account for circa 10% of anthropogenic carbon emissions to the atmosphere, policy mechanisms are increasingly recognized as a low-cost mitigation option to reduce carbon emission. They are, however, contingent upon the capacity to accurately measures carbon stored in the forests. Here we examine the sources of uncertainty and error propagation in generating maps of aboveground biomass. We focus on characterizing uncertainties associated with maps at the pixel and spatially aggregated national scales. We pursue three strategies to describe the error and uncertainty properties of aboveground biomass maps, including: (1) model-based assessment using confidence intervals derived from linear regression methods; (2) data-mining algorithms such as regression trees and ensembles of these; (3) empirical assessments using independently collected data sets.. The latter effort explores error propagation using field data acquired within satellite-based lidar (GLAS) acquisitions versus alternative in situ methods that rely upon field measurements that have not been systematically collected for this purpose (e.g. from forest inventory data sets). A key goal of our effort is to provide multi-level characterizations that provide both pixel and biome-level estimates of uncertainties at different scales.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Quantifying radar-rainfall uncertainties in urban drainage flow modelling

NASA Astrophysics Data System (ADS)

Rico-Ramirez, M. A.; Liguori, S.; Schellart, A. N. A.

2015-09-01

This work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.

Uncertainty for Part Density Determination: An Update

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

Valdez, Mario Orlando

2016-12-14

Accurate and precise density measurements by hydrostatic weighing requires the use of an analytical balance, configured with a suspension system, to both measure the weight of a part in water and in air. Additionally, the densities of these liquid media (water and air) must be precisely known for the part density determination. To validate the accuracy and precision of these measurements, uncertainty statements are required. The work in this report is a revision of an original report written more than a decade ago, specifically applying principles and guidelines suggested by the Guide to the Expression of Uncertainty in Measurement (GUM)more » for determining the part density uncertainty through sensitivity analysis. In this work, updated derivations are provided; an original example is revised with the updated derivations and appendix, provided solely to uncertainty evaluations using Monte Carlo techniques, specifically using the NIST Uncertainty Machine, as a viable alternative method.« less

Uncertainty Quantification in Alchemical Free Energy Methods.

PubMed

Bhati, Agastya P; Wan, Shunzhou; Hu, Yuan; Sherborne, Brad; Coveney, Peter V

2018-06-12

Alchemical free energy methods have gained much importance recently from several reports of improved ligand-protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation-an ensemble of independent MD simulations-which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

Differentiating intolerance of uncertainty from three related but distinct constructs.

PubMed

Rosen, Natalie O; Ivanova, Elena; Knäuper, Bärbel

2014-01-01

Individual differences in uncertainty have been associated with heightened anxiety, stress and approach-oriented coping. Intolerance of uncertainty (IU) is a trait characteristic that arises from negative beliefs about uncertainty and its consequences. Researchers have established the central role of IU in the development of problematic worry and maladaptive coping, highlighting the importance of this construct to anxiety disorders. However, there is a need to improve our understanding of the phenomenology of IU. The goal of this paper was to present hypotheses regarding the similarities and differences between IU and three related constructs--intolerance of ambiguity, uncertainty orientation, and need for cognitive closure--and to call for future empirical studies to substantiate these hypotheses. To assist with achieving this goal, we conducted a systematic review of the literature, which also served to identify current gaps in knowledge. This paper differentiates these constructs by outlining each definition and general approaches to assessment, reviewing the existing empirical relations, and proposing theoretical similarities and distinctions. Findings may assist researchers in selecting the appropriate construct to address their research questions. Future research directions for the application of these constructs, particularly within the field of clinical and health psychology, are discussed.

Methods for Assessing Uncertainties in Climate Change, Impacts and Responses (Invited)

NASA Astrophysics Data System (ADS)

Manning, M. R.; Swart, R.

2009-12-01

Assessing the scientific uncertainties or confidence levels for the many different aspects of climate change is particularly important because of the seriousness of potential impacts and the magnitude of economic and political responses that are needed to mitigate climate change effectively. This has made the treatment of uncertainty and confidence a key feature in the assessments carried out by the Intergovernmental Panel on Climate Change (IPCC). Because climate change is very much a cross-disciplinary area of science, adequately dealing with uncertainties requires recognition of their wide range and different perspectives on assessing and communicating those uncertainties. The structural differences that exist across disciplines are often embedded deeply in the corresponding literature that is used as the basis for an IPCC assessment. The assessment of climate change science by the IPCC has from its outset tried to report the levels of confidence and uncertainty in the degree of understanding in both the underlying multi-disciplinary science and in projections for future climate. The growing recognition of the seriousness of this led to the formation of a detailed approach for consistent treatment of uncertainties in the IPCC’s Third Assessment Report (TAR) [Moss and Schneider, 2000]. However, in completing the TAR there remained some systematic differences between the disciplines raising concerns about the level of consistency. So further consideration of a systematic approach to uncertainties was undertaken for the Fourth Assessment Report (AR4). The basis for the approach used in the AR4 was developed at an expert meeting of scientists representing many different disciplines. This led to the introduction of a broader way of addressing uncertainties in the AR4 [Manning et al., 2004] which was further refined by lengthy discussions among many IPCC Lead Authors, for over a year, resulting in a short summary of a standard approach to be followed for that

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

NASA Astrophysics Data System (ADS)

Jones, D. O.; Scolnic, D. M.; Riess, A. G.; Kessler, R.; Rest, A.; Kirshner, R. P.; Berger, E.; Ortega, C. A.; Foley, R. J.; Chornock, R.; Challis, P. J.; Burgett, W. S.; Chambers, K. C.; Draper, P. W.; Flewelling, H.; Huber, M. E.; Kaiser, N.; Kudritzki, R.-P.; Metcalfe, N.; Wainscoat, R. J.; Waters, C.

2017-07-01

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3147 of these likely SNe and estimate that ˜1000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve-based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on w ({σ }w{CC}) of 0.014, 29% of the statistical uncertainty. Our best method gives {σ }w{CC}=0.004, just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, α and β, with ˜3% bias. However, we find that some variants require α and β to be fixed to known values for BEAMS to yield accurate measurements of w. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions.

Technical note: Design flood under hydrological uncertainty

NASA Astrophysics Data System (ADS)

Botto, Anna; Ganora, Daniele; Claps, Pierluigi; Laio, Francesco

2017-07-01

Planning and verification of hydraulic infrastructures require a design estimate of hydrologic variables, usually provided by frequency analysis, and neglecting hydrologic uncertainty. However, when hydrologic uncertainty is accounted for, the design flood value for a specific return period is no longer a unique value, but is represented by a distribution of values. As a consequence, the design flood is no longer univocally defined, making the design process undetermined. The Uncertainty Compliant Design Flood Estimation (UNCODE) procedure is a novel approach that, starting from a range of possible design flood estimates obtained in uncertain conditions, converges to a single design value. This is obtained through a cost-benefit criterion with additional constraints that is numerically solved in a simulation framework. This paper contributes to promoting a practical use of the UNCODE procedure without resorting to numerical computation. A modified procedure is proposed by using a correction coefficient that modifies the standard (i.e., uncertainty-free) design value on the basis of sample length and return period only. The procedure is robust and parsimonious, as it does not require additional parameters with respect to the traditional uncertainty-free analysis. Simple equations to compute the correction term are provided for a number of probability distributions commonly used to represent the flood frequency curve. The UNCODE procedure, when coupled with this simple correction factor, provides a robust way to manage the hydrologic uncertainty and to go beyond the use of traditional safety factors. With all the other parameters being equal, an increase in the sample length reduces the correction factor, and thus the construction costs, while still keeping the same safety level.

Estimating discharge measurement uncertainty using the interpolated variance estimator

USGS Publications Warehouse

Cohn, T.; Kiang, J.; Mason, R.

2012-01-01

Methods for quantifying the uncertainty in discharge measurements typically identify various sources of uncertainty and then estimate the uncertainty from each of these sources by applying the results of empirical or laboratory studies. If actual measurement conditions are not consistent with those encountered in the empirical or laboratory studies, these methods may give poor estimates of discharge uncertainty. This paper presents an alternative method for estimating discharge measurement uncertainty that uses statistical techniques and at-site observations. This Interpolated Variance Estimator (IVE) estimates uncertainty based on the data collected during the streamflow measurement and therefore reflects the conditions encountered at the site. The IVE has the additional advantage of capturing all sources of random uncertainty in the velocity and depth measurements. It can be applied to velocity-area discharge measurements that use a velocity meter to measure point velocities at multiple vertical sections in a channel cross section.

Uncertainty-based Optimization Algorithms in Designing Fractionated Spacecraft

PubMed Central

Ning, Xin; Yuan, Jianping; Yue, Xiaokui

2016-01-01

A fractionated spacecraft is an innovative application of a distributive space system. To fully understand the impact of various uncertainties on its development, launch and in-orbit operation, we use the stochastic missioncycle cost to comprehensively evaluate the survivability, flexibility, reliability and economy of the ways of dividing the various modules of the different configurations of fractionated spacecraft. We systematically describe its concept and then analyze its evaluation and optimal design method that exists during recent years and propose the stochastic missioncycle cost for comprehensive evaluation. We also establish the models of the costs such as module development, launch and deployment and the impacts of their uncertainties respectively. Finally, we carry out the Monte Carlo simulation of the complete missioncycle costs of various configurations of the fractionated spacecraft under various uncertainties and give and compare the probability density distribution and statistical characteristics of its stochastic missioncycle cost, using the two strategies of timing module replacement and non-timing module replacement. The simulation results verify the effectiveness of the comprehensive evaluation method and show that our evaluation method can comprehensively evaluate the adaptability of the fractionated spacecraft under different technical and mission conditions. PMID:26964755

Extrapolation, uncertainty factors, and the precautionary principle.

PubMed

Steel, Daniel

2011-09-01

This essay examines the relationship between the precautionary principle and uncertainty factors used by toxicologists to estimate acceptable exposure levels for toxic chemicals from animal experiments. It shows that the adoption of uncertainty factors in the United States in the 1950s can be understood by reference to the precautionary principle, but not by cost-benefit analysis because of a lack of relevant quantitative data at that time. In addition, it argues that uncertainty factors continue to be relevant to efforts to implement the precautionary principle and that the precautionary principle should not be restricted to cases involving unquantifiable hazards. Copyright © 2011 Elsevier Ltd. All rights reserved.

ISA implementation and uncertainty: a literature review and expert elicitation study.

PubMed

van der Pas, J W G M; Marchau, V A W J; Walker, W E; van Wee, G P; Vlassenroot, S H

2012-09-01

Each day, an average of over 116 people die from traffic accidents in the European Union. One out of three fatalities is estimated to be the result of speeding. The current state of technology makes it possible to make speeding more difficult, or even impossible, by placing intelligent speed limiters (so called ISA devices) in vehicles. Although the ISA technology has been available for some years now, and reducing the number of road traffic fatalities and injuries has been high on the European political agenda, implementation still seems to be far away. Experts indicate that there are still too many uncertainties surrounding ISA implementation, and dealing with these uncertainties is essential for implementing ISA. In this paper, a systematic and representative inventory of the uncertainties is made based upon the literature. Furthermore, experts in the field of ISA were surveyed and asked which uncertainties are barriers for ISA implementation, and how uncertain these uncertainties are. We found that the long-term effects and the effects of large-scale implementation of ISA are still uncertain and are the most important barriers for the implementation of the most effective types of ISA. One way to deal with these uncertainties would be to start implementation on a small scale and gradually expand the penetration, in order to learn how ISA influences the transport system over time. Copyright © 2010 Elsevier Ltd. All rights reserved.

Novel approaches to reducing uncertainty in regional climate predictions (Invited)

NASA Astrophysics Data System (ADS)

Ammann, C. M.

2009-12-01

Regional planning in preparation for future climate changes is rapidly gaining importance. However, compared to the global mean projections, correctly anticipating regional climate is often much more difficult, particularly with regard to hydrologic changes. The reason for the high, inherent uncertainty in location specific forecasts arises on one hand from the superposition of large internal variability in the atmosphere-ocean system on the more gradual changes. On the other hand, this problem is confounded by the fact that regional climate records are often short and therefore offer little guidance as to how an underlying trend can be identified within the noise. The use of indirect climate information (proxy records) from a host of natural archives has made significant progress recently. Based on an extended record, process studies can help reveal the regional response to changes in large scale climate that likely have to be expected. But in order to come up with robust, season and parameter specific (temperature versus moisture) climate reconstructions, comprehensive data compilations are needed that integrate proxy records of different characteristics, temporal representations, and different systematic and sampling uncertainties. Based on understanding of physical processes, and making explicit use of that knowledge, new dynamical and statistical techniques in paleoclimatology are being developed and explored. In addition to improved estimates of the past climate, the cascade of uncertainties is directly taken into account so that errors can more comprehensively be assessed. A brief overview of the problems and its potential implications for regional planning is followed by an application that demonstrates how collaboration between paleoclimatologists, climate modelers and statisticians can advance our understanding of the climate system and how agencies, businesses and individuals might be able to make better informed decisions in preparation for future

Uncertainty quantification in flood risk assessment

NASA Astrophysics Data System (ADS)

Blöschl, Günter; Hall, Julia; Kiss, Andrea; Parajka, Juraj; Perdigão, Rui A. P.; Rogger, Magdalena; Salinas, José Luis; Viglione, Alberto

2017-04-01

Uncertainty is inherent to flood risk assessments because of the complexity of the human-water system, which is characterised by nonlinearities and interdependencies, because of limited knowledge about system properties and because of cognitive biases in human perception and decision-making. On top of the uncertainty associated with the assessment of the existing risk to extreme events, additional uncertainty arises because of temporal changes in the system due to climate change, modifications of the environment, population growth and the associated increase in assets. Novel risk assessment concepts are needed that take into account all these sources of uncertainty. They should be based on the understanding of how flood extremes are generated and how they change over time. They should also account for the dynamics of risk perception of decision makers and population in the floodplains. In this talk we discuss these novel risk assessment concepts through examples from Flood Frequency Hydrology, Socio-Hydrology and Predictions Under Change. We believe that uncertainty quantification in flood risk assessment should lead to a robust approach of integrated flood risk management aiming at enhancing resilience rather than searching for optimal defense strategies.

Structural Uncertainty in Antarctic sea ice simulations

NASA Astrophysics Data System (ADS)

Schneider, D. P.

2016-12-01

The inability of the vast majority of historical climate model simulations to reproduce the observed increase in Antarctic sea ice has motivated many studies about the quality of the observational record, the role of natural variability versus forced changes, and the possibility of missing or inadequate forcings in the models (such as freshwater discharge from thinning ice shelves or an inadequate magnitude of stratospheric ozone depletion). In this presentation I will highlight another source of uncertainty that has received comparatively little attention: Structural uncertainty, that is, the systematic uncertainty in simulated sea ice trends that arises from model physics and mean-state biases. Using two large ensembles of experiments from the Community Earth System Model (CESM), I will show that the model is predisposed towards producing negative Antarctic sea ice trends during 1979-present, and that this outcome is not simply because the model's decadal variability is out-of-synch with that in nature. In the "Tropical Pacific Pacemaker" ensemble, in which observed tropical Pacific SST anomalies are prescribed, the model produces very realistic atmospheric circulation trends over the Southern Ocean, yet the sea ice trend is negative in every ensemble member. However, if the ensemble-mean trend (commonly interpreted as the forced response) is removed, some ensemble members show a sea ice increase that is very similar to the observed. While this results does confirm the important role of natural variability, it also suggests a strong bias in the forced response. I will discuss the reasons for this systematic bias and explore possible remedies. This an important problem to solve because projections of 21st -Century changes in the Antarctic climate system (including ice sheet surface mass balance changes and related changes in the sea level budget) have a strong dependence on the mean state of and changes in the Antarctic sea ice cover. This problem is not unique to

A Peep into the Uncertainty-Complexity-Relevance Modeling Trilemma through Global Sensitivity and Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.

2014-12-01

Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping

Incorporating climate change into systematic conservation planning

USGS Publications Warehouse

Groves, Craig R.; Game, Edward T.; Anderson, Mark G.; Cross, Molly; Enquist, Carolyn; Ferdana, Zach; Girvetz, Evan; Gondor, Anne; Hall, Kimberly R.; Higgins, Jonathan; Marshall, Rob; Popper, Ken; Schill, Steve; Shafer, Sarah L.

2012-01-01

The principles of systematic conservation planning are now widely used by governments and non-government organizations alike to develop biodiversity conservation plans for countries, states, regions, and ecoregions. Many of the species and ecosystems these plans were designed to conserve are now being affected by climate change, and there is a critical need to incorporate new and complementary approaches into these plans that will aid species and ecosystems in adjusting to potential climate change impacts. We propose five approaches to climate change adaptation that can be integrated into existing or new biodiversity conservation plans: (1) conserving the geophysical stage, (2) protecting climatic refugia, (3) enhancing regional connectivity, (4) sustaining ecosystem process and function, and (5) capitalizing on opportunities emerging in response to climate change. We discuss both key assumptions behind each approach and the trade-offs involved in using the approach for conservation planning. We also summarize additional data beyond those typically used in systematic conservation plans required to implement these approaches. A major strength of these approaches is that they are largely robust to the uncertainty in how climate impacts may manifest in any given region.

Space Radiation Cancer Risks and Uncertainties for Mars Missions

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Schimmerling, W.; Wilson, J. W.; Peterson, L. E.; Badhwar, G. D.; Saganti, P. B.; Dicello, J. F.

2001-01-01

Projecting cancer risks from exposure to space radiation is highly uncertain because of the absence of data for humans and because of the limited radiobiology data available for estimating late effects from the high-energy and charge (HZE) ions present in the galactic cosmic rays (GCR). Cancer risk projections involve many biological and physical factors, each of which has a differential range of uncertainty due to the lack of data and knowledge. We discuss an uncertainty assessment within the linear-additivity model using the approach of Monte Carlo sampling from subjective error distributions that represent the lack of knowledge in each factor to quantify the overall uncertainty in risk projections. Calculations are performed using the space radiation environment and transport codes for several Mars mission scenarios. This approach leads to estimates of the uncertainties in cancer risk projections of 400-600% for a Mars mission. The uncertainties in the quality factors are dominant. Using safety standards developed for low-Earth orbit, long-term space missions (>90 days) outside the Earth's magnetic field are currently unacceptable if the confidence levels in risk projections are considered. Because GCR exposures involve multiple particle or delta-ray tracks per cellular array, our results suggest that the shape of the dose response at low dose rates may be an additional uncertainty for estimating space radiation risks.

Accounting for Parameter Uncertainty in Complex Atmospheric Models, With an Application to Greenhouse Gas Emissions Evaluation

NASA Astrophysics Data System (ADS)

Swallow, B.; Rigby, M. L.; Rougier, J.; Manning, A.; Thomson, D.; Webster, H. N.; Lunt, M. F.; O'Doherty, S.

2016-12-01

In order to understand underlying processes governing environmental and physical phenomena, a complex mathematical model is usually required. However, there is an inherent uncertainty related to the parameterisation of unresolved processes in these simulators. Here, we focus on the specific problem of accounting for uncertainty in parameter values in an atmospheric chemical transport model. Systematic errors introduced by failing to account for these uncertainties have the potential to have a large effect on resulting estimates in unknown quantities of interest. One approach that is being increasingly used to address this issue is known as emulation, in which a large number of forward runs of the simulator are carried out, in order to approximate the response of the output to changes in parameters. However, due to the complexity of some models, it is often unfeasible to run large numbers of training runs that is usually required for full statistical emulators of the environmental processes. We therefore present a simplified model reduction method for approximating uncertainties in complex environmental simulators without the need for very large numbers of training runs. We illustrate the method through an application to the Met Office's atmospheric transport model NAME. We show how our parameter estimation framework can be incorporated into a hierarchical Bayesian inversion, and demonstrate the impact on estimates of UK methane emissions, using atmospheric mole fraction data. We conclude that accounting for uncertainties in the parameterisation of complex atmospheric models is vital if systematic errors are to be minimized and all relevant uncertainties accounted for. We also note that investigations of this nature can prove extremely useful in highlighting deficiencies in the simulator that might otherwise be missed.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

Measurement uncertainty of liquid chromatographic analyses visualized by Ishikawa diagrams.

PubMed

Meyer, Veronika R

2003-09-01

Ishikawa, or cause-and-effect diagrams, help to visualize the parameters that influence a chromatographic analysis. Therefore, they facilitate the set up of the uncertainty budget of the analysis, which can then be expressed in mathematical form. If the uncertainty is calculated as the Gaussian sum of all uncertainty parameters, it is necessary to quantitate them all, a task that is usually not practical. The other possible approach is to use the intermediate precision as a base for the uncertainty calculation. In this case, it is at least necessary to consider the uncertainty of the purity of the reference material in addition to the precision data. The Ishikawa diagram is then very simple, and so is the uncertainty calculation. This advantage is given by the loss of information about the parameters that influence the measurement uncertainty.

Measurement uncertainty budget of an interferometric flow velocity sensor

NASA Astrophysics Data System (ADS)

Bermuske, Mike; Büttner, Lars; Czarske, Jürgen

2017-06-01

Flow rate measurements are a common topic for process monitoring in chemical engineering and food industry. To achieve the requested low uncertainties of 0:1% for flow rate measurements, a precise measurement of the shear layers of such flows is necessary. The Laser Doppler Velocimeter (LDV) is an established method for measuring local flow velocities. For exact estimation of the flow rate, the flow profile in the shear layer is of importance. For standard LDV the axial resolution and therefore the number of measurement points in the shear layer is defined by the length of the measurement volume. A decrease of this length is accompanied by a larger fringe distance variation along the measurement axis which results in a rise of the measurement uncertainty for the flow velocity (uncertainty relation between spatial resolution and velocity uncertainty). As a unique advantage, the laser Doppler profile sensor (LDV-PS) overcomes this problem by using two fan-like fringe systems to obtain the position of the measured particles along the measurement axis and therefore achieve a high spatial resolution while it still offers a low velocity uncertainty. With this technique, the flow rate can be estimated with one order of magnitude lower uncertainty, down to 0:05% statistical uncertainty.1 And flow profiles especially in film flows can be measured more accurately. The problem for this technique is, in contrast to laboratory setups where the system is quite stable, that for industrial applications the sensor needs a reliable and robust traceability to the SI units, meter and second. Small deviations in the calibration can, because of the highly position depending calibration function, cause large systematic errors in the measurement result. Therefore, a simple, stable and accurate tool is needed, that can easily be used in industrial surroundings to check or recalibrate the sensor. In this work, different calibration methods are presented and their influences to the

Impact of Exposure Uncertainty on the Association between Perfluorooctanoate and Preeclampsia in the C8 Health Project Population.

PubMed

Avanasi, Raghavendhran; Shin, Hyeong-Moo; Vieira, Verónica M; Savitz, David A; Bartell, Scott M

2016-01-01

Uncertainty in exposure estimates from models can result in exposure measurement error and can potentially affect the validity of epidemiological studies. We recently used a suite of environmental models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations and assess the association with preeclampsia from 1990 through 2006 for the C8 Health Project participants. The aims of the current study are to evaluate impact of uncertainty in estimated PFOA drinking-water concentrations on estimated serum concentrations and their reported epidemiological association with preeclampsia. For each individual public water district, we used Monte Carlo simulations to vary the year-by-year PFOA drinking-water concentration by randomly sampling from lognormal distributions for random error in the yearly public water district PFOA concentrations, systematic error specific to each water district, and global systematic error in the release assessment (using the estimated concentrations from the original fate and transport model as medians and a range of 2-, 5-, and 10-fold uncertainty). Uncertainty in PFOA water concentrations could cause major changes in estimated serum PFOA concentrations among participants. However, there is relatively little impact on the resulting epidemiological association in our simulations. The contribution of exposure uncertainty to the total uncertainty (including regression parameter variance) ranged from 5% to 31%, and bias was negligible. We found that correlated exposure uncertainty can substantially change estimated PFOA serum concentrations, but results in only minor impacts on the epidemiological association between PFOA and preeclampsia. Avanasi R, Shin HM, Vieira VM, Savitz DA, Bartell SM. 2016. Impact of exposure uncertainty on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Health Perspect 124:126-132; http://dx.doi.org/10

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

DTIC Science & Technology

2012-09-01

94035, USA abhinav.saxena@nasa.gov ABSTRACT Prognostics deals with the prediction of the end of life ( EOL ) of a system. EOL is a random variable, due...future evolution of the system, accumulating additional uncertainty into the predicted EOL . Prediction algorithms that do not account for these sources of...uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

USDA-ARS?s Scientific Manuscript database

Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multisp...

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Saxena, Abhinav; Daigle, Matthew; Goebel, Kai

2013-01-01

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Recognizing and responding to uncertainty: a grounded theory of nurses' uncertainty.

PubMed

Cranley, Lisa A; Doran, Diane M; Tourangeau, Ann E; Kushniruk, Andre; Nagle, Lynn

2012-08-01

There has been little research to date exploring nurses' uncertainty in their practice. Understanding nurses' uncertainty is important because it has potential implications for how care is delivered. The purpose of this study is to develop a substantive theory to explain how staff nurses experience and respond to uncertainty in their practice. Between 2006 and 2008, a grounded theory study was conducted that included in-depth semi-structured interviews. Fourteen staff nurses working in adult medical-surgical intensive care units at two teaching hospitals in Ontario, Canada, participated in the study. The theory recognizing and responding to uncertainty characterizes the processes through which nurses' uncertainty manifested and how it was managed. Recognizing uncertainty involved the processes of assessing, reflecting, questioning, and/or being unable to predict aspects of the patient situation. Nurses' responses to uncertainty highlighted the cognitive-affective strategies used to manage uncertainty. Study findings highlight the importance of acknowledging uncertainty and having collegial support to manage uncertainty. The theory adds to our understanding the processes involved in recognizing uncertainty, strategies and outcomes of managing uncertainty, and influencing factors. Tailored nursing education programs should be developed to assist nurses in developing skills in articulating and managing their uncertainty. Further research is needed to extend, test and refine the theory of recognizing and responding to uncertainty to develop strategies for managing uncertainty. This theory advances the nursing perspective of uncertainty in clinical practice. The theory is relevant to nurses who are faced with uncertainty and complex clinical decisions, to managers who support nurses in their clinical decision-making, and to researchers who investigate ways to improve decision-making and care delivery. ©2012 Sigma Theta Tau International.

Flood resilience and uncertainty in flood risk assessment

NASA Astrophysics Data System (ADS)

Beven, K.; Leedal, D.; Neal, J.; Bates, P.; Hunter, N.; Lamb, R.; Keef, C.

2012-04-01

Flood risk assessments do not normally take account of the uncertainty in assessing flood risk. There is no requirement in the EU Floods Directive to do so. But given the generally short series (and potential non-stationarity) of flood discharges, the extrapolation to smaller exceedance potentials may be highly uncertain. This means that flood risk mapping may also be highly uncertainty, with additional uncertainties introduced by the representation of flood plain and channel geometry, conveyance and infrastructure. This suggests that decisions about flood plain management should be based on exceedance probability of risk rather than the deterministic hazard maps that are common in most EU countries. Some examples are given from 2 case studies in the UK where a framework for good practice in assessing uncertainty in flood risk mapping has been produced as part of the Flood Risk Management Research Consortium and Catchment Change Network Projects. This framework provides a structure for the communication and audit of assumptions about uncertainties.

Uncertainty Quantification Techniques of SCALE/TSUNAMI

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

Rearden, Bradley T; Mueller, Don

2011-01-01

an additional administrative margin to account for gap in the validation data or to conclude that the impact on the calculated bias and bias uncertainty is negligible. As a result of advances in computer programs and the evolution of cross-section covariance data, analysts can use the sensitivity and uncertainty analysis tools in the TSUNAMI codes to estimate the potential impact on the application-specific bias and bias uncertainty resulting from nuclides not represented in available benchmark experiments. This paper presents the application of methods described in a companion paper.« less

Optimization Under Uncertainty for Wake Steering Strategies

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

Quick, Julian; Annoni, Jennifer; King, Ryan N.

Here, wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in themore » presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.« less

Optimization Under Uncertainty for Wake Steering Strategies

NASA Astrophysics Data System (ADS)

Quick, Julian; Annoni, Jennifer; King, Ryan; Dykes, Katherine; Fleming, Paul; Ning, Andrew

2017-05-01

Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as “wake steering,” in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.

Optimization Under Uncertainty for Wake Steering Strategies

DOE PAGES

Quick, Julian; Annoni, Jennifer; King, Ryan N.; ...

2017-06-13

Here, wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in themore » presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.« less

Understanding the origin of Paris Agreement emission uncertainties

NASA Astrophysics Data System (ADS)

Rogelj, Joeri; Fricko, Oliver; Meinshausen, Malte; Krey, Volker; Zilliacus, Johanna J. J.; Riahi, Keywan

2017-06-01

The UN Paris Agreement puts in place a legally binding mechanism to increase mitigation action over time. Countries put forward pledges called nationally determined contributions (NDC) whose impact is assessed in global stocktaking exercises. Subsequently, actions can then be strengthened in light of the Paris climate objective: limiting global mean temperature increase to well below 2 °C and pursuing efforts to limit it further to 1.5 °C. However, pledged actions are currently described ambiguously and this complicates the global stocktaking exercise. Here, we systematically explore possible interpretations of NDC assumptions, and show that this results in estimated emissions for 2030 ranging from 47 to 63 GtCO2e yr-1. We show that this uncertainty has critical implications for the feasibility and cost to limit warming well below 2 °C and further to 1.5 °C. Countries are currently working towards clarifying the modalities of future NDCs. We identify salient avenues to reduce the overall uncertainty by about 10 percentage points through simple, technical clarifications regarding energy accounting rules. Remaining uncertainties depend to a large extent on politically valid choices about how NDCs are expressed, and therefore raise the importance of a thorough and robust process that keeps track of where emissions are heading over time.

Uncertainty Analysis of Sonic Boom Levels Measured in a Simulator at NASA Langley

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Ely, Jeffry W.

2012-01-01

A sonic boom simulator has been constructed at NASA Langley Research Center for testing the human response to sonic booms heard indoors. Like all measured quantities, sonic boom levels in the simulator are subject to systematic and random errors. To quantify these errors, and their net influence on the measurement result, a formal uncertainty analysis is conducted. Knowledge of the measurement uncertainty, or range of values attributable to the quantity being measured, enables reliable comparisons among measurements at different locations in the simulator as well as comparisons with field data or laboratory data from other simulators. The analysis reported here accounts for acoustic excitation from two sets of loudspeakers: one loudspeaker set at the facility exterior that reproduces the exterior sonic boom waveform and a second set of interior loudspeakers for reproducing indoor rattle sounds. The analysis also addresses the effect of pressure fluctuations generated when exterior doors of the building housing the simulator are opened. An uncertainty budget is assembled to document each uncertainty component, its sensitivity coefficient, and the combined standard uncertainty. The latter quantity will be reported alongside measurement results in future research reports to indicate data reliability.

Certainty Equivalence M-MRAC for Systems with Unmatched Uncertainties

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

The paper presents a certainty equivalence state feedback indirect adaptive control design method for the systems of any relative degree with unmatched uncertainties. The approach is based on the parameter identification (estimation) model, which is completely separated from the control design and is capable of producing parameter estimates as fast as the computing power allows without generating high frequency oscillations. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters.

Uncertainty Quantification for Ice Sheet Science and Sea Level Projections

NASA Astrophysics Data System (ADS)

Boening, C.; Schlegel, N.; Limonadi, D.; Schodlok, M.; Seroussi, H. L.; Larour, E. Y.; Watkins, M. M.

2017-12-01

In order to better quantify uncertainties in global mean sea level rise projections and in particular upper bounds, we aim at systematically evaluating the contributions from ice sheets and potential for extreme sea level rise due to sudden ice mass loss. Here, we take advantage of established uncertainty quantification tools embedded within the Ice Sheet System Model (ISSM) as well as sensitivities to ice/ocean interactions using melt rates and melt potential derived from MITgcm/ECCO2. With the use of these tools, we conduct Monte-Carlo style sampling experiments on forward simulations of the Antarctic ice sheet, by varying internal parameters and boundary conditions of the system over both extreme and credible worst-case ranges. Uncertainty bounds for climate forcing are informed by CMIP5 ensemble precipitation and ice melt estimates for year 2100, and uncertainty bounds for ocean melt rates are derived from a suite of regional sensitivity experiments using MITgcm. Resulting statistics allow us to assess how regional uncertainty in various parameters affect model estimates of century-scale sea level rise projections. The results inform efforts to a) isolate the processes and inputs that are most responsible for determining ice sheet contribution to sea level; b) redefine uncertainty brackets for century-scale projections; and c) provide a prioritized list of measurements, along with quantitative information on spatial and temporal resolution, required for reducing uncertainty in future sea level rise projections. Results indicate that ice sheet mass loss is dependent on the spatial resolution of key boundary conditions - such as bedrock topography and melt rates at the ice-ocean interface. This work is performed at and supported by the California Institute of Technology's Jet Propulsion Laboratory. Supercomputing time is also supported through a contract with the National Aeronautics and Space Administration's Cryosphere program.

Uncertainties in the cluster-cluster correlation function

NASA Astrophysics Data System (ADS)

Ling, E. N.; Frenk, C. S.; Barrow, J. D.

1986-12-01

The bootstrap resampling technique is applied to estimate sampling errors and significance levels of the two-point correlation functions determined for a subset of the CfA redshift survey of galaxies and a redshift sample of 104 Abell clusters. The angular correlation function for a sample of 1664 Abell clusters is also calculated. The standard errors in xi(r) for the Abell data are found to be considerably larger than quoted 'Poisson errors'. The best estimate for the ratio of the correlation length of Abell clusters (richness class R greater than or equal to 1, distance class D less than or equal to 4) to that of CfA galaxies is 4.2 + 1.4 or - 1.0 (68 percentile error). The enhancement of cluster clustering over galaxy clustering is statistically significant in the presence of resampling errors. The uncertainties found do not include the effects of possible systematic biases in the galaxy and cluster catalogs and could be regarded as lower bounds on the true uncertainty range.

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm.

PubMed

Lee, ZhongPing; Arnone, Robert; Hu, Chuanmin; Werdell, P Jeremy; Lubac, Bertrand

2010-01-20

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Instrument Drift Uncertainties and the Long-Term TOMS/SBUV Total Ozone Record

NASA Technical Reports Server (NTRS)

Solarski, Richard S.; Frith, Stacey

2005-01-01

Long-term climate records from satellites are often constructed from the measurements of a sequence of instruments launched at different times. Each of these instruments is calibrated prior to launch. After launch they are subjected to potential offsets and slow drifts in calibration. We illustrate these issues in the construction of a merged total ozone record from two TOMS and three SBUV instruments. This record extends from late 1978 through the present. The question is "How good are these records?". We have examined the uncertainty in determining the relative calibration of two instruments during an overlap period in their measurements. When comparing a TOMS instrument, such as that on Nimbus 7, with an SBUV instrument, also on Nimbus 7, we find systematic differences and random differences. We have combined these findings with estimates of individual instrument drift into a monte- carlo uncertainty propagation model. We estimate an instrument drift uncertainty of a little larger than 1 percent per decade over the 25-year history of the TOMS/SBUV measurements. We make an independent estimate of the drift uncertainty in the ground-based network of total ozone measurements and find it to be of similar, but slightly smaller magnitude. The implications of these uncertainties for trend and recovery determination will be discussed.

Uncertainty in the Modeling of Tsunami Sediment Transport

NASA Astrophysics Data System (ADS)

Jaffe, B. E.; Sugawara, D.; Goto, K.; Gelfenbaum, G. R.; La Selle, S.

2016-12-01

Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. A recent study (Jaffe et al., 2016) explores sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami properties, study site characteristics, available input data, sediment grain size, and the model used. Although uncertainty has the potential to be large, case studies for both forward and inverse models have shown that sediment transport modeling provides useful information on tsunami inundation and hydrodynamics that can be used to improve tsunami hazard assessment. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and the development of hybrid modeling approaches to exploit the strengths of forward and inverse models. As uncertainty in tsunami sediment transport modeling is reduced, and with increased ability to quantify uncertainty, the geologic record of tsunamis will become more valuable in the assessment of tsunami hazard. Jaffe, B., Goto, K., Sugawara, D., Gelfenbaum, G., and La Selle, S., "Uncertainty in Tsunami Sediment Transport Modeling", Journal of Disaster Research Vol. 11 No. 4, pp. 647-661, 2016, doi: 10.20965/jdr.2016.p0647 https://www.fujipress.jp/jdr/dr/dsstr001100040647/

Forensic Uncertainty Quantification of Explosive Dispersal of Particles

NASA Astrophysics Data System (ADS)

Hughes, Kyle; Park, Chanyoung; Haftka, Raphael; Kim, Nam-Ho

2017-06-01

In addition to the numerical challenges of simulating the explosive dispersal of particles, validation of the simulation is often plagued with poor knowledge of the experimental conditions. The level of experimental detail required for validation is beyond what is usually included in the literature. This presentation proposes the use of forensic uncertainty quantification (UQ) to investigate validation-quality experiments to discover possible sources of uncertainty that may have been missed in initial design of experiments or under-reported. The current experience of the authors has found that by making an analogy to crime scene investigation when looking at validation experiments, valuable insights may be gained. One examines all the data and documentation provided by the validation experimentalists, corroborates evidence, and quantifies large sources of uncertainty a posteriori with empirical measurements. In addition, it is proposed that forensic UQ may benefit from an independent investigator to help remove possible implicit biases and increases the likelihood of discovering unrecognized uncertainty. Forensic UQ concepts will be discussed and then applied to a set of validation experiments performed at Eglin Air Force Base. This work was supported in part by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program.

Uncertainty reduction in intensity modulated proton therapy by inverse Monte Carlo treatment planning

NASA Astrophysics Data System (ADS)

Morávek, Zdenek; Rickhey, Mark; Hartmann, Matthias; Bogner, Ludwig

2009-08-01

Treatment plans for intensity-modulated proton therapy may be sensitive to some sources of uncertainty. One source is correlated with approximations of the algorithms applied in the treatment planning system and another one depends on how robust the optimization is with regard to intra-fractional tissue movements. The irradiated dose distribution may substantially deteriorate from the planning when systematic errors occur in the dose algorithm. This can influence proton ranges and lead to improper modeling of the Braggpeak degradation in heterogeneous structures or particle scatter or the nuclear interaction part. Additionally, systematic errors influence the optimization process, which leads to the convergence error. Uncertainties with regard to organ movements are related to the robustness of a chosen beam setup to tissue movements on irradiation. We present the inverse Monte Carlo treatment planning system IKO for protons (IKO-P), which tries to minimize the errors described above to a large extent. Additionally, robust planning is introduced by beam angle optimization according to an objective function penalizing paths representing strongly longitudinal and transversal tissue heterogeneities. The same score function is applied to optimize spot planning by the selection of a robust choice of spots. As spots can be positioned on different energy grids or on geometric grids with different space filling factors, a variety of grids were used to investigate the influence on the spot-weight distribution as a result of optimization. A tighter distribution of spot weights was assumed to result in a more robust plan with respect to movements. IKO-P is described in detail and demonstrated on a test case and a lung cancer case as well. Different options of spot planning and grid types are evaluated, yielding a superior plan quality with dose delivery to the spots from all beam directions over optimized beam directions. This option shows a tighter spot-weight distribution

Robust control of the DC-DC boost converter based on the uncertainty and disturbance estimator

NASA Astrophysics Data System (ADS)

Oucheriah, Said

2017-11-01

In this paper, a robust non-linear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed and implemented for the output voltage regulation of the DC-DC boost converter. System uncertainties, external disturbances and unknown non-linear dynamics are lumped as a signal that is accurately estimated using a low-pass filter and their effects are cancelled by the controller. This methodology forms the basis of the UDE-based controller. A simple procedure is also developed that systematically determines the parameters of the controller to meet certain specifications. Using simulation, the effectiveness of the proposed controller is compared against the sliding-mode control (SMC). Experimental tests also show that the proposed controller is robust to system uncertainties, large input and load perturbations.

[Dealing with diagnostic uncertainty in general practice].

PubMed

Wübken, Magdalena; Oswald, Jana; Schneider, Antonius

2013-01-01

In general, the prevalence of diseases is low in primary care. Therefore, the positive predictive value of diagnostic tests is lower than in hospitals where patients are highly selected. In addition, the patients present with milder forms of disease; and many diseases might hide behind the initial symptom(s). These facts lead to diagnostic uncertainty which is somewhat inherent to general practice. This narrative review discusses different sources of and reasons for uncertainty and strategies to deal with it in the context of the current literature. Fear of uncertainty correlates with higher diagnostic activities. The attitude towards uncertainty correlates with the choice of medical speciality by vocational trainees or medical students. An intolerance of uncertainty, which still increases as medicine is making steady progress, might partly explain the growing shortage of general practitioners. The bio-psycho-social context appears to be important to diagnostic decision-making. The effect of intuition and heuristics are investigated by cognitive psychologists. It is still unclear whether these aspects are prone to bias or useful, which might depend on the context of medical decisions. Good communication is of great importance to share uncertainty with the patients in a transparent way and to alleviate shared decision-making. Dealing with uncertainty should be seen as an important core component of general practice and needs to be investigated in more detail to improve the respective medical decisions. Copyright © 2013. Published by Elsevier GmbH.

First Year Wilkinson Microwave Anisotropy Probe(WMAP) Observations: Data Processing Methods and Systematic Errors Limits

NASA Technical Reports Server (NTRS)

Hinshaw, G.; Barnes, C.; Bennett, C. L.; Greason, M. R.; Halpern, M.; Hill, R. S.; Jarosik, N.; Kogut, A.; Limon, M.; Meyer, S. S.

2003-01-01

We describe the calibration and data processing methods used to generate full-sky maps of the cosmic microwave background (CMB) from the first year of Wilkinson Microwave Anisotropy Probe (WMAP) observations. Detailed limits on residual systematic errors are assigned based largely on analyses of the flight data supplemented, where necessary, with results from ground tests. The data are calibrated in flight using the dipole modulation of the CMB due to the observatory's motion around the Sun. This constitutes a full-beam calibration source. An iterative algorithm simultaneously fits the time-ordered data to obtain calibration parameters and pixelized sky map temperatures. The noise properties are determined by analyzing the time-ordered data with this sky signal estimate subtracted. Based on this, we apply a pre-whitening filter to the time-ordered data to remove a low level of l/f noise. We infer and correct for a small (approx. 1 %) transmission imbalance between the two sky inputs to each differential radiometer, and we subtract a small sidelobe correction from the 23 GHz (K band) map prior to further analysis. No other systematic error corrections are applied to the data. Calibration and baseline artifacts, including the response to environmental perturbations, are negligible. Systematic uncertainties are comparable to statistical uncertainties in the characterization of the beam response. Both are accounted for in the covariance matrix of the window function and are propagated to uncertainties in the final power spectrum. We characterize the combined upper limits to residual systematic uncertainties through the pixel covariance matrix.

Beyond procedure's content: the role of accessibility experiences and personal uncertainty in procedural justice judgements.

PubMed

Greifeneder, Rainer; Müller, Patrick; Stahlberg, Dagmar; Van den Bos, Kees; Bless, Herbert

2011-01-01

Procedural justice concerns play a critical role in economic settings, politics, and other domains of human life. Despite the vast evidence corroborating their relevance, considerably less is known about how procedural justice judgments are formed. Whereas earlier theorizing focused on the systematic integration of content information, the present contribution provides a new perspective on the formation of justice judgments by examining the influence of accessibility experiences. Specifically, we hypothesize that procedural justice judgments may be formed based on the ease or difficulty with which justice-relevant information comes to mind. Three experiments corroborate this prediction in that procedures were evaluated less positively when the retrieval of associated unfair aspects was easy compared to difficult. Presumably this is because when it feels easy (difficult) to retrieve unfair aspects, these are perceived as frequent (infrequent), and hence the procedure as unjust (just). In addition to demonstrating that ease-of-retrieval may influence justice judgments, the studies further revealed that reliance on accessibility experiences is high in conditions of personal certainty. We suggest that this is because personal uncertainty fosters systematic processing of content information, whereas personal certainty may invite less taxing judgmental strategies such as reliance on ease-of-retrieval.

Addressing uncertainty in adaptation planning for agriculture.

PubMed

Vermeulen, Sonja J; Challinor, Andrew J; Thornton, Philip K; Campbell, Bruce M; Eriyagama, Nishadi; Vervoort, Joost M; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J; Hawkins, Ed; Smith, Daniel R

2013-05-21

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Addressing uncertainty in adaptation planning for agriculture

PubMed Central

Vermeulen, Sonja J.; Challinor, Andrew J.; Thornton, Philip K.; Campbell, Bruce M.; Eriyagama, Nishadi; Vervoort, Joost M.; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J.; Hawkins, Ed; Smith, Daniel R.

2013-01-01

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty. PMID:23674681

Uncertainty of rotating shadowband irradiometers and Si-pyranometers including the spectral irradiance error

NASA Astrophysics Data System (ADS)

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan; Geuder, Norbert; Habte, Aron; Schwandt, Marko; Vignola, Frank

2016-05-01

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible and color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2 % for global horizontal irradiance (GHI), and 2.9 % for DNI (for GHI and DNI over 300 W/m²) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.

Uncertainty of Rotating Shadowband Irradiometers and Si-Pyranometers Including the Spectral Irradiance Error

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

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan

2016-05-31

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible andmore » color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2% for global horizontal irradiance (GHI), and 2.9% for DNI (for GHI and DNI over 300 W/m2) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.« less

Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and Opportunities

ERIC Educational Resources Information Center

Demmans Epp, Carrie; Bull, Susan

2015-01-01

Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed decisions are made. This work has shown the difficulty that is inherent to representing uncertainty. Moreover, the representation of uncertainty has yet to be thoroughly explored in educational domains even…

Uncertainty as knowledge

PubMed Central

Lewandowsky, Stephan; Ballard, Timothy; Pancost, Richard D.

2015-01-01

This issue of Philosophical Transactions examines the relationship between scientific uncertainty about climate change and knowledge. Uncertainty is an inherent feature of the climate system. Considerable effort has therefore been devoted to understanding how to effectively respond to a changing, yet uncertain climate. Politicians and the public often appeal to uncertainty as an argument to delay mitigative action. We argue that the appropriate response to uncertainty is exactly the opposite: uncertainty provides an impetus to be concerned about climate change, because greater uncertainty increases the risks associated with climate change. We therefore suggest that uncertainty can be a source of actionable knowledge. We survey the papers in this issue, which address the relationship between uncertainty and knowledge from physical, economic and social perspectives. We also summarize the pervasive psychological effects of uncertainty, some of which may militate against a meaningful response to climate change, and we provide pointers to how those difficulties may be ameliorated. PMID:26460108

Analysis of uncertainties in turbine metal temperature predictions

NASA Technical Reports Server (NTRS)

Stepka, F. S.

1980-01-01

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Uncertainties in selected river water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2007-02-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise from natural or anthropogenic causes. Empirical quality of river water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected river water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2005). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerably to the overall uncertainty of river water quality data. Temporal autocorrelation of river water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments (500-3000 km2) reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For

Uncertainties in selected surface water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2006-09-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise form natural or anthropogenic causes. Empirical quality of surface water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected surface water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2006). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability's within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerable to the overall uncertainty of surface water quality data. Temporal autocorrelation of surface water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended

Markov logic network based complex event detection under uncertainty

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Jia, Bin; Chen, Genshe; Chen, Hua-mei; Sullivan, Nichole; Pham, Khanh; Blasch, Erik

2018-05-01

In a cognitive reasoning system, the four-stage Observe-Orient-Decision-Act (OODA) reasoning loop is of interest. The OODA loop is essential for the situational awareness especially in heterogeneous data fusion. Cognitive reasoning for making decisions can take advantage of different formats of information such as symbolic observations, various real-world sensor readings, or the relationship between intelligent modalities. Markov Logic Network (MLN) provides mathematically sound technique in presenting and fusing data at multiple levels of abstraction, and across multiple intelligent sensors to conduct complex decision-making tasks. In this paper, a scenario about vehicle interaction is investigated, in which uncertainty is taken into consideration as no systematic approaches can perfectly characterize the complex event scenario. MLNs are applied to the terrestrial domain where the dynamic features and relationships among vehicles are captured through multiple sensors and information sources regarding the data uncertainty.

Identifying additional studies for a systematic review of retention strategies in randomised controlled trials: making contact with trials units and trial methodologists.

PubMed

Brueton, Valerie; Tierney, Jayne F; Stenning, Sally; Rait, Greta

2017-08-22

Search strategies for systematic reviews aim to identify all evidence relevant to the research question posed. Reports of methodological research can be difficult to find leading to biased results in systematic reviews of research methodology. Evidence suggests that contact with investigators can help to identify unpublished research. To identify additional eligible randomised controlled trials (RCTs) for a Cochrane systematic review of strategies to improve retention in RCTs, we conducted a survey of UK clinical trials units (CTUs) and made contact with RCT methodologists. Key contacts for all UK CTUs were sent a personalised email with a short questionnaire and summary protocol of the Cochrane methodology review. The questionnaire asked whether a RCT evaluating strategies to improve retention embedded in a RCT had ever been conducted by the CTU. Questions about the stage of completion and publication of such RCTs were included. The summary protocol outlined the aims, eligibility criteria, examples of types of retention strategies, and the primary outcome for the systematic review. Personal communication with RCT methodologists and presentations of preliminary results of the review at conferences were also used to identify additional eligible RCTs. We checked the results of our standard searches to see if eligible studies identified through these additional methods were also found using our standard searches. We identified 14 of the 38 RCTs included in the Cochrane methodology review by contacting trials units and methodologists. Eleven of the 14 RCTs identified by these methods were either published in grey literature, in press or unpublished. Three remaining RCTs were fully published at the time. Six of the RCTs identified were not found through any other searches. The RCTs identified represented data for 6 of 14 RCTs of incentive strategies (52% of randomised participants included in the review), and 6 of 14 RCTs of communication strategies (52% of randomised

Efficient Location Uncertainty Treatment for Probabilistic Modelling of Portfolio Loss from Earthquake Events

NASA Astrophysics Data System (ADS)

Scheingraber, Christoph; Käser, Martin; Allmann, Alexander

2017-04-01

Probabilistic seismic risk analysis (PSRA) is a well-established method for modelling loss from earthquake events. In the insurance industry, it is widely employed for probabilistic modelling of loss to a distributed portfolio. In this context, precise exposure locations are often unknown, which results in considerable loss uncertainty. The treatment of exposure uncertainty has already been identified as an area where PSRA would benefit from increased research attention. However, so far, epistemic location uncertainty has not been in the focus of a large amount of research. We propose a new framework for efficient treatment of location uncertainty. To demonstrate the usefulness of this novel method, a large number of synthetic portfolios resembling real-world portfolios is systematically analyzed. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on loss variability. Several sampling criteria to increase the computational efficiency of the framework are proposed and put into the wider context of well-established Monte-Carlo variance reduction techniques. The performance of each of the proposed criteria is analyzed.

Quantification of uncertainties in global grazing systems assessment

NASA Astrophysics Data System (ADS)

Fetzel, T.; Havlik, P.; Herrero, M.; Kaplan, J. O.; Kastner, T.; Kroisleitner, C.; Rolinski, S.; Searchinger, T.; Van Bodegom, P. M.; Wirsenius, S.; Erb, K.-H.

2017-07-01

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing data sets on (a) grazing feed intake, (b) the spatial distribution of livestock, (c) the extent of grazing land, and (d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of the world's grazing lands, but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder, it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe, massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input data for NPP, animal distribution, and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level data sets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security.

On a Lagrange-Hamilton formalism describing position and momentum uncertainties

NASA Technical Reports Server (NTRS)

Schuch, Dieter

1993-01-01

According to Heisenberg's uncertainty relation, in quantum mechanics it is not possible to determine, simultaneously, exact values for the position and the momentum of a material system. Calculating the mean value of the Hamiltonian operator with the aid of exact analytic Gaussian wave packet solutions, these uncertainties cause an energy contribution additional to the classical energy of the system. For the harmonic oscillator, e.g., this nonclassical energy represents the ground state energy. It will be shown that this additional energy contribution can be considered as a Hamiltonian function, if it is written in appropriate variables. With the help of the usual Lagrange-Hamilton formalism known from classical particle mechanics, but now considering this new Hamiltonian function, it is possible to obtain the equations of motion for position and momentum uncertainties.

Unveiling the decoherence effect of noise on the entropic uncertainty relation and its control by partially collapsed operations

NASA Astrophysics Data System (ADS)

Chen, Min-Nan; Sun, Wen-Yang; Huang, Ai-Jun; Ming, Fei; Wang, Dong; Ye, Liu

2018-01-01

In this work, we investigate the dynamics of quantum-memory-assisted entropic uncertainty relations under open systems, and how to steer the uncertainty under different types of decoherence. Specifically, we develop the dynamical behaviors of the uncertainty of interest under two typical categories of noise; bit flipping and depolarizing channels. It has been shown that the measurement uncertainty firstly increases and then decreases with the growth of the decoherence strength in bit flipping channels. In contrast, the uncertainty monotonically increases with the increase of the decoherence strength in depolarizing channels. Notably, and to a large degree, it is shown that the uncertainty depends on both the systematic quantum correlation and the minimal conditional entropy of the observed subsystem. Moreover, we present a possible physical interpretation for these distinctive behaviors of the uncertainty within such scenarios. Furthermore, we propose a simple and effective strategy to reduce the entropic uncertainty by means of a partially collapsed operation—quantum weak measurement. Therefore, our investigations might offer an insight into the dynamics of the measurment uncertainty under decoherence, and be of importance to quantum precision measurement in open systems.

Comparison between bottom-up and top-down approaches in the estimation of measurement uncertainty.

PubMed

Lee, Jun Hyung; Choi, Jee-Hye; Youn, Jae Saeng; Cha, Young Joo; Song, Woonheung; Park, Ae Ja

2015-06-01

Measurement uncertainty is a metrological concept to quantify the variability of measurement results. There are two approaches to estimate measurement uncertainty. In this study, we sought to provide practical and detailed examples of the two approaches and compare the bottom-up and top-down approaches to estimating measurement uncertainty. We estimated measurement uncertainty of the concentration of glucose according to CLSI EP29-A guideline. Two different approaches were used. First, we performed a bottom-up approach. We identified the sources of uncertainty and made an uncertainty budget and assessed the measurement functions. We determined the uncertainties of each element and combined them. Second, we performed a top-down approach using internal quality control (IQC) data for 6 months. Then, we estimated and corrected systematic bias using certified reference material of glucose (NIST SRM 965b). The expanded uncertainties at the low glucose concentration (5.57 mmol/L) by the bottom-up approach and top-down approaches were ±0.18 mmol/L and ±0.17 mmol/L, respectively (all k=2). Those at the high glucose concentration (12.77 mmol/L) by the bottom-up and top-down approaches were ±0.34 mmol/L and ±0.36 mmol/L, respectively (all k=2). We presented practical and detailed examples for estimating measurement uncertainty by the two approaches. The uncertainties by the bottom-up approach were quite similar to those by the top-down approach. Thus, we demonstrated that the two approaches were approximately equivalent and interchangeable and concluded that clinical laboratories could determine measurement uncertainty by the simpler top-down approach.

Error Detection and Recovery for Robot Motion Planning with Uncertainty.

DTIC Science & Technology

1987-07-01

plans for these problems . This intuition-which is a heuristic claim, so the reader is advised to proceed with caution--should be verified or disproven...that might work. but fail in a --reasonable" way when they cannot. While EDR is largely motivated by the problems of uncertainty and model error. its...definition for EDR strategies and show how they can be computed. This theory represents what is perhaps the first systematic attack on the problem of

Impulsivity modulates performance under response uncertainty in a reaching task.

PubMed

Tzagarakis, C; Pellizzer, G; Rogers, R D

2013-03-01

We sought to explore the interaction of the impulsivity trait with response uncertainty. To this end, we used a reaching task (Pellizzer and Hedges in Exp Brain Res 150:276-289, 2003) where a motor response direction was cued at different levels of uncertainty (1 cue, i.e., no uncertainty, 2 cues or 3 cues). Data from 95 healthy adults (54 F, 41 M) were analysed. Impulsivity was measured using the Barratt Impulsiveness Scale version 11 (BIS-11). Behavioral variables recorded were reaction time (RT), errors of commission (referred to as 'early errors') and errors of precision. Data analysis employed generalised linear mixed models and generalised additive mixed models. For the early errors, there was an interaction of impulsivity with uncertainty and gender, with increased errors for high impulsivity in the one-cue condition for women and the three-cue condition for men. There was no effect of impulsivity on precision errors or RT. However, the analysis of the effect of RT and impulsivity on precision errors showed a different pattern for high versus low impulsives in the high uncertainty (3 cue) condition. In addition, there was a significant early error speed-accuracy trade-off for women, primarily in low uncertainty and a 'reverse' speed-accuracy trade-off for men in high uncertainty. These results extend those of past studies of impulsivity which help define it as a behavioural trait that modulates speed versus accuracy response styles depending on environmental constraints and highlight once more the importance of gender in the interplay of personality and behaviour.

Orbital State Uncertainty Realism

NASA Astrophysics Data System (ADS)

Horwood, J.; Poore, A. B.

2012-09-01

Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten

Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Wang, Weile; Michaelis, Andrew; Votava, Petr; Nemani, Ramakrishma

2010-01-01

In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements

NASA Astrophysics Data System (ADS)

Määttä, A.; Laine, M.; Tamminen, J.; Veefkind, J. P.

2014-05-01

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application.

Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty.

PubMed

Eick, Geeta N; Bridgham, Jamie T; Anderson, Douglas P; Harms, Michael J; Thornton, Joseph W

2017-02-01

Hypotheses about the functions of ancient proteins and the effects of historical mutations on them are often tested using ancestral protein reconstruction (APR)-phylogenetic inference of ancestral sequences followed by synthesis and experimental characterization. Usually, some sequence sites are ambiguously reconstructed, with two or more statistically plausible states. The extent to which the inferred functions and mutational effects are robust to uncertainty about the ancestral sequence has not been studied systematically. To address this issue, we reconstructed ancestral proteins in three domain families that have different functions, architectures, and degrees of uncertainty; we then experimentally characterized the functional robustness of these proteins when uncertainty was incorporated using several approaches, including sampling amino acid states from the posterior distribution at each site and incorporating the alternative amino acid state at every ambiguous site in the sequence into a single "worst plausible case" protein. In every case, qualitative conclusions about the ancestral proteins' functions and the effects of key historical mutations were robust to sequence uncertainty, with similar functions observed even when scores of alternate amino acids were incorporated. There was some variation in quantitative descriptors of function among plausible sequences, suggesting that experimentally characterizing robustness is particularly important when quantitative estimates of ancient biochemical parameters are desired. The worst plausible case method appears to provide an efficient strategy for characterizing the functional robustness of ancestral proteins to large amounts of sequence uncertainty. Sampling from the posterior distribution sometimes produced artifactually nonfunctional proteins for sequences reconstructed with substantial ambiguity. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and

Analogy as a strategy for supporting complex problem solving under uncertainty.

PubMed

Chan, Joel; Paletz, Susannah B F; Schunn, Christian D

2012-11-01

Complex problem solving in naturalistic environments is fraught with uncertainty, which has significant impacts on problem-solving behavior. Thus, theories of human problem solving should include accounts of the cognitive strategies people bring to bear to deal with uncertainty during problem solving. In this article, we present evidence that analogy is one such strategy. Using statistical analyses of the temporal dynamics between analogy and expressed uncertainty in the naturalistic problem-solving conversations among scientists on the Mars Rover Mission, we show that spikes in expressed uncertainty reliably predict analogy use (Study 1) and that expressed uncertainty reduces to baseline levels following analogy use (Study 2). In addition, in Study 3, we show with qualitative analyses that this relationship between uncertainty and analogy is not due to miscommunication-related uncertainty but, rather, is primarily concentrated on substantive problem-solving issues. Finally, we discuss a hypothesis about how analogy might serve as an uncertainty reduction strategy in naturalistic complex problem solving.

Governing Laws of Complex System Predictability under Co-evolving Uncertainty Sources: Theory and Nonlinear Geophysical Applications

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.

2017-12-01

Predictability assessments are traditionally made on a case-by-case basis, often by running the particular model of interest with randomly perturbed initial/boundary conditions and parameters, producing computationally expensive ensembles. These approaches provide a lumped statistical view of uncertainty evolution, without eliciting the fundamental processes and interactions at play in the uncertainty dynamics. In order to address these limitations, we introduce a systematic dynamical framework for predictability assessment and forecast, by analytically deriving governing equations of predictability in terms of the fundamental architecture of dynamical systems, independent of any particular problem under consideration. The framework further relates multiple uncertainty sources along with their coevolutionary interplay, enabling a comprehensive and explicit treatment of uncertainty dynamics along time, without requiring the actual model to be run. In doing so, computational resources are freed and a quick and effective a-priori systematic dynamic evaluation is made of predictability evolution and its challenges, including aspects in the model architecture and intervening variables that may require optimization ahead of initiating any model runs. It further brings out universal dynamic features in the error dynamics elusive to any case specific treatment, ultimately shedding fundamental light on the challenging issue of predictability. The formulated approach, framed with broad mathematical physics generality in mind, is then implemented in dynamic models of nonlinear geophysical systems with various degrees of complexity, in order to evaluate their limitations and provide informed assistance on how to optimize their design and improve their predictability in fundamental dynamical terms.

Additional considerations are required when preparing a protocol for a systematic review with multiple interventions.

PubMed

Chaimani, Anna; Caldwell, Deborah M; Li, Tianjing; Higgins, Julian P T; Salanti, Georgia

2017-03-01

The number of systematic reviews that aim to compare multiple interventions using network meta-analysis is increasing. In this study, we highlight aspects of a standard systematic review protocol that may need modification when multiple interventions are to be compared. We take the protocol format suggested by Cochrane for a standard systematic review as our reference and compare the considerations for a pairwise review with those required for a valid comparison of multiple interventions. We suggest new sections for protocols of systematic reviews including network meta-analyses with a focus on how to evaluate their assumptions. We provide example text from published protocols to exemplify the considerations. Standard systematic review protocols for pairwise meta-analyses need extensions to accommodate the increased complexity of network meta-analysis. Our suggested modifications are widely applicable to both Cochrane and non-Cochrane systematic reviews involving network meta-analyses. Copyright © 2017 Elsevier Inc. All rights reserved.

Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Bretl, Katherine N.; Walker, Eric L.; Pinier, Jeremy T.

2015-01-01

The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69o DeltaWing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.

Evaluation of measurement uncertainty of glucose in clinical chemistry.

PubMed

Berçik Inal, B; Koldas, M; Inal, H; Coskun, C; Gümüs, A; Döventas, Y

2007-04-01

The definition of the uncertainty of measurement used in the International Vocabulary of Basic and General Terms in Metrology (VIM) is a parameter associated with the result of a measurement, which characterizes the dispersion of the values that could reasonably be attributed to the measurand. Uncertainty of measurement comprises many components. In addition to every parameter, the measurement uncertainty is that a value should be given by all institutions that have been accredited. This value shows reliability of the measurement. GUM, published by NIST, contains uncertainty directions. Eurachem/CITAC Guide CG4 was also published by Eurachem/CITAC Working Group in the year 2000. Both of them offer a mathematical model, for uncertainty can be calculated. There are two types of uncertainty in measurement. Type A is the evaluation of uncertainty through the statistical analysis and type B is the evaluation of uncertainty through other means, for example, certificate reference material. Eurachem Guide uses four types of distribution functions: (1) rectangular distribution that gives limits without specifying a level of confidence (u(x)=a/ radical3) to a certificate; (2) triangular distribution that values near to the same point (u(x)=a/ radical6); (3) normal distribution in which an uncertainty is given in the form of a standard deviation s, a relative standard deviation s/ radicaln, or a coefficient of variance CV% without specifying the distribution (a = certificate value, u = standard uncertainty); and (4) confidence interval.

On the Directional Dependence and Null Space Freedom in Uncertainty Bound Identification

NASA Technical Reports Server (NTRS)

Lim, K. B.; Giesy, D. P.

1997-01-01

In previous work, the determination of uncertainty models via minimum norm model validation is based on a single set of input and output measurement data. Since uncertainty bounds at each frequency is directionally dependent for multivariable systems, this will lead to optimistic uncertainty levels. In addition, the design freedom in the uncertainty model has not been utilized to further reduce uncertainty levels. The above issues are addressed by formulating a min- max problem. An analytical solution to the min-max problem is given to within a generalized eigenvalue problem, thus avoiding a direct numerical approach. This result will lead to less conservative and more realistic uncertainty models for use in robust control.

SU-E-T-573: The Robustness of a Combined Margin Recipe for Uncertainties During Radiotherapy

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

Stroom, J; Vieira, S; Greco, C

2014-06-01

Purpose: To investigate the variability of a safety margin recipe that combines CTV and PTV margins quadratically, with several tumor, treatment, and user related factors. Methods: Margin recipes were calculated by monte-carlo simulations in 5 steps. 1. A spherical tumor with or without isotropic microscopic was irradiated with a 5 field dose plan2. PTV: Geometric uncertainties were introduced using systematic (Sgeo) and random (sgeo) standard deviations. CTV: Microscopic disease distribution was modelled by semi-gaussian (Smicro) with varying number of islets (Ni)3. For a specific uncertainty set (Sgeo, sgeo, Smicro(Ni)), margins were varied until pre-defined decrease in TCP or dose coveragemore » was fulfilled. 4. First, margin recipes were calculated for each of the three uncertainties separately. CTV and PTV recipes were then combined quadratically to yield a final recipe M(Sgeo, sgeo, Smicro(Ni)).5. The final M was verified by simultaneous simulations of the uncertainties.Now, M has been calculated for various changing parameters like margin criteria, penumbra steepness, islet radio-sensitivity, dose conformity, and number of fractions. We subsequently investigated A: whether the combined recipe still holds in all these situations, and B: what the margin variation was in all these cases. Results: We found that the accuracy of the combined margin recipes remains on average within 1mm for all situations, confirming the correctness of the quadratic addition. Depending on the specific parameter, margin factors could change such that margins change over 50%. Especially margin recipes based on TCP-criteria are more sensitive to more parameters than those based on purely geometric Dmin-criteria. Interestingly, measures taken to minimize treatment field sizes (by e.g. optimizing dose conformity) are counteracted by the requirement of larger margins to get the same tumor coverage. Conclusion: Margin recipes combining geometric and microscopic uncertainties

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Developing a spectroradiometer data uncertainty methodology

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

Peterson, Josh; Vignola, Frank; Habte, Aron

The proper calibration and measurement uncertainty of spectral data obtained from spectroradiometers is essential in accurately quantifying the output of photovoltaic (PV) devices. PV cells and modules are initially characterized using solar simulators but field performance is evaluated using natural sunlight. Spectroradiometers are used to measure the spectrum of both these light sources in an effort to understand the spectral dependence of various PV output capabilities. These chains of characterization and measurement are traceable to National Metrology Institutes such as National Institute of Standards and Technology, and therefore there is a need for a comprehensive uncertainty methodology to determine themore » accuracy of spectroradiometer data. In this paper, the uncertainties associated with the responsivity of a spectroradiometer are examined using the Guide to the Expression of Uncertainty in Measurement (GUM) protocols. This is first done for a generic spectroradiometer, and then, to illustrate the methodology, the calibration of a LI-COR 1800 spectroradiometer is performed. The reader should be aware that the implementation of this methodology will be specific to the spectroradiometer being analyzed and the experimental setup that is used. Depending of the characteristics of the spectroradiometer being evaluated additional sources of uncertainty may need to be included, but the general GUM methodology is the same. Several sources of uncertainty are associated with the spectroradiometer responsivity. Major sources of uncertainty associated with the LI-COR spectroradiometer are noise in the signal at wavelengths less than 400 nm. At wavelengths more than 400 nm, the responsivity can vary drastically, and it is dependent on the wavelength of light, the temperature dependence, the angle of incidence, and the azimuthal orientation of the sensor to the light source. As a result, the expanded uncertainties in the responsivity of the LI-COR spectroradiometer

Developing a spectroradiometer data uncertainty methodology

DOE PAGES

Peterson, Josh; Vignola, Frank; Habte, Aron; ...

2017-04-11

The proper calibration and measurement uncertainty of spectral data obtained from spectroradiometers is essential in accurately quantifying the output of photovoltaic (PV) devices. PV cells and modules are initially characterized using solar simulators but field performance is evaluated using natural sunlight. Spectroradiometers are used to measure the spectrum of both these light sources in an effort to understand the spectral dependence of various PV output capabilities. These chains of characterization and measurement are traceable to National Metrology Institutes such as National Institute of Standards and Technology, and therefore there is a need for a comprehensive uncertainty methodology to determine themore » accuracy of spectroradiometer data. In this paper, the uncertainties associated with the responsivity of a spectroradiometer are examined using the Guide to the Expression of Uncertainty in Measurement (GUM) protocols. This is first done for a generic spectroradiometer, and then, to illustrate the methodology, the calibration of a LI-COR 1800 spectroradiometer is performed. The reader should be aware that the implementation of this methodology will be specific to the spectroradiometer being analyzed and the experimental setup that is used. Depending of the characteristics of the spectroradiometer being evaluated additional sources of uncertainty may need to be included, but the general GUM methodology is the same. Several sources of uncertainty are associated with the spectroradiometer responsivity. Major sources of uncertainty associated with the LI-COR spectroradiometer are noise in the signal at wavelengths less than 400 nm. At wavelengths more than 400 nm, the responsivity can vary drastically, and it is dependent on the wavelength of light, the temperature dependence, the angle of incidence, and the azimuthal orientation of the sensor to the light source. As a result, the expanded uncertainties in the responsivity of the LI-COR spectroradiometer

ICYESS 2013: Understanding and Interpreting Uncertainty

NASA Astrophysics Data System (ADS)

Rauser, F.; Niederdrenk, L.; Schemann, V.; Schmidt, A.; Suesser, D.; Sonntag, S.

2013-12-01

We will report the outcomes and highlights of the Interdisciplinary Conference of Young Earth System Scientists (ICYESS) on Understanding and Interpreting Uncertainty in September 2013, Hamburg, Germany. This conference is aimed at early career scientists (Masters to Postdocs) from a large variety of scientific disciplines and backgrounds (natural, social and political sciences) and will enable 3 days of discussions on a variety of uncertainty-related aspects: 1) How do we deal with implicit and explicit uncertainty in our daily scientific work? What is uncertain for us, and for which reasons? 2) How can we communicate these uncertainties to other disciplines? E.g., is uncertainty in cloud parameterization and respectively equilibrium climate sensitivity a concept that is understood equally well in natural and social sciences that deal with Earth System questions? Or vice versa, is, e.g., normative uncertainty as in choosing a discount rate relevant for natural scientists? How can those uncertainties be reconciled? 3) How can science communicate this uncertainty to the public? Is it useful at all? How are the different possible measures of uncertainty understood in different realms of public discourse? Basically, we want to learn from all disciplines that work together in the broad Earth System Science community how to understand and interpret uncertainty - and then transfer this understanding to the problem of how to communicate with the public, or its different layers / agents. ICYESS is structured in a way that participation is only possible via presentation, so every participant will give their own professional input into how the respective disciplines deal with uncertainty. Additionally, a large focus is put onto communication techniques; there are no 'standard presentations' in ICYESS. Keynote lectures by renowned scientists and discussions will lead to a deeper interdisciplinary understanding of what we do not really know, and how to deal with it. Many

Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation

NASA Astrophysics Data System (ADS)

Leisenring, Marc; Moradkhani, Hamid

2012-10-01

SummaryA first step in understanding the impacts of sediment and controlling the sources of sediment is to quantify the mass loading. Since mass loading is the product of flow and concentration, the quantification of loads first requires the quantification of runoff volume. Using the National Weather Service's SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study employed particle filter based Bayesian data assimilation methods to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California, USA. A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread. In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter. Both of these methods significantly improved the performance of the particle filter without requiring excessively wide prediction bounds. The flow ensemble was linked to a non-linear regression model that was used to predict suspended sediment concentrations (SSCs) based on runoff rate and time of year. Runoff volumes and SSC were then combined to produce an ensemble of suspended sediment load estimates. Annual suspended sediment loads for the 5 years of simulation were finally computed along with 95% prediction intervals that account for uncertainty in both the SSC regression model and flow rate estimates. Understanding the uncertainty associated with annual suspended sediment load predictions is critical for making sound watershed management decisions aimed at maintaining the exceptional clarity of Lake Tahoe. The computational methods developed and applied in this research could assist with similar studies where it is important to quantify the predictive uncertainty of pollutant load

Dynamics Under Location Uncertainty: Model Derivation, Modified Transport and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.

2017-12-01

In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last

SDRE controller for motion design of cable-suspended robot with uncertainties and moving obstacles

NASA Astrophysics Data System (ADS)

Behboodi, Ahad; Salehi, Seyedmohammad

2017-10-01

In this paper an optimal control approach for nonlinear dynamical systems was proposed based on State Dependent Riccati Equation (SDRE) and its robustness against uncertainties is shown by simulation results. The proposed method was applied on a spatial six-cable suspended robot, which was designed to carry loads or perform different tasks in huge workspaces. Motion planning for cable-suspended robots in such a big workspace is subjected to uncertainties and obstacles. First, we emphasized the ability of SDRE to construct a systematic basis and efficient design of controller for wide variety of nonlinear dynamical systems. Then we showed how this systematic design improved the robustness of the system and facilitated the integration of motion planning techniques with the controller. In particular, obstacle avoidance technique based on artificial potential field (APF) can be easily combined with SDRE controller with efficient performance. Due to difficulties of exact solution for SDRE, an approximation method was used based on power series expansion. The efficiency and robustness of the SDRE controller was illustrated on a six-cable suspended robot with proper simulations.

Tightness Entropic Uncertainty Relation in Quantum Markovian-Davies Environment

NASA Astrophysics Data System (ADS)

Zhang, Jun; Liu, Liang; Han, Yan

2018-05-01

In this paper, we investigate the tightness of entropic uncertainty relation in the absence (presence) of the quantum memory which the memory particle being weakly coupled to a decohering Davies-type Markovian environment. The results show that the tightness of the quantum uncertainty relation can be controlled by the energy relaxation time F, the dephasing time G and the rescaled temperature p, the perfect tightness can be arrived by dephasing and energy relaxation satisfying F = 2G and p = 1/2. In addition, the tightness of the memory-assisted entropic uncertainty relation and the entropic uncertainty relation can be influenced mainly by the purity. While in memory-assisted model, the purity and quantum correlation can also influence the tightness actively while the quantum entanglement can influence the tightness slightly.

Onward through the Fog: Uncertainty and Management Adaptation in Systems Analysis and Design

DTIC Science & Technology

1990-07-01

has fallen into stereotyped problem formulations and analytical ap- proaches. In particular, treatments of uncertainty are typically quite incomplete...and often conceptually wrong. This report argues that these shortcomings produce pervasive systematic biases in analyses. Problem formulations ...capability were lost. The expected number of aircraft that would not be fully mission capable thirty days later was roughly twice the num - ber

Understanding the origin of Paris Agreement emission uncertainties

PubMed Central

Rogelj, Joeri; Fricko, Oliver; Meinshausen, Malte; Krey, Volker; Zilliacus, Johanna J. J.; Riahi, Keywan

2017-01-01

The UN Paris Agreement puts in place a legally binding mechanism to increase mitigation action over time. Countries put forward pledges called nationally determined contributions (NDC) whose impact is assessed in global stocktaking exercises. Subsequently, actions can then be strengthened in light of the Paris climate objective: limiting global mean temperature increase to well below 2 °C and pursuing efforts to limit it further to 1.5 °C. However, pledged actions are currently described ambiguously and this complicates the global stocktaking exercise. Here, we systematically explore possible interpretations of NDC assumptions, and show that this results in estimated emissions for 2030 ranging from 47 to 63 GtCO2e yr−1. We show that this uncertainty has critical implications for the feasibility and cost to limit warming well below 2 °C and further to 1.5 °C. Countries are currently working towards clarifying the modalities of future NDCs. We identify salient avenues to reduce the overall uncertainty by about 10 percentage points through simple, technical clarifications regarding energy accounting rules. Remaining uncertainties depend to a large extent on politically valid choices about how NDCs are expressed, and therefore raise the importance of a thorough and robust process that keeps track of where emissions are heading over time. PMID:28585924

Understanding the origin of Paris Agreement emission uncertainties.

PubMed

Rogelj, Joeri; Fricko, Oliver; Meinshausen, Malte; Krey, Volker; Zilliacus, Johanna J J; Riahi, Keywan

2017-06-06

The UN Paris Agreement puts in place a legally binding mechanism to increase mitigation action over time. Countries put forward pledges called nationally determined contributions (NDC) whose impact is assessed in global stocktaking exercises. Subsequently, actions can then be strengthened in light of the Paris climate objective: limiting global mean temperature increase to well below 2 °C and pursuing efforts to limit it further to 1.5 °C. However, pledged actions are currently described ambiguously and this complicates the global stocktaking exercise. Here, we systematically explore possible interpretations of NDC assumptions, and show that this results in estimated emissions for 2030 ranging from 47 to 63 GtCO 2 e yr -1 . We show that this uncertainty has critical implications for the feasibility and cost to limit warming well below 2 °C and further to 1.5 °C. Countries are currently working towards clarifying the modalities of future NDCs. We identify salient avenues to reduce the overall uncertainty by about 10 percentage points through simple, technical clarifications regarding energy accounting rules. Remaining uncertainties depend to a large extent on politically valid choices about how NDCs are expressed, and therefore raise the importance of a thorough and robust process that keeps track of where emissions are heading over time.

The Harm that Underestimation of Uncertainty Does to Our Community: A Case Study Using Sunspot Area Measurements

NASA Astrophysics Data System (ADS)

Munoz-Jaramillo, Andres

2017-08-01

Data products in heliospheric physics are very often provided without clear estimates of uncertainty. From helioseismology in the solar interior, all the way to in situ solar wind measurements beyond 1AU, uncertainty estimates are typically hard for users to find (buried inside long documents that are separate from the data products), or simply non-existent.There are two main reasons why uncertainty measurements are hard to find:1. Understanding instrumental systematic errors is given a much higher priority inside instrumental teams.2. The desire to perfectly understand all sources of uncertainty postpones indefinitely the actual quantification of uncertainty in our measurements.Using the cross calibration of 200 years of sunspot area measurements as a case study, in this presentation we will discuss the negative impact that inadequate measurements of uncertainty have on users, through the appearance of toxic and unnecessary controversies, and data providers, through the creation of unrealistic expectations regarding the information that can be extracted from their data. We will discuss how empirical estimates of uncertainty represent a very good alternative to not providing any estimates at all, and finalize by discussing the bare essentials that should become our standard practice for future instruments and surveys.

A Systematic Review and Methodological Evaluation of Published Cost-Effectiveness Analyses of Aromatase Inhibitors versus Tamoxifen in Early Stage Breast Cancer

PubMed Central

John-Baptiste, Ava A.; Wu, Wei; Rochon, Paula; Anderson, Geoffrey M.; Bell, Chaim M.

2013-01-01

Background A key priority in developing policies for providing affordable cancer care is measuring the value for money of new therapies using cost-effectiveness analyses (CEAs). For CEA to be useful it should focus on relevant outcomes and include thorough investigation of uncertainty. Randomized controlled trials (RCTs) of five years of aromatase inhibitors (AI) versus five years of tamoxifen in the treatment of post-menopausal women with early stage breast cancer, show benefit of AI in terms of disease free survival (DFS) but not overall survival (OS) and indicate higher risk of fracture with AI. Policy-relevant CEA of AI versus tamoxifen should focus on OS and include analysis of uncertainty over key assumptions. Methods We conducted a systematic review of published CEAs comparing an AI to tamoxifen. We searched Ovid MEDLINE, EMBASE, PsychINFO, and the Cochrane Database of Systematic Reviews without language restrictions. We selected CEAs with outcomes expressed as cost per life year or cost per quality adjusted life year (QALY). We assessed quality using the Neumann checklist. Using structured forms two abstractors collected descriptive information, sources of data, baseline assumptions on effectiveness and adverse events, and recorded approaches to assessing parameter uncertainty, methodological uncertainty, and structural uncertainty. Results We identified 1,622 citations and 18 studies met inclusion criteria. All CE estimates assumed a survival benefit for aromatase inhibitors. Twelve studies performed sensitivity analysis on the risk of adverse events and 7 assumed no additional mortality risk with any adverse event. Sub-group analysis was limited; 6 studies examined older women, 2 examined women with low recurrence risk, and 1 examined women with multiple comorbidities. Conclusion Published CEAs comparing AIs to tamoxifen assumed an OS benefit though none has been shown in RCTs, leading to an overestimate of the cost-effectiveness of AIs. Results of these

A systematic review and methodological evaluation of published cost-effectiveness analyses of aromatase inhibitors versus tamoxifen in early stage breast cancer.

PubMed

John-Baptiste, Ava A; Wu, Wei; Rochon, Paula; Anderson, Geoffrey M; Bell, Chaim M

2013-01-01

A key priority in developing policies for providing affordable cancer care is measuring the value for money of new therapies using cost-effectiveness analyses (CEAs). For CEA to be useful it should focus on relevant outcomes and include thorough investigation of uncertainty. Randomized controlled trials (RCTs) of five years of aromatase inhibitors (AI) versus five years of tamoxifen in the treatment of post-menopausal women with early stage breast cancer, show benefit of AI in terms of disease free survival (DFS) but not overall survival (OS) and indicate higher risk of fracture with AI. Policy-relevant CEA of AI versus tamoxifen should focus on OS and include analysis of uncertainty over key assumptions. We conducted a systematic review of published CEAs comparing an AI to tamoxifen. We searched Ovid MEDLINE, EMBASE, PsychINFO, and the Cochrane Database of Systematic Reviews without language restrictions. We selected CEAs with outcomes expressed as cost per life year or cost per quality adjusted life year (QALY). We assessed quality using the Neumann checklist. Using structured forms two abstractors collected descriptive information, sources of data, baseline assumptions on effectiveness and adverse events, and recorded approaches to assessing parameter uncertainty, methodological uncertainty, and structural uncertainty. We identified 1,622 citations and 18 studies met inclusion criteria. All CE estimates assumed a survival benefit for aromatase inhibitors. Twelve studies performed sensitivity analysis on the risk of adverse events and 7 assumed no additional mortality risk with any adverse event. Sub-group analysis was limited; 6 studies examined older women, 2 examined women with low recurrence risk, and 1 examined women with multiple comorbidities. Published CEAs comparing AIs to tamoxifen assumed an OS benefit though none has been shown in RCTs, leading to an overestimate of the cost-effectiveness of AIs. Results of these CEA analyses may be suboptimal for

Operationalising uncertainty in data and models for integrated water resources management.

PubMed

Blind, M W; Refsgaard, J C

2007-01-01

Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.

Interstellar Flow and Temperature Determination with IBEX: Robustness and Sensitivity to Systematic Effects

NASA Astrophysics Data System (ADS)

Möbius, E.; Bzowski, M.; Frisch, P. C.; Fuselier, S. A.; Heirtzler, D.; Kubiak, M. A.; Kucharek, H.; Lee, M. A.; Leonard, T.; McComas, D. J.; Schwadron, N. A.; Sokół, J. M.; Swaczyna, P.; Wurz, P.

2015-10-01

The Interstellar Boundary Explorer (IBEX) samples the interstellar neutral (ISN) gas flow of several species every year from December through late March when the Earth moves into the incoming flow. The first quantitative analyses of these data resulted in a narrow tube in four-dimensional interstellar parameter space, which couples speed, flow latitude, flow longitude, and temperature, and center values with approximately 3° larger longitude and 3 km s-1 lower speed, but with temperatures similar to those obtained from observations by the Ulysses spacecraft. IBEX has now recorded six years of ISN flow observations, providing a large database over increasing solar activity and using varying viewing strategies. In this paper, we evaluate systematic effects that are important for the ISN flow vector and temperature determination. We find that all models in use return ISN parameters well within the observational uncertainties and that the derived ISN flow direction is resilient against uncertainties in the ionization rate. We establish observationally an effective IBEX-Lo pointing uncertainty of ±0.°18 in spin angle and confirm an uncertainty of ±0.°1 in longitude. We also show that the IBEX viewing strategy with different spin-axis orientations minimizes the impact of several systematic uncertainties, and thus improves the robustness of the measurement. The Helium Warm Breeze has likely contributed substantially to the somewhat different center values of the ISN flow vector. By separating the flow vector and temperature determination, we can mitigate these effects on the analysis, which returns an ISN flow vector very close to the Ulysses results, but with a substantially higher temperature. Due to coupling with the ISN flow speed along the ISN parameter tube, we provide the temperature {T}{VISN∞ }=8710+440/-680 K for {V}{ISN∞ }=26 {km} {{{s}}}-1 for comparison, where most of the uncertainty is systematic and likely due to the presence of the Warm Breeze.

Optimization Under Uncertainty for Wake Steering Strategies: Preprint

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

Quick, Julian; Annoni, Jennifer; King, Ryan N

Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presencemore » of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.« less

Big data uncertainties.

PubMed

Maugis, Pierre-André G

2018-07-01

Big data-the idea that an always-larger volume of information is being constantly recorded-suggests that new problems can now be subjected to scientific scrutiny. However, can classical statistical methods be used directly on big data? We analyze the problem by looking at two known pitfalls of big datasets. First, that they are biased, in the sense that they do not offer a complete view of the populations under consideration. Second, that they present a weak but pervasive level of dependence between all their components. In both cases we observe that the uncertainty of the conclusion obtained by statistical methods is increased when used on big data, either because of a systematic error (bias), or because of a larger degree of randomness (increased variance). We argue that the key challenge raised by big data is not only how to use big data to tackle new problems, but to develop tools and methods able to rigorously articulate the new risks therein. Copyright © 2016. Published by Elsevier Ltd.

Managing uncertainty in collaborative robotics engineering projects: The influence of task structure and peer interaction

NASA Astrophysics Data System (ADS)

Jordan, Michelle

Uncertainty is ubiquitous in life, and learning is an activity particularly likely to be fraught with uncertainty. Previous research suggests that students and teachers struggle in their attempts to manage the psychological experience of uncertainty and that students often fail to experience uncertainty when uncertainty may be warranted. Yet, few educational researchers have explicitly and systematically observed what students do, their behaviors and strategies, as they attempt to manage the uncertainty they experience during academic tasks. In this study I investigated how students in one fifth grade class managed uncertainty they experienced while engaged in collaborative robotics engineering projects, focusing particularly on how uncertainty management was influenced by task structure and students' interactions with their peer collaborators. The study was initiated at the beginning of instruction related to robotics engineering and preceded through the completion of several long-term collaborative robotics projects, one of which was a design project. I relied primarily on naturalistic observation of group sessions, semi-structured interviews, and collection of artifacts. My data analysis was inductive and interpretive, using qualitative discourse analysis techniques and methods of grounded theory. Three theoretical frameworks influenced the conception and design of this study: community of practice, distributed cognition, and complex adaptive systems theory. Uncertainty was a pervasive experience for the students collaborating in this instructional context. Students experienced uncertainty related to the project activity and uncertainty related to the social system as they collaborated to fulfill the requirements of their robotics engineering projects. They managed their uncertainty through a diverse set of tactics for reducing, ignoring, maintaining, and increasing uncertainty. Students experienced uncertainty from more different sources and used more and

Towards SI-traceable radio occultation excess phase processing with integrated uncertainty estimation for climate applications

NASA Astrophysics Data System (ADS)

Innerkofler, Josef; Pock, Christian; Kirchengast, Gottfried; Schwaerz, Marc; Jaeggi, Adrian; Schwarz, Jakob

2016-04-01

The GNSS Radio Occultation (RO) measurement technique is highly valuable for climate monitoring of the atmosphere as it provides accurate and precise measurements in the troposphere and stratosphere regions with global coverage, long-term stability, and virtually all-weather capability. The novel Reference Occultation Processing System (rOPS), currently under development at the WEGC at University of Graz aims to process raw RO measurements into essential climate variables, such as temperature, pressure, and tropospheric water vapor, in a way which is SI-traceable to the universal time standard and which includes rigorous uncertainty propagation. As part of this rOPS climate-quality processing system, accurate atmospheric excess phase profiles with new approaches integrating uncertainty propagation are derived from the raw occultation tracking data and orbit data. Regarding the latter, highly accurate orbit positions and velocities of the GNSS transmitter satellites and the RO receiver satellites in low Earth orbit (LEO) need to be determined, in order to enable high accuracy of the excess phase profiles. Using several representative test days of GPS orbit data from the CODE and IGS archives, which are available at accuracies of about 3 cm (position) / 0.03 mm/s (velocity), and employing Bernese 5.2 and Napeos 3.3.1 software packages for the LEO orbit determination of the CHAMP, GRACE, and MetOp RO satellites, we achieved robust SI-traced LEO orbit uncertainty estimates of about 5 cm (position) / 0.05 mm/s (velocity) for the daily orbits, including estimates of systematic uncertainty bounds and of propagated random uncertainties. For COSMIC RO satellites, we found decreased accuracy estimates near 10-15 cm (position) / 0.1-0.15 mm/s (velocity), since the characteristics of the small COSMIC satellite platforms and antennas provide somewhat less favorable orbit determination conditions. We present the setup of how we (I) used the Bernese and Napeos package in mutual

Tranexamic acid in epistaxis: a systematic review.

PubMed

Kamhieh, Y; Fox, H

2016-12-01

The role of tranexamic acid in the management of epistaxis remains unclear. There is uncertainty about its safety and about the contraindications for its use. We performed a systematic review of the use of systemic and topical tranexamic acid in epistaxis and a comparative review of its use in other specialties. This review assesses and summarises the existing evidence for the efficacy and safety of tranexamic acid in the management of epistaxis. Systematic review. MEDLINE and EMBASE were searched for 'epistaxis' and equivalent MESH terms, combined with the Boolean operator 'OR' and 'tranexamic acid'. The Cochrane library and society guidelines were reviewed for evidence regarding the use of tranexamic acid in other specialties. All five relevant RCTs were included in the review and were evaluated according to the recommendations of the Cochrane Handbook for Systematic Reviews. Three RCTS pertained to spontaneous epistaxis; of these, one trial found no benefit of oral tranexamic acid in acute epistaxis, one trial found no significant benefit of topical tranexamic acid, but the largest of the trials showed significant benefit of topical tranexamic acid in acute epistaxis management. Two RCTs examined oral tranexamic acid for prophylaxis of recurrent epistaxes in patients with hereditary haemorrhagic telangiectasia; both showed significant reduction in severity and frequency. Tranexamic acid, as a WHO 'essential medicine', is a powerful, readily available tool, the use of which in epistaxis has been limited by uncertainty over its efficacy and its safety profile. This systematic review summarises the existing evidence and extrapolates from the wealth of data for other specialties to address the clinical question - does TXA have a role in epistaxis management? © 2016 John Wiley & Sons Ltd.

Role of turbulence fluctuations on uncertainties of acoutic Doppler current profiler discharge measurements

USGS Publications Warehouse

Tarrab, Leticia; Garcia, Carlos M.; Cantero, Mariano I.; Oberg, Kevin

2012-01-01

This work presents a systematic analysis quantifying the role of the presence of turbulence fluctuations on uncertainties (random errors) of acoustic Doppler current profiler (ADCP) discharge measurements from moving platforms. Data sets of three-dimensional flow velocities with high temporal and spatial resolution were generated from direct numerical simulation (DNS) of turbulent open channel flow. Dimensionless functions relating parameters quantifying the uncertainty in discharge measurements due to flow turbulence (relative variance and relative maximum random error) to sampling configuration were developed from the DNS simulations and then validated with field-scale discharge measurements. The validated functions were used to evaluate the role of the presence of flow turbulence fluctuations on uncertainties in ADCP discharge measurements. The results of this work indicate that random errors due to the flow turbulence are significant when: (a) a low number of transects is used for a discharge measurement, and (b) measurements are made in shallow rivers using high boat velocity (short time for the boat to cross a flow turbulence structure).

Combined influence of CT random noise and HU-RSP calibration curve nonlinearities on proton range systematic errors

NASA Astrophysics Data System (ADS)

Brousmiche, S.; Souris, K.; Orban de Xivry, J.; Lee, J. A.; Macq, B.; Seco, J.

2017-11-01

Proton range random and systematic uncertainties are the major factors undermining the advantages of proton therapy, namely, a sharp dose falloff and a better dose conformality for lower doses in normal tissues. The influence of CT artifacts such as beam hardening or scatter can easily be understood and estimated due to their large-scale effects on the CT image, like cupping and streaks. In comparison, the effects of weakly-correlated stochastic noise are more insidious and less attention is drawn on them partly due to the common belief that they only contribute to proton range uncertainties and not to systematic errors thanks to some averaging effects. A new source of systematic errors on the range and relative stopping powers (RSP) has been highlighted and proved not to be negligible compared to the 3.5% uncertainty reference value used for safety margin design. Hence, we demonstrate that the angular points in the HU-to-RSP calibration curve are an intrinsic source of proton range systematic error for typical levels of zero-mean stochastic CT noise. Systematic errors on RSP of up to 1% have been computed for these levels. We also show that the range uncertainty does not generally vary linearly with the noise standard deviation. We define a noise-dependent effective calibration curve that better describes, for a given material, the RSP value that is actually used. The statistics of the RSP and the range continuous slowing down approximation (CSDA) have been analytically derived for the general case of a calibration curve obtained by the stoichiometric calibration procedure. These models have been validated against actual CSDA simulations for homogeneous and heterogeneous synthetical objects as well as on actual patient CTs for prostate and head-and-neck treatment planning situations.

[Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

PubMed

Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

2015-04-01

This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

PubMed Central

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

Assessing uncertainty in high-resolution spatial climate data across the US Northeast.

PubMed

Bishop, Daniel A; Beier, Colin M

2013-01-01

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980-2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products.

Making framing of uncertainty in water management practice explicit by using a participant-structured approach.

PubMed

Isendahl, Nicola; Dewulf, Art; Pahl-Wostl, Claudia

2010-01-01

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice. Copyright 2009 Elsevier Ltd. All rights reserved.

Model Uncertainties for Valencia RPA Effect for MINERvA

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

Gran, Richard

2017-05-08

This technical note describes the application of the Valencia RPA multi-nucleon effect and its uncertainty to QE reactions from the GENIE neutrino event generator. The analysis of MINERvA neutrino data in Rodrigues et al. PRL 116 071802 (2016) paper makes clear the need for an RPA suppression, especially at very low momentum and energy transfer. That published analysis does not constrain the magnitude of the effect; it only tests models with and without the effect against the data. Other MINERvA analyses need an expression of the model uncertainty in the RPA effect. A well-described uncertainty can be used for systematics for unfolding, for model errors in the analysis of non-QE samples, and as input for fitting exercises for model testing or constraining backgrounds. This prescription takes uncertainties on the parameters in the Valencia RPA model and adds a (not-as-tight) constraint from muon capture data. For MINERvA we apply it as a 2D (more » $$q_0$$,$$q_3$$) weight to GENIE events, in lieu of generating a full beyond-Fermi-gas quasielastic events. Because it is a weight, it can be applied to the generated and fully Geant4 simulated events used in analysis without a special GENIE sample. For some limited uses, it could be cast as a 1D $Q^2$ weight without much trouble. This procedure is a suitable starting point for NOvA and DUNE where the energy dependence is modest, but probably not adequate for T2K or MicroBooNE.« less

Exploiting risk-reward structures in decision making under uncertainty.

PubMed

Leuker, Christina; Pachur, Thorsten; Hertwig, Ralph; Pleskac, Timothy J

2018-06-01

People often have to make decisions under uncertainty-that is, in situations where the probabilities of obtaining a payoff are unknown or at least difficult to ascertain. One solution to this problem is to infer the probability from the magnitude of the potential payoff and thus exploit the inverse relationship between payoffs and probabilities that occurs in many domains in the environment. Here, we investigated how the mind may implement such a solution: (1) Do people learn about risk-reward relationships from the environment-and if so, how? (2) How do learned risk-reward relationships impact preferences in decision-making under uncertainty? Across three experiments (N = 352), we found that participants can learn risk-reward relationships from being exposed to choice environments with a negative, positive, or uncorrelated risk-reward relationship. They were able to learn the associations both from gambles with explicitly stated payoffs and probabilities (Experiments 1 & 2) and from gambles about epistemic events (Experiment 3). In subsequent decisions under uncertainty, participants often exploited the learned association by inferring probabilities from the magnitudes of the payoffs. This inference systematically influenced their preferences under uncertainty: Participants who had been exposed to a negative risk-reward relationship tended to prefer the uncertain option over a smaller sure option for low payoffs, but not for high payoffs. This pattern reversed in the positive condition and disappeared in the uncorrelated condition. This adaptive change in preferences is consistent with the use of the risk-reward heuristic. Copyright © 2018 Elsevier B.V. All rights reserved.

Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

NASA Astrophysics Data System (ADS)

Höllermann, Britta; Evers, Mariele

2017-04-01

Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working

Alternatives to connective tissue graft in the treatment of localized gingival recessions: A systematic review.

PubMed

Amine, K; El Amrani, Y; Chemlali, S; Kissa, J

2018-02-01

The aim of this Systematic Review (SR) was to assess the clinical efficacy of alternatives procedures; Acellular Dermal Matrix (ADM), Xenogeneic Collagen Matrix (XCM), Enamel Matrix Derivative (EMD) and Platelet Rich Fibrin (PRF), compared to conventional procedures in the treatment of localized gingival recessions. Electronic searches were performed to identify randomized clinical trials (RCTs) on treatment of single gingival recession with at least 6 months of follow-up. Applying guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA). The risk of bias was assessed using the Cochrane Collaboration's Risk of Bias tool. Eighteen randomized controlled trials (RCTs) with a total of 390 treated patients (606 recessions) were included. This systematic review showed that: Coronally Advanced Flap (CAF) in conjunction with ADM was significantly better than CAF alone, while the comparison between CAF+ADM and CTG was affected by large uncertainty. The CAF+EMD was significantly better than CAF alone, whereas the comparison between CAF+EMD and CTG was affected by large uncertainty. No significant difference was recorded when comparing CAF+XCM with CAF alone, and the comparison between CAF+XCM and CTG was affected by large uncertainty. The comparison between PRF and others technique was affected by large uncertainty. ADM, XCM and EMD assisted to CAF might be considered alternatives of CTG in the treatment of Miller class I and II gingival recession. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Disentangling dark energy and cosmic tests of gravity from weak lensing systematics

NASA Astrophysics Data System (ADS)

Laszlo, Istvan; Bean, Rachel; Kirk, Donnacha; Bridle, Sarah

2012-06-01

We consider the impact of key astrophysical and measurement systematics on constraints on dark energy and modifications to gravity on cosmic scales. We focus on upcoming photometric ‘stage III’ and ‘stage IV’ large-scale structure surveys such as the Dark Energy Survey (DES), the Subaru Measurement of Images and Redshifts survey, the Euclid survey, the Large Synoptic Survey Telescope (LSST) and Wide Field Infra-Red Space Telescope (WFIRST). We illustrate the different redshift dependencies of gravity modifications compared to intrinsic alignments, the main astrophysical systematic. The way in which systematic uncertainties, such as galaxy bias and intrinsic alignments, are modelled can change dark energy equation-of-state parameter and modified gravity figures of merit by a factor of 4. The inclusion of cross-correlations of cosmic shear and galaxy position measurements helps reduce the loss of constraining power from the lensing shear surveys. When forecasts for Planck cosmic microwave background and stage IV surveys are combined, constraints on the dark energy equation-of-state parameter and modified gravity model are recovered, relative to those from shear data with no systematic uncertainties, provided fewer than 36 free parameters in total are used to describe the galaxy bias and intrinsic alignment models as a function of scale and redshift. While some uncertainty in the intrinsic alignment (IA) model can be tolerated, it is going to be important to be able to parametrize IAs well in order to realize the full potential of upcoming surveys. To facilitate future investigations, we also provide a fitting function for the matter power spectrum arising from the phenomenological modified gravity model we consider.

Uncertainty in hydrological signatures

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Westerberg, Ida

2015-04-01

Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Uncertainty in geocenter estimates in the context of ITRF2014

NASA Astrophysics Data System (ADS)

Riddell, Anna R.; King, Matt A.; Watson, Christopher S.; Sun, Yu; Riva, Riccardo E. M.; Rietbroek, Roelof

2017-05-01

Uncertainty in the geocenter position and its subsequent motion affects positioning estimates on the surface of the Earth and downstream products such as site velocities, particularly the vertical component. The current version of the International Terrestrial Reference Frame, ITRF2014, derives its origin as the long-term averaged center of mass as sensed by satellite laser ranging (SLR), and by definition, it adopts only linear motion of the origin with uncertainty determined using a white noise process. We compare weekly SLR translations relative to the ITRF2014 origin, with network translations estimated from station displacements from surface mass transport models. We find that the proportion of variance explained in SLR translations by the model-derived translations is on average less than 10%. Time-correlated noise and nonlinear rates, particularly evident in the Y and Z components of the SLR translations with respect to the ITRF2014 origin, are not fully replicated by the model-derived translations. This suggests that translation-related uncertainties are underestimated when a white noise model is adopted and that substantial systematic errors remain in the data defining the ITRF origin. When using a white noise model, we find uncertainties in the rate of SLR X, Y, and Z translations of ±0.03, ±0.03, and ±0.06, respectively, increasing to ±0.13, ±0.17, and ±0.33 (mm/yr, 1 sigma) when a power law and white noise model is adopted.

Associating uncertainty with datasets using Linked Data and allowing propagation via provenance chains

NASA Astrophysics Data System (ADS)

Car, Nicholas; Cox, Simon; Fitch, Peter

2015-04-01

With earth-science datasets increasingly being published to enable re-use in projects disassociated from the original data acquisition or generation, there is an urgent need for associated metadata to be connected, in order to guide their application. In particular, provenance traces should support the evaluation of data quality and reliability. However, while standards for describing provenance are emerging (e.g. PROV-O), these do not include the necessary statistical descriptors and confidence assessments. UncertML has a mature conceptual model that may be used to record uncertainty metadata. However, by itself UncertML does not support the representation of uncertainty of multi-part datasets, and provides no direct way of associating the uncertainty information - metadata in relation to a dataset - with dataset objects.We present a method to address both these issues by combining UncertML with PROV-O, and delivering resulting uncertainty-enriched provenance traces through the Linked Data API. UncertProv extends the PROV-O provenance ontology with an RDF formulation of the UncertML conceptual model elements, adds further elements to support uncertainty representation without a conceptual model and the integration of UncertML through links to documents. The Linked ID API provides a systematic way of navigating from dataset objects to their UncertProv metadata and back again. The Linked Data API's 'views' capability enables access to UncertML and non-UncertML uncertainty metadata representations for a dataset. With this approach, it is possible to access and navigate the uncertainty metadata associated with a published dataset using standard semantic web tools, such as SPARQL queries. Where the uncertainty data follows the UncertML model it can be automatically interpreted and may also support automatic uncertainty propagation . Repositories wishing to enable uncertainty propagation for all datasets must ensure that all elements that are associated with uncertainty

Optimal observation network design for conceptual model discrimination and uncertainty reduction

NASA Astrophysics Data System (ADS)

Pham, Hai V.; Tsai, Frank T.-C.

2016-02-01

This study expands the Box-Hill discrimination function to design an optimal observation network to discriminate conceptual models and, in turn, identify a most favored model. The Box-Hill discrimination function measures the expected decrease in Shannon entropy (for model identification) before and after the optimal design for one additional observation. This study modifies the discrimination function to account for multiple future observations that are assumed spatiotemporally independent and Gaussian-distributed. Bayesian model averaging (BMA) is used to incorporate existing observation data and quantify future observation uncertainty arising from conceptual and parametric uncertainties in the discrimination function. In addition, the BMA method is adopted to predict future observation data in a statistical sense. The design goal is to find optimal locations and least data via maximizing the Box-Hill discrimination function value subject to a posterior model probability threshold. The optimal observation network design is illustrated using a groundwater study in Baton Rouge, Louisiana, to collect additional groundwater heads from USGS wells. The sources of uncertainty creating multiple groundwater models are geological architecture, boundary condition, and fault permeability architecture. Impacts of considering homoscedastic and heteroscedastic future observation data and the sources of uncertainties on potential observation areas are analyzed. Results show that heteroscedasticity should be considered in the design procedure to account for various sources of future observation uncertainty. After the optimal design is obtained and the corresponding data are collected for model updating, total variances of head predictions can be significantly reduced by identifying a model with a superior posterior model probability.

Do Orthopaedic Surgeons Acknowledge Uncertainty?

PubMed

Teunis, Teun; Janssen, Stein; Guitton, Thierry G; Ring, David; Parisien, Robert

2016-06-01

Much of the decision-making in orthopaedics rests on uncertain evidence. Uncertainty is therefore part of our normal daily practice, and yet physician uncertainty regarding treatment could diminish patients' health. It is not known if physician uncertainty is a function of the evidence alone or if other factors are involved. With added experience, uncertainty could be expected to diminish, but perhaps more influential are things like physician confidence, belief in the veracity of what is published, and even one's religious beliefs. In addition, it is plausible that the kind of practice a physician works in can affect the experience of uncertainty. Practicing physicians may not be immediately aware of these effects on how uncertainty is experienced in their clinical decision-making. We asked: (1) Does uncertainty and overconfidence bias decrease with years of practice? (2) What sociodemographic factors are independently associated with less recognition of uncertainty, in particular belief in God or other deity or deities, and how is atheism associated with recognition of uncertainty? (3) Do confidence bias (confidence that one's skill is greater than it actually is), degree of trust in the orthopaedic evidence, and degree of statistical sophistication correlate independently with recognition of uncertainty? We created a survey to establish an overall recognition of uncertainty score (four questions), trust in the orthopaedic evidence base (four questions), confidence bias (three questions), and statistical understanding (six questions). Seven hundred six members of the Science of Variation Group, a collaboration that aims to study variation in the definition and treatment of human illness, were approached to complete our survey. This group represents mainly orthopaedic surgeons specializing in trauma or hand and wrist surgery, practicing in Europe and North America, of whom the majority is involved in teaching. Approximately half of the group has more than 10 years

Prediction uncertainty and optimal experimental design for learning dynamical systems.

PubMed

Letham, Benjamin; Letham, Portia A; Rudin, Cynthia; Browne, Edward P

2016-06-01

Dynamical systems are frequently used to model biological systems. When these models are fit to data, it is necessary to ascertain the uncertainty in the model fit. Here, we present prediction deviation, a metric of uncertainty that determines the extent to which observed data have constrained the model's predictions. This is accomplished by solving an optimization problem that searches for a pair of models that each provides a good fit for the observed data, yet has maximally different predictions. We develop a method for estimating a priori the impact that additional experiments would have on the prediction deviation, allowing the experimenter to design a set of experiments that would most reduce uncertainty. We use prediction deviation to assess uncertainty in a model of interferon-alpha inhibition of viral infection, and to select a sequence of experiments that reduces this uncertainty. Finally, we prove a theoretical result which shows that prediction deviation provides bounds on the trajectories of the underlying true model. These results show that prediction deviation is a meaningful metric of uncertainty that can be used for optimal experimental design.

Variability And Uncertainty Analysis Of Contaminant Transport Model Using Fuzzy Latin Hypercube Sampling Technique

NASA Astrophysics Data System (ADS)

Kumar, V.; Nayagum, D.; Thornton, S.; Banwart, S.; Schuhmacher2, M.; Lerner, D.

2006-12-01

Characterization of uncertainty associated with groundwater quality models is often of critical importance, as for example in cases where environmental models are employed in risk assessment. Insufficient data, inherent variability and estimation errors of environmental model parameters introduce uncertainty into model predictions. However, uncertainty analysis using conventional methods such as standard Monte Carlo sampling (MCS) may not be efficient, or even suitable, for complex, computationally demanding models and involving different nature of parametric variability and uncertainty. General MCS or variant of MCS such as Latin Hypercube Sampling (LHS) assumes variability and uncertainty as a single random entity and the generated samples are treated as crisp assuming vagueness as randomness. Also when the models are used as purely predictive tools, uncertainty and variability lead to the need for assessment of the plausible range of model outputs. An improved systematic variability and uncertainty analysis can provide insight into the level of confidence in model estimates, and can aid in assessing how various possible model estimates should be weighed. The present study aims to introduce, Fuzzy Latin Hypercube Sampling (FLHS), a hybrid approach of incorporating cognitive and noncognitive uncertainties. The noncognitive uncertainty such as physical randomness, statistical uncertainty due to limited information, etc can be described by its own probability density function (PDF); whereas the cognitive uncertainty such estimation error etc can be described by the membership function for its fuzziness and confidence interval by ?-cuts. An important property of this theory is its ability to merge inexact generated data of LHS approach to increase the quality of information. The FLHS technique ensures that the entire range of each variable is sampled with proper incorporation of uncertainty and variability. A fuzzified statistical summary of the model results will

Different methodologies to quantify uncertainties of air emissions.

PubMed

Romano, Daniela; Bernetti, Antonella; De Lauretis, Riccardo

2004-10-01

Characterization of the uncertainty associated with air emission estimates is of critical importance especially in the compilation of air emission inventories. In this paper, two different theories are discussed and applied to evaluate air emissions uncertainty. In addition to numerical analysis, which is also recommended in the framework of the United Nation Convention on Climate Change guidelines with reference to Monte Carlo and Bootstrap simulation models, fuzzy analysis is also proposed. The methodologies are discussed and applied to an Italian example case study. Air concentration values are measured from two electric power plants: a coal plant, consisting of two boilers and a fuel oil plant, of four boilers; the pollutants considered are sulphur dioxide (SO(2)), nitrogen oxides (NO(X)), carbon monoxide (CO) and particulate matter (PM). Monte Carlo, Bootstrap and fuzzy methods have been applied to estimate uncertainty of these data. Regarding Monte Carlo, the most accurate results apply to Gaussian distributions; a good approximation is also observed for other distributions with almost regular features either positive asymmetrical or negative asymmetrical. Bootstrap, on the other hand, gives a good uncertainty estimation for irregular and asymmetrical distributions. The logic of fuzzy analysis, where data are represented as vague and indefinite in opposition to the traditional conception of neatness, certain classification and exactness of the data, follows a different description. In addition to randomness (stochastic variability) only, fuzzy theory deals with imprecision (vagueness) of data. Fuzzy variance of the data set was calculated; the results cannot be directly compared with empirical data but the overall performance of the theory is analysed. Fuzzy theory may appear more suitable for qualitative reasoning than for a quantitative estimation of uncertainty, but it suits well when little information and few measurements are available and when

Additional Samples: Where They Should Be Located

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

Pilger, G. G., E-mail: jfelipe@ufrgs.br; Costa, J. F. C. L.; Koppe, J. C.

2001-09-15

Information for mine planning requires to be close spaced, if compared to the grid used for exploration and resource assessment. The additional samples collected during quasimining usually are located in the same pattern of the original diamond drillholes net but closer spaced. This procedure is not the best in mathematical sense for selecting a location. The impact of an additional information to reduce the uncertainty about the parameter been modeled is not the same everywhere within the deposit. Some locations are more sensitive in reducing the local and global uncertainty than others. This study introduces a methodology to select additionalmore » sample locations based on stochastic simulation. The procedure takes into account data variability and their spatial location. Multiple equally probable models representing a geological attribute are generated via geostatistical simulation. These models share basically the same histogram and the same variogram obtained from the original data set. At each block belonging to the model a value is obtained from the n simulations and their combination allows one to access local variability. Variability is measured using an uncertainty index proposed. This index was used to map zones of high variability. A value extracted from a given simulation is added to the original data set from a zone identified as erratic in the previous maps. The process of adding samples and simulation is repeated and the benefit of the additional sample is evaluated. The benefit in terms of uncertainty reduction is measure locally and globally. The procedure showed to be robust and theoretically sound, mapping zones where the additional information is most beneficial. A case study in a coal mine using coal seam thickness illustrates the method.« less

Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates

NASA Astrophysics Data System (ADS)

Moore, Christopher J.; Gair, Jonathan R.

2014-12-01

Posterior distributions on parameters computed from experimental data using Bayesian techniques are only as accurate as the models used to construct them. In many applications, these models are incomplete, which both reduces the prospects of detection and leads to a systematic error in the parameter estimates. In the analysis of data from gravitational wave detectors, for example, accurate waveform templates can be computed using numerical methods, but the prohibitive cost of these simulations means this can only be done for a small handful of parameters. In this Letter, a novel method to fold model uncertainties into data analysis is proposed; the waveform uncertainty is analytically marginalized over using with a prior distribution constructed by using Gaussian process regression to interpolate the waveform difference from a small training set of accurate templates. The method is well motivated, easy to implement, and no more computationally expensive than standard techniques. The new method is shown to perform extremely well when applied to a toy problem. While we use the application to gravitational wave data analysis to motivate and illustrate the technique, it can be applied in any context where model uncertainties exist.

Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty.

PubMed

Lash, Timothy L

2007-11-26

The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is

TSUNAMI Primer: A Primer for Sensitivity/Uncertainty Calculations with SCALE

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

Rearden, Bradley T; Mueller, Don; Bowman, Stephen M

2009-01-01

This primer presents examples in the application of the SCALE/TSUNAMI tools to generate k{sub eff} sensitivity data for one- and three-dimensional models using TSUNAMI-1D and -3D and to examine uncertainties in the computed k{sub eff} values due to uncertainties in the cross-section data used in their calculation. The proper use of unit cell data and need for confirming the appropriate selection of input parameters through direct perturbations are described. The uses of sensitivity and uncertainty data to identify and rank potential sources of computational bias in an application system and TSUNAMI tools for assessment of system similarity using sensitivity andmore » uncertainty criteria are demonstrated. Uses of these criteria in trending analyses to assess computational biases, bias uncertainties, and gap analyses are also described. Additionally, an application of the data adjustment tool TSURFER is provided, including identification of specific details of sources of computational bias.« less

Characterizing unknown systematics in large scale structure surveys

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

Agarwal, Nishant; Ho, Shirley; Myers, Adam D.

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data,more » we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.« less

Measuring the uncertainties of discharge measurements: interlaboratory experiments in hydrometry

NASA Astrophysics Data System (ADS)

Le Coz, Jérôme; Blanquart, Bertrand; Pobanz, Karine; Dramais, Guillaume; Pierrefeu, Gilles; Hauet, Alexandre; Despax, Aurélien

2015-04-01

Quantifying the uncertainty of streamflow data is key for hydrological sciences. The conventional uncertainty analysis based on error propagation techniques is restricted by the absence of traceable discharge standards and by the weight of difficult-to-predict errors related to the operator, procedure and measurement environment. Field interlaboratory experiments recently emerged as an efficient, standardized method to 'measure' the uncertainties of a given streamgauging technique in given measurement conditions. Both uncertainty approaches are compatible and should be developed jointly in the field of hydrometry. In the recent years, several interlaboratory experiments have been reported by different hydrological services. They involved different streamgauging techniques, including acoustic profilers (ADCP), current-meters and handheld radars (SVR). Uncertainty analysis was not always their primary goal: most often, testing the proficiency and homogeneity of instruments, makes and models, procedures and operators was the original motivation. When interlaboratory experiments are processed for uncertainty analysis, once outliers have been discarded all participants are assumed to be equally skilled and to apply the same streamgauging technique in equivalent conditions. A universal requirement is that all participants simultaneously measure the same discharge, which shall be kept constant within negligible variations. To our best knowledge, we were the first to apply the interlaboratory method for computing the uncertainties of streamgauging techniques, according to the authoritative international documents (ISO standards). Several specific issues arise due to the measurements conditions in outdoor canals and rivers. The main limitation is that the best available river discharge references are usually too uncertain to quantify the bias of the streamgauging technique, i.e. the systematic errors that are common to all participants in the experiment. A reference or a

Multimodel Uncertainty Changes in Simulated River Flows Induced by Human Impact Parameterizations

NASA Technical Reports Server (NTRS)

Liu, Xingcai; Tang, Qiuhong; Cui, Huijuan; Mu, Mengfei; Gerten Dieter; Gosling, Simon; Masaki, Yoshimitsu; Satoh, Yusuke; Wada, Yoshihide

2017-01-01

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971-2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2 for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20 to 5, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20 to 20. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not only

Target position uncertainty during visually guided deep-inspiration breath-hold radiotherapy in locally advanced lung cancer.

PubMed

Scherman Rydhög, Jonas; Riisgaard de Blanck, Steen; Josipovic, Mirjana; Irming Jølck, Rasmus; Larsen, Klaus Richter; Clementsen, Paul; Lars Andersen, Thomas; Poulsen, Per Rugaard; Fredberg Persson, Gitte; Munck Af Rosenschold, Per

2017-04-01

The purpose of this study was to estimate the uncertainty in voluntary deep-inspiration breath-hold (DIBH) radiotherapy for locally advanced non-small cell lung cancer (NSCLC) patients. Perpendicular fluoroscopic movies were acquired in free breathing (FB) and DIBH during a course of visually guided DIBH radiotherapy of nine patients with NSCLC. Patients had liquid markers injected in mediastinal lymph nodes and primary tumours. Excursion, systematic- and random errors, and inter-breath-hold position uncertainty were investigated using an image based tracking algorithm. A mean reduction of 2-6mm in marker excursion in DIBH versus FB was seen in the anterior-posterior (AP), left-right (LR) and cranio-caudal (CC) directions. Lymph node motion during DIBH originated from cardiac motion. The systematic- (standard deviation (SD) of all the mean marker positions) and random errors (root-mean-square of the intra-BH SD) during DIBH were 0.5 and 0.3mm (AP), 0.5 and 0.3mm (LR), 0.8 and 0.4mm (CC), respectively. The mean inter-breath-hold shifts were -0.3mm (AP), -0.2mm (LR), and -0.2mm (CC). Intra- and inter-breath-hold uncertainty of tumours and lymph nodes were small in visually guided breath-hold radiotherapy of NSCLC. Target motion could be substantially reduced, but not eliminated, using visually guided DIBH. Copyright © 2017 Elsevier B.V. All rights reserved.

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

NASA Astrophysics Data System (ADS)

Wahl, N.; Hennig, P.; Wieser, H. P.; Bangert, M.

2017-07-01

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ≤slant {5} min). The resulting standard deviation (expectation value) of dose show average global γ{3% / {3}~mm} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy.

PubMed

Wahl, N; Hennig, P; Wieser, H P; Bangert, M

2017-06-26

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU [Formula: see text] min). The resulting standard deviation (expectation value) of dose show average global [Formula: see text] pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time

[A correlational study on uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers].

PubMed

Yoo, Kyung Hee

2007-06-01

This study was conducted to investigate the correlation among uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers. Self report questionnaires were used to measure the variables. Variables were uncertainty, mastery and appraisal of uncertainty. In data analysis, the SPSSWIN 12.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Reliability of the instruments was cronbach's alpha=.84~.94. Mastery negatively correlated with uncertainty(r=-.444, p=.000) and danger appraisal of uncertainty(r=-.514, p=.000). In regression of danger appraisal of uncertainty, uncertainty and mastery were significant predictors explaining 39.9%. Mastery was a significant mediating factor between uncertainty and danger appraisal of uncertainty in hospitalized children's mothers. Therefore, nursing interventions which improve mastery must be developed for hospitalized children's mothers.

Neutrino mass hierarchy and precision physics with medium-baseline reactors: Impact of energy-scale and flux-shape uncertainties

NASA Astrophysics Data System (ADS)

Capozzi, F.; Lisi, E.; Marrone, A.

2015-11-01

Nuclear reactors provide intense sources of electron antineutrinos, characterized by few-MeV energy E and unoscillated spectral shape Φ (E ). High-statistics observations of reactor neutrino oscillations over medium-baseline distances L ˜O (50 ) km would provide unprecedented opportunities to probe both the long-wavelength mass-mixing parameters (δ m2 and θ12) and the short-wavelength ones (Δ mee 2 and θ13), together with the subtle interference effects associated with the neutrino mass hierarchy (either normal or inverted). In a given experimental setting—here taken as in the JUNO project for definiteness—the achievable hierarchy sensitivity and parameter accuracy depend not only on the accumulated statistics but also on systematic uncertainties, which include (but are not limited to) the mass-mixing priors and the normalizations of signals and backgrounds. We examine, in addition, the effect of introducing smooth deformations of the detector energy scale, E →E'(E ), and of the reactor flux shape, Φ (E )→Φ'(E ), within reasonable error bands inspired by state-of-the-art estimates. It turns out that energy-scale and flux-shape systematics can noticeably affect the performance of a JUNO-like experiment, both on the hierarchy discrimination and on precision oscillation physics. It is shown that a significant reduction of the assumed energy-scale and flux-shape uncertainties (by, say, a factor of 2) would be highly beneficial to the physics program of medium-baseline reactor projects. Our results also shed some light on the role of the inverse-beta decay threshold, of geoneutrino backgrounds, and of matter effects in the analysis of future reactor oscillation data.

Effects of radiobiological uncertainty on shield design for a 60-day lunar mission

NASA Technical Reports Server (NTRS)

Wilson, John W.; Nealy, John E.; Schimmerling, Walter

1993-01-01

Some consequences of uncertainties in radiobiological risk due to galactic cosmic ray exposure are analyzed to determine their effect on engineering designs for a first lunar outpost - a 60-day mission. Quantitative estimates of shield mass requirements as a function of a radiobiological uncertainty factor are given for a simplified vehicle structure. The additional shield mass required for compensation is calculated as a function of the uncertainty in galactic cosmic ray exposure, and this mass is found to be as large as a factor of 3 for a lunar transfer vehicle. The additional cost resulting from this mass is also calculated. These cost estimates are then used to exemplify the cost-effectiveness of research.

Uncertainties in (E)UV model atmosphere fluxes

NASA Astrophysics Data System (ADS)

Rauch, T.

2008-04-01

Context: During the comparison of synthetic spectra calculated with two NLTE model atmosphere codes, namely TMAP and TLUSTY, we encounter systematic differences in the EUV fluxes due to the treatment of level dissolution by pressure ionization. Aims: In the case of Sirius B, we demonstrate an uncertainty in modeling the EUV flux reliably in order to challenge theoreticians to improve the theory of level dissolution. Methods: We calculated synthetic spectra for hot, compact stars using state-of-the-art NLTE model-atmosphere techniques. Results: Systematic differences may occur due to a code-specific cutoff frequency of the H I Lyman bound-free opacity. This is the case for TMAP and TLUSTY. Both codes predict the same flux level at wavelengths lower than about 1500 Å for stars with effective temperatures (T_eff) below about 30 000 K only, if the same cutoff frequency is chosen. Conclusions: The theory of level dissolution in high-density plasmas, which is available for hydrogen only should be generalized to all species. Especially, the cutoff frequencies for the bound-free opacities should be defined in order to make predictions of UV fluxes more reliable.

Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

NASA Astrophysics Data System (ADS)

Lewandowsky, S.; Risbey, J.; Smithson, M.; Newell, B. R.

2012-12-01

Uncertainty forms an integral part of climate science, and it is often cited in connection with arguments against mitigative action. We argue that an analysis of uncertainty must consider existing knowledge as well as uncertainty, and the two must be evaluated with respect to the outcomes and risks associated with possible policy options. Although risk judgments are inherently subjective, an analysis of the role of uncertainty within the climate system yields two constraints that are robust to a broad range of assumptions. Those constraints are that (a) greater uncertainty about the climate system is necessarily associated with greater expected damages from warming, and (b) greater uncertainty translates into a greater risk of the failure of mitigation efforts. These ordinal constraints are unaffected by subjective or cultural risk-perception factors, they are independent of the discount rate, and they are independent of the magnitude of the estimate for climate sensitivity. The constraints mean that any appeal to uncertainty must imply a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

Known unknowns: building an ethics of uncertainty into genomic medicine.

PubMed

Newson, Ainsley J; Leonard, Samantha J; Hall, Alison; Gaff, Clara L

2016-09-01

Genomic testing has reached the point where, technically at least, it can be cheaper to undertake panel-, exome- or whole genome testing than it is to sequence a single gene. An attribute of these approaches is that information gleaned will often have uncertain significance. In addition to the challenges this presents for pre-test counseling and informed consent, a further consideration emerges over how - ethically - we should conceive of and respond to this uncertainty. To date, the ethical aspects of uncertainty in genomics have remained under-explored. In this paper, we draft a conceptual and ethical response to the question of how to conceive of and respond to uncertainty in genomic medicine. After introducing the problem, we articulate a concept of 'genomic uncertainty'. Drawing on this, together with exemplar clinical cases and related empirical literature, we then critique the presumption that uncertainty is always problematic and something to be avoided, or eradicated. We conclude by outlining an 'ethics of genomic uncertainty'; describing how we might handle uncertainty in genomic medicine. This involves fostering resilience, welfare, autonomy and solidarity. Uncertainty will be an inherent aspect of clinical practice in genomics for some time to come. Genomic testing should not be offered with the explicit aim to reduce uncertainty. Rather, uncertainty should be appraised, adapted to and communicated about as part of the process of offering and providing genomic information.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

NASA Astrophysics Data System (ADS)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Communicating spatial uncertainty to non-experts using R

NASA Astrophysics Data System (ADS)

Luzzi, Damiano; Sawicka, Kasia; Heuvelink, Gerard; de Bruin, Sytze

2016-04-01

Effective visualisation methods are important for the efficient use of uncertainty information for various groups of users. Uncertainty propagation analysis is often used with spatial environmental models to quantify the uncertainty within the information. A challenge arises when trying to effectively communicate the uncertainty information to non-experts (not statisticians) in a wide range of cases. Due to the growing popularity and applicability of the open source programming language R, we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. The package has implemented Monte Carlo algorithms for uncertainty propagation, the output of which is represented by an ensemble of model outputs (i.e. a sample from a probability distribution). Numerous visualisation methods exist that aim to present such spatial uncertainty information both statically, dynamically and interactively. To provide the most universal visualisation tools for non-experts, we conducted a survey on a group of 20 university students and assessed the effectiveness of selected static and interactive methods for visualising uncertainty in spatial variables such as DEM and land cover. The static methods included adjacent maps and glyphs for continuous variables. Both allow for displaying maps with information about the ensemble mean, variance/standard deviation and prediction intervals. Adjacent maps were also used for categorical data, displaying maps of the most probable class, as well as its associated probability. The interactive methods included a graphical user interface, which in addition to displaying the previously mentioned variables also allowed for comparison of joint uncertainties at multiple locations. The survey indicated that users could understand the basics of the uncertainty information displayed in the static maps, with the interactive interface allowing for more in-depth information. Subsequently, the R

Exploring the implication of climate process uncertainties within the Earth System Framework

NASA Astrophysics Data System (ADS)

Booth, B.; Lambert, F. H.; McNeal, D.; Harris, G.; Sexton, D.; Boulton, C.; Murphy, J.

2011-12-01

Uncertainties in the magnitude of future climate change have been a focus of a great deal of research. Much of the work with General Circulation Models has focused on the atmospheric response to changes in atmospheric composition, while other processes remain outside these frameworks. Here we introduce an ensemble of new simulations, based on an Earth System configuration of HadCM3C, designed to explored uncertainties in both physical (atmospheric, oceanic and aerosol physics) and carbon cycle processes, using perturbed parameter approaches previously used to explore atmospheric uncertainty. Framed in the context of the climate response to future changes in emissions, the resultant future projections represent significantly broader uncertainty than existing concentration driven GCM assessments. The systematic nature of the ensemble design enables interactions between components to be explored. For example, we show how metrics of physical processes (such as climate sensitivity) are also influenced carbon cycle parameters. The suggestion from this work is that carbon cycle processes represent a comparable contribution to uncertainty in future climate projections as contributions from atmospheric feedbacks more conventionally explored. The broad range of climate responses explored within these ensembles, rather than representing a reason for inaction, provide information on lower likelihood but high impact changes. For example while the majority of these simulations suggest that future Amazon forest extent is resilient to the projected climate changes, a small number simulate dramatic forest dieback. This ensemble represents a framework to examine these risks, breaking them down into physical processes (such as ocean temperature drivers of rainfall change) and vegetation processes (where uncertainties point towards requirements for new observational constraints).

Genome Size Variation in the Genus Carthamus (Asteraceae, Cardueae): Systematic Implications and Additive Changes During Allopolyploidization

PubMed Central

GARNATJE, TERESA; GARCIA, SÒNIA; VILATERSANA, ROSER; VALLÈS, JOAN

2006-01-01

• Background and Aims Plant genome size is an important biological characteristic, with relationships to systematics, ecology and distribution. Currently, there is no information regarding nuclear DNA content for any Carthamus species. In addition to improving the knowledge base, this research focuses on interspecific variation and its implications for the infrageneric classification of this genus. Genome size variation in the process of allopolyploid formation is also addressed. • Methods Nuclear DNA samples from 34 populations of 16 species of the genus Carthamus were assessed by flow cytometry using propidium iodide. • Key Results The 2C values ranged from 2·26 pg for C. leucocaulos to 7·46 pg for C. turkestanicus, and monoploid genome size (1Cx-value) ranged from 1·13 pg in C. leucocaulos to 1·53 pg in C. alexandrinus. Mean genome sizes differed significantly, based on sectional classification. Both allopolyploid species (C. creticus and C. turkestanicus) exhibited nuclear DNA contents in accordance with the sum of the putative parental C-values (in one case with a slight reduction, frequent in polyploids), supporting their hybrid origin. • Conclusions Genome size represents a useful tool in elucidating systematic relationships between closely related species. A considerable reduction in monoploid genome size, possibly due to the hybrid formation, is also reported within these taxa. PMID:16390843

Approach to determine measurement uncertainty in complex nanosystems with multiparametric dependencies and multivariate output quantities

NASA Astrophysics Data System (ADS)

Hampel, B.; Liu, B.; Nording, F.; Ostermann, J.; Struszewski, P.; Langfahl-Klabes, J.; Bieler, M.; Bosse, H.; Güttler, B.; Lemmens, P.; Schilling, M.; Tutsch, R.

2018-03-01

In many cases, the determination of the measurement uncertainty of complex nanosystems provides unexpected challenges. This is in particular true for complex systems with many degrees of freedom, i.e. nanosystems with multiparametric dependencies and multivariate output quantities. The aim of this paper is to address specific questions arising during the uncertainty calculation of such systems. This includes the division of the measurement system into subsystems and the distinction between systematic and statistical influences. We demonstrate that, even if the physical systems under investigation are very different, the corresponding uncertainty calculation can always be realized in a similar manner. This is exemplarily shown in detail for two experiments, namely magnetic nanosensors and ultrafast electro-optical sampling of complex time-domain signals. For these examples the approach for uncertainty calculation following the guide to the expression of uncertainty in measurement (GUM) is explained, in which correlations between multivariate output quantities are captured. To illustate the versatility of the proposed approach, its application to other experiments, namely nanometrological instruments for terahertz microscopy, dimensional scanning probe microscopy, and measurement of concentration of molecules using surface enhanced Raman scattering, is shortly discussed in the appendix. We believe that the proposed approach provides a simple but comprehensive orientation for uncertainty calculation in the discussed measurement scenarios and can also be applied to similar or related situations.

Direct Aerosol Forcing Uncertainty

DOE Data Explorer

Mccomiskey, Allison

2008-01-15

Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are calculated at three locations representing distinct aerosol types and radiative environments. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective aerosol or surface property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 1.3 W m-2 (42 to 20%) depending on location (from tropical to polar sites), solar zenith angle, surface reflectance, aerosol type, and aerosol optical depth. The largest contributor to total uncertainty in DRF is usually single scattering albedo; however decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

USGS Publications Warehouse

Olea, R.A.; Luppens, J.A.; Tewalt, S.J.

2011-01-01

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling. ?? 2010.

Uncertainties in the estimation of specific absorption rate during radiofrequency alternating magnetic field induced non-adiabatic heating of ferrofluids

NASA Astrophysics Data System (ADS)

Lahiri, B. B.; Ranoo, Surojit; Philip, John

2017-11-01

Magnetic fluid hyperthermia (MFH) is becoming a viable cancer treatment methodology where the alternating magnetic field induced heating of magnetic fluid is utilized for ablating the cancerous cells or making them more susceptible to the conventional treatments. The heating efficiency in MFH is quantified in terms of specific absorption rate (SAR), which is defined as the heating power generated per unit mass. In majority of the experimental studies, SAR is evaluated from the temperature rise curves, obtained under non-adiabatic experimental conditions, which is prone to various thermodynamic uncertainties. A proper understanding of the experimental uncertainties and its remedies is a prerequisite for obtaining accurate and reproducible SAR. Here, we study the thermodynamic uncertainties associated with peripheral heating, delayed heating, heat loss from the sample and spatial variation in the temperature profile within the sample. Using first order approximations, an adiabatic reconstruction protocol for the measured temperature rise curves is developed for SAR estimation, which is found to be in good agreement with those obtained from the computationally intense slope corrected method. Our experimental findings clearly show that the peripheral and delayed heating are due to radiation heat transfer from the heating coils and slower response time of the sensor, respectively. Our results suggest that the peripheral heating is linearly proportional to the sample area to volume ratio and coil temperature. It is also observed that peripheral heating decreases in presence of a non-magnetic insulating shielding. The delayed heating is found to contribute up to ~25% uncertainties in SAR values. As the SAR values are very sensitive to the initial slope determination method, explicit mention of the range of linear regression analysis is appropriate to reproduce the results. The effect of sample volume to area ratio on linear heat loss rate is systematically studied and the

Exploring entropic uncertainty relation in the Heisenberg XX model with inhomogeneous magnetic field

NASA Astrophysics Data System (ADS)

Huang, Ai-Jun; Wang, Dong; Wang, Jia-Ming; Shi, Jia-Dong; Sun, Wen-Yang; Ye, Liu

2017-08-01

In this work, we investigate the quantum-memory-assisted entropic uncertainty relation in a two-qubit Heisenberg XX model with inhomogeneous magnetic field. It has been found that larger coupling strength J between the two spin-chain qubits can effectively reduce the entropic uncertainty. Besides, we observe the mechanics of how the inhomogeneous field influences the uncertainty, and find out that when the inhomogeneous field parameter b<1, the uncertainty will decrease with the decrease of the inhomogeneous field parameter b, conversely, the uncertainty will increase with decreasing b under the condition that b>1. Intriguingly, the entropic uncertainty can shrink to zero when the coupling coefficients are relatively large, while the entropic uncertainty only reduces to 1 with the increase of the homogeneous magnetic field. Additionally, we observe the purity of the state and Bell non-locality and obtain that the entropic uncertainty is anticorrelated with both the purity and Bell non-locality of the evolution state.

Coupled semivariogram uncertainty of hydrogeological and geophysical data on capture zone uncertainty analysis

USGS Publications Warehouse

Rahman, A.; Tsai, F.T.-C.; White, C.D.; Willson, C.S.

2008-01-01

This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis. ?? 2008 ASCE.

The application of the heuristic-systematic processing model to treatment decision making about prostate cancer.

PubMed

Steginga, Suzanne K; Occhipinti, Stefano

2004-01-01

The study investigated the utility of the Heuristic-Systematic Processing Model as a framework for the investigation of patient decision making. A total of 111 men recently diagnosed with localized prostate cancer were assessed using Verbal Protocol Analysis and self-report measures. Study variables included men's use of nonsystematic and systematic information processing, desire for involvement in decision making, and the individual differences of health locus of control, tolerance of ambiguity, and decision-related uncertainty. Most men (68%) preferred that decision making be shared equally between them and their doctor. Men's use of the expert opinion heuristic was related to men's verbal reports of decisional uncertainty and having a positive orientation to their doctor and medical care; a desire for greater involvement in decision making was predicted by a high internal locus of health control. Trends were observed for systematic information processing to increase when the heuristic strategy used was negatively affect laden and when men were uncertain about the probabilities for cure and side effects. There was a trend for decreased systematic processing when the expert opinion heuristic was used. Findings were consistent with the Heuristic-Systematic Processing Model and suggest that this model has utility for future research in applied decision making about health.

A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

NASA Astrophysics Data System (ADS)

Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

2017-09-01

Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.

Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

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

Newman, Jennifer F.; Clifton, Andrew

2017-03-08

Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

Uncertainty and Sensitivity Analyses of a Pebble Bed HTGR Loss of Cooling Event

DOE PAGES

Strydom, Gerhard

2013-01-01

The Very High Temperature Reactor Methods Development group at the Idaho National Laboratory identified the need for a defensible and systematic uncertainty and sensitivity approach in 2009. This paper summarizes the results of an uncertainty and sensitivity quantification investigation performed with the SUSA code, utilizing the International Atomic Energy Agency CRP 5 Pebble Bed Modular Reactor benchmark and the INL code suite PEBBED-THERMIX. Eight model input parameters were selected for inclusion in this study, and after the input parameters variations and probability density functions were specified, a total of 800 steady state and depressurized loss of forced cooling (DLOFC) transientmore » PEBBED-THERMIX calculations were performed. The six data sets were statistically analyzed to determine the 5% and 95% DLOFC peak fuel temperature tolerance intervals with 95% confidence levels. It was found that the uncertainties in the decay heat and graphite thermal conductivities were the most significant contributors to the propagated DLOFC peak fuel temperature uncertainty. No significant differences were observed between the results of Simple Random Sampling (SRS) or Latin Hypercube Sampling (LHS) data sets, and use of uniform or normal input parameter distributions also did not lead to any significant differences between these data sets.« less

Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

2016-12-01

Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate

A comprehensive evaluation of input data-induced uncertainty in nonpoint source pollution modeling

NASA Astrophysics Data System (ADS)

Chen, L.; Gong, Y.; Shen, Z.

2015-11-01

Watershed models have been used extensively for quantifying nonpoint source (NPS) pollution, but few studies have been conducted on the error-transitivity from different input data sets to NPS modeling. In this paper, the effects of four input data, including rainfall, digital elevation models (DEMs), land use maps, and the amount of fertilizer, on NPS simulation were quantified and compared. A systematic input-induced uncertainty was investigated using watershed model for phosphorus load prediction. Based on the results, the rain gauge density resulted in the largest model uncertainty, followed by DEMs, whereas land use and fertilizer amount exhibited limited impacts. The mean coefficient of variation for errors in single rain gauges-, multiple gauges-, ASTER GDEM-, NFGIS DEM-, land use-, and fertilizer amount information was 0.390, 0.274, 0.186, 0.073, 0.033 and 0.005, respectively. The use of specific input information, such as key gauges, is also highlighted to achieve the required model accuracy. In this sense, these results provide valuable information to other model-based studies for the control of prediction uncertainty.

Bayesian models for comparative analysis integrating phylogenetic uncertainty.

PubMed

de Villemereuil, Pierre; Wells, Jessie A; Edwards, Robert D; Blomberg, Simon P

2012-06-28

Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for phylogenetic comparative analyses

Bayesian models for comparative analysis integrating phylogenetic uncertainty

PubMed Central

2012-01-01

Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for

Asymmetric Uncertainty Expression for High Gradient Aerodynamics

NASA Technical Reports Server (NTRS)

Pinier, Jeremy T

2012-01-01

When the physics of the flow around an aircraft changes very abruptly either in time or space (e.g., flow separation/reattachment, boundary layer transition, unsteadiness, shocks, etc), the measurements that are performed in a simulated environment like a wind tunnel test or a computational simulation will most likely incorrectly predict the exact location of where (or when) the change in physics happens. There are many reasons for this, includ- ing the error introduced by simulating a real system at a smaller scale and at non-ideal conditions, or the error due to turbulence models in a computational simulation. The un- certainty analysis principles that have been developed and are being implemented today do not fully account for uncertainty in the knowledge of the location of abrupt physics changes or sharp gradients, leading to a potentially underestimated uncertainty in those areas. To address this problem, a new asymmetric aerodynamic uncertainty expression containing an extra term to account for a phase-uncertainty, the magnitude of which is emphasized in the high-gradient aerodynamic regions is proposed in this paper. Additionally, based on previous work, a method for dispersing aerodynamic data within asymmetric uncer- tainty bounds in a more realistic way has been developed for use within Monte Carlo-type analyses.

Modeling for waste management associated with environmental-impact abatement under uncertainty.

PubMed

Li, P; Li, Y P; Huang, G H; Zhang, J L

2015-04-01

Municipal solid waste (MSW) treatment can generate significant amounts of pollutants, and thus pose a risk on human health. Besides, in MSW management, various uncertainties exist in the related costs, impact factors, and objectives, which can affect the optimization processes and the decision schemes generated. In this study, a life cycle assessment-based interval-parameter programming (LCA-IPP) method is developed for MSW management associated with environmental-impact abatement under uncertainty. The LCA-IPP can effectively examine the environmental consequences based on a number of environmental impact categories (i.e., greenhouse gas equivalent, acid gas emissions, and respiratory inorganics), through analyzing each life cycle stage and/or major contributing process related to various MSW management activities. It can also tackle uncertainties existed in the related costs, impact factors, and objectives and expressed as interval numbers. Then, the LCA-IPP method is applied to MSW management for the City of Beijing, the capital of China, where energy consumptions and six environmental parameters [i.e., CO2, CO, CH4, NOX, SO2, inhalable particle (PM10)] are used as systematic tool to quantify environmental releases in entire life cycle stage of waste collection, transportation, treatment, and disposal of. Results associated with system cost, environmental impact, and the related policy implication are generated and analyzed. Results can help identify desired alternatives for managing MSW flows, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty.

REDD+ emissions estimation and reporting: dealing with uncertainty

NASA Astrophysics Data System (ADS)

Pelletier, Johanne; Martin, Davy; Potvin, Catherine

2013-09-01

The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries’ emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between ‘modelling sources’ of uncertainty, which refers to model-specific parameters and assumptions, and ‘recurring sources’ of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology

Evaluation of setup uncertainties for single-fraction SRS by comparing two different mask-creation methods

NASA Astrophysics Data System (ADS)

Baek, Jong Geun; Jang, Hyun Soo; Oh, Young Kee; Lee, Hyun Jeong; Kim, Eng Chan

2015-07-01

The purpose of this study was to evaluate the setup uncertainties for single-fraction stereotactic radiosurgery (SF-SRS) based on clinical data with two different mask-creation methods using pretreatment con-beam computed tomography imaging guidance. Dedicated frameless fixation Brain- LAB masks for 23 patients were created as a routine mask (R-mask) making method, as explained in the BrainLAB's user manual. Alternative masks (A-masks), which were created by modifying the cover range of the R-masks for the patient's head, were used for 23 patients. The systematic errors including these for each mask and stereotactic target localizer were analyzed, and the errors were calculated as the means ± standard deviations (SD) from the left-right (LR), superior-inferior (SI), anterior-posterior (AP), and yaw setup corrections. In addition, the frequencies of the threedimensional (3D) vector length were analyzed. The values of the mean setup corrections for the R-mask in all directions were < 0.7 mm and < 0.1°, whereas the magnitudes of the SDs were relatively large compared to the mean values. In contrast, the means and SDs of the A-mask were smaller than those for the R-mask with the exception of the SD in the AP direction. The means and SDs in the yaw rotational direction for the R-mask and the A-mask system were comparable. 3D vector shifts of larger magnitude occurred more frequently for the R-mask than the A-mask. The setup uncertainties for each mask with the stereotactic localizing system had an asymmetric offset towards the positive AP direction. The A-mask-creation method, which is capable of covering the top of the patient's head, is superior to that for the R-mask, so the use of the A-mask is encouraged for SF-SRS to reduce the setup uncertainties. Moreover, careful mask-making is required to prevent possible setup uncertainties.

The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: theoretical systematics and Baryon Acoustic Oscillations in the galaxy correlation function

NASA Astrophysics Data System (ADS)

Vargas-Magaña, Mariana; Ho, Shirley; Cuesta, Antonio J.; O'Connell, Ross; Ross, Ashley J.; Eisenstein, Daniel J.; Percival, Will J.; Grieb, Jan Niklas; Sánchez, Ariel G.; Tinker, Jeremy L.; Tojeiro, Rita; Beutler, Florian; Chuang, Chia-Hsun; Kitaura, Francisco-Shu; Prada, Francisco; Rodríguez-Torres, Sergio A.; Rossi, Graziano; Seo, Hee-Jong; Brownstein, Joel R.; Olmstead, Matthew; Thomas, Daniel

2018-06-01

We investigate the potential sources of theoretical systematics in the anisotropic Baryon Acoustic Oscillation (BAO) distance scale measurements from the clustering of galaxies in configuration space using the final Data Release (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS). We perform a detailed study of the impact on BAO measurements from choices in the methodology such as fiducial cosmology, clustering estimators, random catalogues, fitting templates, and covariance matrices. The theoretical systematic uncertainties in BAO parameters are found to be 0.002 in the isotropic dilation α and 0.003 in the quadrupolar dilation ɛ. The leading source of systematic uncertainty is related to the reconstruction techniques. Theoretical uncertainties are sub-dominant compared with the statistical uncertainties for BOSS survey, accounting 0.2σstat for α and 0.25σstat for ɛ (σα, stat ˜ 0.010 and σɛ, stat ˜ 0.012, respectively). We also present BAO-only distance scale constraints from the anisotropic analysis of the correlation function. Our constraints on the angular diameter distance DA(z) and the Hubble parameter H(z), including both statistical and theoretical systematic uncertainties, are 1.5 per cent and 2.8 per cent at zeff = 0.38, 1.4 per cent and 2.4 per cent at zeff = 0.51, and 1.7 per cent and 2.6 per cent at zeff = 0.61. This paper is part of a set that analyses the final galaxy clustering data set from BOSS. The measurements and likelihoods presented here are cross-checked with other BAO analysis in Alam et al. The systematic error budget concerning the methodology on post-reconstruction BAO analysis presented here is used in Alam et al. to produce the final cosmological constraints from BOSS.

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.

Understanding uncertainties in modeling the galactic diffuse gamma-ray emission

NASA Astrophysics Data System (ADS)

Storm, Emma; Calore, Francesca; Weniger, Christoph

2017-01-01

The nature of the Galactic diffuse gamma-ray emission as measured by the Fermi Gamma-ray Space Telescope has remained an active area of research for the last several years. A standard technique to disentangle the origins of the diffuse emission is the template fitting approach, where predictions for various diffuse components, such as emission from cosmic rays derived from Galprop or Dragon, are compared to the data. However, this method always results in an overall bad fit to the data, with strong residuals that are difficult to interpret. Additionally, there are instrinsic uncertainties in the predicted templates that are not accounted for naturally with this method. We therefore introduce a new template fitting approach to study the various components of the Galactic diffuse gamma-ray emission, and their correlations and uncertainties. We call this approach Sky Factorization with Adaptive Constrained Templates (SkyFACT). Rather than using fixed predictions from cosmic-ray propagation codes and examining the residuals to evaluate the quality of fits and the presence of excesses, we introduce additional fine-grained variations in the templates that account for uncertainties in the predictions, such as uncertainties in the gas tracers and from small scale variations in the density of cosmic rays. We show that fits to the gamma-ray diffuse emission can be dramatically improved by including an appropriate level of uncertainty in the initial spatial templates from cosmic-ray propagation codes. We further show that we can recover the morphology of the Fermi Bubbles from its spectrum alone with SkyFACT.

Uncertainty in exposure to air pollution

NASA Astrophysics Data System (ADS)

Pebesma, Edzer; Helle, Kristina; Christoph, Stasch; Rasouli, Soora; Timmermans, Harry; Walker, Sam-Erik; Denby, Bruce

2013-04-01

To assess exposure to air pollution for a person or for a group of people, one needs to know where the person or group is as a function of time, and what the air pollution is at these times and locations. In this study we used the Albatross activity-based model to assess the whereabouts of people and the uncertainties in this, and a probabilistic air quality system based on TAPM/EPISODE to assess air quality probabilistically. The outcomes of the two models were combined to assess exposure to air pollution, and the errors in it. We used the area around Rotterdam (Netherlands) as a case study. As the outcomes of both models come as Monte Carlo realizations, it was relatively easy to cancel one of the sources of uncertainty (movement of persons, air pollution) in order to identify their respective contributions, and also to compare evaluations for individuals with averages for a population of persons. As the output is probabilistic, and in addition spatially and temporally varying, the visual analysis of the complete results poses some challenges. This case study was one of the test cases in the UncertWeb project, which has built concepts and tools to realize the uncertainty-enabled model web. Some of the tools and protocols will be shown and evaluated in this presentation. For the uncertainty of exposure, the uncertainty of air quality was more important than the uncertainty of peoples locations. This difference was stronger for PM10 than for NO2. The workflow was implemented as generic Web services in UncertWeb that also allow for other inputs than the simulated activity schedules and air quality with other resolution. However, due to this flexibility, the Web services require standardized formats and the overlay algorithm is not optimized for the specific use case resulting in a data and processing overhead. Hence, we implemented the full analysis in parallel in R, for this specific case as the model web solution had difficulties with massive data.

Target Uncertainty Mediates Sensorimotor Error Correction.

PubMed

Acerbi, Luigi; Vijayakumar, Sethu; Wolpert, Daniel M

2017-01-01

Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated.

Towards national-scale greenhouse gas emissions evaluation with robust uncertainty estimates

NASA Astrophysics Data System (ADS)

Rigby, Matthew; Swallow, Ben; Lunt, Mark; Manning, Alistair; Ganesan, Anita; Stavert, Ann; Stanley, Kieran; O'Doherty, Simon

2016-04-01

Through the Deriving Emissions related to Climate Change (DECC) network and the Greenhouse gAs Uk and Global Emissions (GAUGE) programme, the UK's greenhouse gases are now monitored by instruments mounted on telecommunications towers and churches, on a ferry that performs regular transects of the North Sea, on-board a research aircraft and from space. When combined with information from high-resolution chemical transport models such as the Met Office Numerical Atmospheric dispersion Modelling Environment (NAME), these measurements are allowing us to evaluate emissions more accurately than has previously been possible. However, it has long been appreciated that current methods for quantifying fluxes using atmospheric data suffer from uncertainties, primarily relating to the chemical transport model, that have been largely ignored to date. Here, we use novel model reduction techniques for quantifying the influence of a set of potential systematic model errors on the outcome of a national-scale inversion. This new technique has been incorporated into a hierarchical Bayesian framework, which can be shown to reduce the influence of subjective choices on the outcome of inverse modelling studies. Using estimates of the UK's methane emissions derived from DECC and GAUGE tall-tower measurements as a case study, we will show that such model systematic errors have the potential to significantly increase the uncertainty on national-scale emissions estimates. Therefore, we conclude that these factors must be incorporated in national emissions evaluation efforts, if they are to be credible.

Uncertainty and sensitivity assessment of flood risk assessments

NASA Astrophysics Data System (ADS)

de Moel, H.; Aerts, J. C.

2009-12-01

uncertainties of the final risk estimate will be helpful to decision makers to make better informed decisions and attributing this uncertainty to the input parameters helps to identify which parameters are most important when it comes to uncertainty in the final estimate and should therefore deserve additional attention in further research.

Uncertainty Assessment of Gaseous Oxidized Mercury Measurements Collected by Atmospheric Mercury Network.

PubMed

Cheng, Irene; Zhang, Leiming

2017-01-17

Gaseous oxidized mercury (GOM) measurement uncertainties undoubtedly impact the understanding of mercury biogeochemical cycling; however, there is a lack of consensus on the uncertainty magnitude. The numerical method presented in this study provides an alternative means of estimating the uncertainties of previous GOM measurements. Weekly GOM in ambient air was predicted from measured weekly mercury wet deposition using a scavenging ratio approach, and compared against field measurements of 2-4 hly GOM to estimate the measurement biases of the Tekran speciation instruments at 13 Atmospheric Mercury Network (AMNet) sites. Multiyear average GOM measurements were estimated to be biased low by more than a factor of 2 at six sites, between a factor of 1.5 and 1.8 at six other sites, and below a factor of 1.3 at one site. The differences between predicted and observed were significantly larger during summer than other seasons potentially because of higher ozone concentrations that may interfere with GOM sampling. The analysis data collected over six years at multiple sites suggests a systematic bias in GOM measurements, supporting the need for further investigation of measurement technologies and identifying the chemical composition of GOM.

The Global Aerosol Synthesis and Science Project (GASSP): Measurements and Modeling to Reduce Uncertainty

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

Reddington, C. L.; Carslaw, K. S.; Stier, P.

The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, tomore » create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.« less

The Global Aerosol Synthesis and Science Project (GASSP): Measurements and Modeling to Reduce Uncertainty

DOE PAGES

Reddington, C. L.; Carslaw, K. S.; Stier, P.; ...

2017-09-01

The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, tomore » create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.« less

Information Seeking in Uncertainty Management Theory: Exposure to Information About Medical Uncertainty and Information-Processing Orientation as Predictors of Uncertainty Management Success.

PubMed

Rains, Stephen A; Tukachinsky, Riva

2015-01-01

Uncertainty management theory outlines the processes through which individuals cope with health-related uncertainty. Information seeking has been frequently documented as an important uncertainty management strategy. The reported study investigates exposure to specific types of medical information during a search, and one's information-processing orientation as predictors of successful uncertainty management (i.e., a reduction in the discrepancy between the level of uncertainty one feels and the level one desires). A lab study was conducted in which participants were primed to feel more or less certain about skin cancer and then were allowed to search the World Wide Web for skin cancer information. Participants' search behavior was recorded and content analyzed. The results indicate that exposure to two health communication constructs that pervade medical forms of uncertainty (i.e., severity and susceptibility) and information-processing orientation predicted uncertainty management success.

Conditional uncertainty principle

NASA Astrophysics Data System (ADS)

Gour, Gilad; Grudka, Andrzej; Horodecki, Michał; Kłobus, Waldemar; Łodyga, Justyna; Narasimhachar, Varun

2018-04-01

We develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. Our formalism is built upon a mathematical relation which we call conditional majorization. We define conditional majorization and, for the case of classical memory, we provide its thorough characterization in terms of monotones, i.e., functions that preserve the partial order under conditional majorization. We demonstrate the application of this framework by deriving two types of memory-assisted uncertainty relations, (1) a monotone-based conditional uncertainty relation and (2) a universal measure-independent conditional uncertainty relation, both of which set a lower bound on the minimal uncertainty that Bob has about Alice's pair of incompatible measurements, conditioned on arbitrary measurement that Bob makes on his own system. We next compare the obtained relations with their existing entropic counterparts and find that they are at least independent.

High-voltage measurements on the 5 ppm relative uncertainty level with collinear laser spectroscopy

NASA Astrophysics Data System (ADS)

Krämer, J.; König, K.; Geppert, Ch; Imgram, P.; Maaß, B.; Meisner, J.; Otten, E. W.; Passon, S.; Ratajczyk, T.; Ullmann, J.; Nörtershäuser, W.

2018-04-01

We present the results of high-voltage collinear laser spectroscopy measurements on the 5 ppm relative uncertainty level using a pump and probe scheme at the 4s ^2S1/2 → 4p ^2P3/2 transition of {\\hspace{0pt}}40Ca+ involving the 3d ^2D5/2 metastable state. With two-stage laser interaction and a reference measurement we can eliminate systematic effects such as differences in the contact potentials due to different electrode materials and thermoelectric voltages, and the unknown starting potential of the ions in the ion source. Voltage measurements were performed between  -5 kV and  -19 kV and parallel measurements with stable high-voltage dividers calibrated to 5 ppm relative uncertainty were used as a reference. Our measurements are compatible with the uncertainty limits of the high-voltage dividers and demonstrate an unprecedented (factor of 20) increase in the precision of direct laser-based high-voltage measurements.

Conservation planning under uncertainty in urban development and vegetation dynamics

PubMed Central

Carmel, Yohay

2018-01-01

Systematic conservation planning is a framework for optimally locating and prioritizing areas for conservation. An often-noted shortcoming of most conservation planning studies is that they do not address future uncertainty. The selection of protected areas that are intended to ensure the long-term persistence of biodiversity is often based on a snapshot of the current situation, ignoring processes such as climate change. Scenarios, in the sense of being accounts of plausible futures, can be utilized to identify conservation area portfolios that are robust to future uncertainty. We compared three approaches for utilizing scenarios in conservation area selection: considering a full set of scenarios (all-scenarios portfolio), assuming the realization of specific scenarios, and a reference strategy based on the current situation (current distributions portfolio). Our objective was to compare the robustness of these approaches in terms of their relative performance across future scenarios. We focused on breeding bird species in Israel’s Mediterranean region. We simulated urban development and vegetation dynamics scenarios 60 years into the future using DINAMICA-EGO, a cellular-automata simulation model. For each scenario, we mapped the target species’ available habitat distribution, identified conservation priority areas using the site-selection software MARXAN, and constructed conservation area portfolios using the three aforementioned strategies. We then assessed portfolio performance based on the number of species for which representation targets were met in each scenario. The all-scenarios portfolio consistently outperformed the other portfolios, and was more robust to ‘errors’ (e.g., when an assumed specific scenario did not occur). On average, the all-scenarios portfolio achieved representation targets for five additional species compared with the current distributions portfolio (approximately 33 versus 28 species). Our findings highlight the importance

Conservation planning under uncertainty in urban development and vegetation dynamics.

PubMed

Troupin, David; Carmel, Yohay

2018-01-01

Systematic conservation planning is a framework for optimally locating and prioritizing areas for conservation. An often-noted shortcoming of most conservation planning studies is that they do not address future uncertainty. The selection of protected areas that are intended to ensure the long-term persistence of biodiversity is often based on a snapshot of the current situation, ignoring processes such as climate change. Scenarios, in the sense of being accounts of plausible futures, can be utilized to identify conservation area portfolios that are robust to future uncertainty. We compared three approaches for utilizing scenarios in conservation area selection: considering a full set of scenarios (all-scenarios portfolio), assuming the realization of specific scenarios, and a reference strategy based on the current situation (current distributions portfolio). Our objective was to compare the robustness of these approaches in terms of their relative performance across future scenarios. We focused on breeding bird species in Israel's Mediterranean region. We simulated urban development and vegetation dynamics scenarios 60 years into the future using DINAMICA-EGO, a cellular-automata simulation model. For each scenario, we mapped the target species' available habitat distribution, identified conservation priority areas using the site-selection software MARXAN, and constructed conservation area portfolios using the three aforementioned strategies. We then assessed portfolio performance based on the number of species for which representation targets were met in each scenario. The all-scenarios portfolio consistently outperformed the other portfolios, and was more robust to 'errors' (e.g., when an assumed specific scenario did not occur). On average, the all-scenarios portfolio achieved representation targets for five additional species compared with the current distributions portfolio (approximately 33 versus 28 species). Our findings highlight the importance of

Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

NASA Astrophysics Data System (ADS)

Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven

2016-05-01

Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.

Sources of Uncertainty and the Interpretation of Short-Term Fluctuations

NASA Astrophysics Data System (ADS)

Lewandowsky, S.; Risbey, J.; Cowtan, K.; Rahmstorf, S.

2016-12-01

The alleged significant slowdown in global warming during the first decade of the 21st century, and the appearance of a discrepancy between models and observations, has attracted considerable research attention. We trace the history of this research and show how its conclusions were shaped by several sources of uncertainty and ambiguity about models and observations. We show that as those sources of uncertainty were gradually eliminated by further research, insufficient evidence remained to infer any discrepancy between models and observations or a significant slowing of warming. Specifically, we show that early research had to contend with uncertainties about coverage biases in the global temperature record and biases in the sea surface temperature observations which turned out to have exaggerated the extent of slowing. In addition, uncertainties in the observed forcings were found to have exaggerated the mismatch between models and observations. Further sources of uncertainty that were ultimately eliminated involved the use of incommensurate sea surface temperature data between models and observations and a tacit interpretation of model projections as predictions or forecasts. After all those sources of uncertainty were eliminated, the most recent research finds little evidence for an unusual slowdown or a discrepancy between models and observations. We discuss whether these different kinds of uncertainty could have been anticipated or managed differently, and how one can apply those lessons to future short-term fluctuations in warming.

Uncertainty and Cognitive Control

PubMed Central

Mushtaq, Faisal; Bland, Amy R.; Schaefer, Alexandre

2011-01-01

A growing trend of neuroimaging, behavioral, and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1) There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2) There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3) The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the “need for control”; (4) Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders. PMID:22007181

Examples of Communicating Uncertainty Applied to Earthquake Hazard and Risk Products

NASA Astrophysics Data System (ADS)

Wald, D. J.

2013-12-01

facilitating rapid and proportionate earthquake response. For uncertainty representation, PAGER employs an Earthquake Impact Scale (EIS) that provides simple alerting thresholds, derived from systematic analyses of past earthquake impact and response levels. The alert levels are characterized by alerts of green (little or no impact), yellow (regional impact and response), orange (national-scale impact and response), and red (major disaster, necessitating international response). We made a conscious attempt at both simple and intuitive color-coded alerting criterion; yet, we preserve the necessary uncertainty measures (with simple histograms) by which one can gauge the likelihood for the alert to be over- or underestimated. In these hazard and loss modeling examples, both products are widely used across a range of technical as well as general audiences. Ironically, ShakeMap uncertainties--rigorously reported and portrayed for the primarily scientific portion of the audience--are rarely employed and are routinely misunderstood; for PAGER, uncertainties aimed at a wider user audience seem to be more easily digested. We discuss how differences in the way these uncertainties are portrayed may play into their acceptance and uptake, or lack thereof.

Fundamental uncertainty limit of optical flow velocimetry according to Heisenberg's uncertainty principle.

PubMed

Fischer, Andreas

2016-11-01

Optical flow velocity measurements are important for understanding the complex behavior of flows. Although a huge variety of methods exist, they are either based on a Doppler or a time-of-flight measurement principle. Doppler velocimetry evaluates the velocity-dependent frequency shift of light scattered at a moving particle, whereas time-of-flight velocimetry evaluates the traveled distance of a scattering particle per time interval. Regarding the aim of achieving a minimal measurement uncertainty, it is unclear if one principle allows to achieve lower uncertainties or if both principles can achieve equal uncertainties. For this reason, the natural, fundamental uncertainty limit according to Heisenberg's uncertainty principle is derived for Doppler and time-of-flight measurement principles, respectively. The obtained limits of the velocity uncertainty are qualitatively identical showing, e.g., a direct proportionality for the absolute value of the velocity to the power of 32 and an indirect proportionality to the square root of the scattered light power. Hence, both measurement principles have identical potentials regarding the fundamental uncertainty limit due to the quantum mechanical behavior of photons. This fundamental limit can be attained (at least asymptotically) in reality either with Doppler or time-of-flight methods, because the respective Cramér-Rao bounds for dominating photon shot noise, which is modeled as white Poissonian noise, are identical with the conclusions from Heisenberg's uncertainty principle.

Uncertainties in internal gas counting

NASA Astrophysics Data System (ADS)

Unterweger, M.; Johansson, L.; Karam, L.; Rodrigues, M.; Yunoki, A.

2015-06-01

The uncertainties in internal gas counting will be broken down into counting uncertainties and gas handling uncertainties. Counting statistics, spectrum analysis, and electronic uncertainties will be discussed with respect to the actual counting of the activity. The effects of the gas handling and quantities of counting and sample gases on the uncertainty in the determination of the activity will be included when describing the uncertainties arising in the sample preparation.

Treatment of uncertainties in atmospheric chemical systems: A combined modeling and experimental approach

NASA Astrophysics Data System (ADS)

Pun, Betty Kong-Ling

1998-12-01

Uncertainty is endemic in modeling. This thesis is a two- phase program to understand the uncertainties in urban air pollution model predictions and in field data used to validate them. Part I demonstrates how to improve atmospheric models by analyzing the uncertainties in these models and using the results to guide new experimentation endeavors. Part II presents an experiment designed to characterize atmospheric fluctuations, which have significant implications towards the model validation process. A systematic study was undertaken to investigate the effects of uncertainties in the SAPRC mechanism for gas- phase chemistry in polluted atmospheres. The uncertainties of more than 500 parameters were compiled, including reaction rate constants, product coefficients, organic composition, and initial conditions. Uncertainty propagation using the Deterministic Equivalent Modeling Method (DEMM) revealed that the uncertainties in ozone predictions can be up to 45% based on these parametric uncertainties. The key parameters found to dominate the uncertainties of the predictions include photolysis rates of NO2, O3, and formaldehyde; the rate constant for nitric acid formation; and initial amounts of NOx and VOC. Similar uncertainty analysis procedures applied to two other mechanisms used in regional air quality models led to the conclusion that in the presence of parametric uncertainties, the mechanisms cannot be discriminated. Research efforts should focus on reducing parametric uncertainties in photolysis rates, reaction rate constants, and source terms. A new tunable diode laser (TDL) infrared spectrometer was designed and constructed to measure multiple pollutants simultaneously in the same ambient air parcels. The sensitivities of the one hertz measurements were 2 ppb for ozone, 1 ppb for NO, and 0.5 ppb for NO2. Meteorological data were also collected for wind, temperature, and UV intensity. The field data showed clear correlations between ozone, NO, and NO2 in the one

GCR Environmental Models III: GCR Model Validation and Propagated Uncertainties in Effective Dose

NASA Technical Reports Server (NTRS)

Slaba, Tony C.; Xu, Xiaojing; Blattnig, Steve R.; Norman, Ryan B.

2014-01-01

This is the last of three papers focused on quantifying the uncertainty associated with galactic cosmic rays (GCR) models used for space radiation shielding applications. In the first paper, it was found that GCR ions with Z>2 and boundary energy below 500 MeV/nucleon induce less than 5% of the total effective dose behind shielding. This is an important finding since GCR model development and validation have been heavily biased toward Advanced Composition Explorer/Cosmic Ray Isotope Spectrometer measurements below 500 MeV/nucleon. Weights were also developed that quantify the relative contribution of defined GCR energy and charge groups to effective dose behind shielding. In the second paper, it was shown that these weights could be used to efficiently propagate GCR model uncertainties into effective dose behind shielding. In this work, uncertainties are quantified for a few commonly used GCR models. A validation metric is developed that accounts for measurements uncertainty, and the metric is coupled to the fast uncertainty propagation method. For this work, the Badhwar-O'Neill (BON) 2010 and 2011 and the Matthia GCR models are compared to an extensive measurement database. It is shown that BON2011 systematically overestimates heavy ion fluxes in the range 0.5-4 GeV/nucleon. The BON2010 and BON2011 also show moderate and large errors in reproducing past solar activity near the 2000 solar maximum and 2010 solar minimum. It is found that all three models induce relative errors in effective dose in the interval [-20%, 20%] at a 68% confidence level. The BON2010 and Matthia models are found to have similar overall uncertainty estimates and are preferred for space radiation shielding applications.

Uncertainty in BMP evaluation and optimization for watershed management

NASA Astrophysics Data System (ADS)

Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

2012-12-01

Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT

Influence of uncertainty on framed decision-making with moral dilemma.

PubMed

Merlhiot, Gaëtan; Mermillod, Martial; Le Pennec, Jean-Luc; Dutheil, Frédéric; Mondillon, Laurie

2018-01-01

In cases of impending natural disasters, most events are uncertain and emotionally relevant, both critical factors for decision-making. Moreover, for exposed individuals, the sensitivity to the framing of the consequences (gain or loss) and the moral judgments they have to perform (e.g., evacuate or help an injured person) constitute two central effects that have never been examined in the same context of decision-making. In a framed decision-making task with moral dilemma, we investigated whether uncertainty (i.e., unpredictably of events) and a threatening context would influence the framing effect (actions framed in loss are avoided in comparison to the ones framed in gain) and the personal intention effect (unintentional actions are more morally acceptable in comparison to intentional actions) on the perceived moral acceptability of taking action. Considering the impact of uncertainty and fear on the processes underlying these effects, we assumed that these emotions would lead to the negation of the two effects. Our results indicate that the exposure to uncertain events leads to the negation of the framing effect, but does not influence the moral acceptability and the effect of personal intention. We discuss our results in the light of dual-process models (i.e. systematic vs. heuristic), appraisal theories, and neurocognitive aspects. These elements highlight the importance of providing solutions to cope with uncertainty, both for scientists and local populations exposed to natural hazards.

Uncertainty evaluation of mass values determined by electronic balances in analytical chemistry: a new method to correct for air buoyancy.

PubMed

Wunderli, S; Fortunato, G; Reichmuth, A; Richard, Ph

2003-06-01

A new method to correct for the largest systematic influence in mass determination-air buoyancy-is outlined. A full description of the most relevant influence parameters is given and the combined measurement uncertainty is evaluated according to the ISO-GUM approach [1]. A new correction method for air buoyancy using an artefact is presented. This method has the advantage that only a mass artefact is used to correct for air buoyancy. The classical approach demands the determination of the air density and therefore suitable equipment to measure at least the air temperature, the air pressure and the relative air humidity within the demanded uncertainties (i.e. three independent measurement tasks have to be performed simultaneously). The calculated uncertainty is lower for the classical method. However a field laboratory may not always be in possession of fully traceable measurement systems for these room climatic parameters.A comparison of three approaches applied to the calculation of the combined uncertainty of mass values is presented. Namely the classical determination of air buoyancy, the artefact method, and the neglecting of this systematic effect as proposed in the new EURACHEM/CITAC guide [2]. The artefact method is suitable for high-precision measurement in analytical chemistry and especially for the production of certified reference materials, reference values and analytical chemical reference materials. The method could also be used either for volume determination of solids or for air density measurement by an independent method.

Impacts of uncertainties in weather and streamflow observations in calibration and evaluation of an elevation distributed HBV-model

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.; Petersen-Øverleir, A.; Johansen, S.

2012-04-01

The aim of this study is to assess the uncertainties in streamflow simulations when uncertainties in both observed inputs (precipitation and temperature) and streamflow observations used in the calibration of the hydrological model are explicitly accounted for. To achieve this goal we applied the elevation distributed HBV model operating on daily time steps to a small catchment in high elevation in Southern Norway where the seasonal snow cover is important. The uncertainties in precipitation inputs were quantified using conditional simulation. This procedure accounts for the uncertainty related to the density of the precipitation network, but neglects uncertainties related to measurement bias/errors and eventual elevation gradients in precipitation. The uncertainties in temperature inputs were quantified using a Bayesian temperature interpolation procedure where the temperature lapse rate is re-estimated every day. The uncertainty in the lapse rate was accounted for whereas the sampling uncertainty related to network density was neglected. For every day a random sample of precipitation and temperature inputs were drawn to be applied as inputs to the hydrologic model. The uncertainties in observed streamflow were assessed based on the uncertainties in the rating curve model. A Bayesian procedure was applied to estimate the probability for rating curve models with 1 to 3 segments and the uncertainties in their parameters. This method neglects uncertainties related to errors in observed water levels. Note that one rating curve was drawn to make one realisation of a whole time series of streamflow, thus the rating curve errors lead to a systematic bias in the streamflow observations. All these uncertainty sources were linked together in both calibration and evaluation of the hydrologic model using a DREAM based MCMC routine. Effects of having less information (e.g. missing one streamflow measurement for defining the rating curve or missing one precipitation station

Stationarity is undead: Uncertainty dominates the distribution of extremes

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.

2015-03-01

The increasing effort to develop and apply nonstationary models in hydrologic frequency analyses under changing environmental conditions can be frustrated when the additional uncertainty related to the model complexity is accounted for along with the sampling uncertainty. In order to show the practical implications and possible problems of using nonstationary models and provide critical guidelines, in this study we review the main tools developed in this field (such as nonstationary distribution functions, return periods, and risk of failure) highlighting advantages and disadvantages. The discussion is supported by three case studies that revise three illustrative examples reported in the scientific and technical literature referring to the Little Sugar Creek (at Charlotte, North Carolina), Red River of the North (North Dakota/Minnesota), and the Assunpink Creek (at Trenton, New Jersey). The uncertainty of the results is assessed by complementing point estimates with confidence intervals (CIs) and emphasizing critical aspects such as the subjectivity affecting the choice of the models' structure. Our results show that (1) nonstationary frequency analyses should not only be based on at-site time series but require additional information and detailed exploratory data analyses (EDA); (2) as nonstationary models imply that the time-varying model structure holds true for the entire future design life period, an appropriate modeling strategy requires that EDA identifies a well-defined deterministic mechanism leading the examined process; (3) when the model structure cannot be inferred in a deductive manner and nonstationary models are fitted by inductive inference, model structure introduces an additional source of uncertainty so that the resulting nonstationary models can provide no practical enhancement of the credibility and accuracy of the predicted extreme quantiles, whereas possible model misspecification can easily lead to physically inconsistent results; (4) when

Defense Resource Planning Under Uncertainty: An Application of Robust Decision Making to Munitions Mix Planning

DTIC Science & Technology

2016-02-01

In addition , the parser updates some parameters based on uncertainties. For example, Analytica was very slow to update Pk values based on...moderate range. The additional security environments helped to fill gaps in lower severity. Weapons Effectiveness Pk values were modified to account for two...project is to help improve the value and character of defense resource planning in an era of growing uncertainty and complex strategic challenges

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations.

PubMed

van Horen, Femke; Mussweiler, Thomas

2015-09-16

Human beings are constantly surrounded by uncertainty and change. The question arises how people cope with such uncertainty. To date, most research has focused on the cognitive strategies people adopt to deal with uncertainty. However, especially when uncertainty is due to unpredictable societal events (e.g., economical crises, political revolutions, terrorism threats) of which one is unable to judge the impact on one's future live, cognitive strategies (like seeking additional information) is likely to fail to combat uncertainty. Instead, the current paper discusses a method demonstrating that people might deal with uncertainty experientially through soft haptic sensations. More specifically, because touching something soft creates a feeling of comfort and security, people prefer objects with softer as compared to harder properties when feeling uncertain. Seeking for softness is a highly efficient and effective tool to deal with uncertainty as our hands are available at all times. This protocol describes a set of methods demonstrating 1) how environmental (un)certainty can be situationally activated with an experiential priming procedure, 2) that the quality of the softness experience (what type of softness and how it is experienced) matters and 3) how uncertainty can be reduced using different methods.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties.

PubMed

Baysal, U; Eyüboğlu, B M

2000-08-01

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Sensitivity to Uncertainty in Asteroid Impact Risk Assessment

NASA Astrophysics Data System (ADS)

Mathias, D.; Wheeler, L.; Prabhu, D. K.; Aftosmis, M.; Dotson, J.; Robertson, D. K.

2015-12-01

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center is developing a physics-based impact risk model for probabilistically assessing threats from potential asteroid impacts on Earth. The model integrates probabilistic sampling of asteroid parameter ranges with physics-based analyses of entry, breakup, and impact to estimate damage areas and casualties from various impact scenarios. Assessing these threats is a highly coupled, dynamic problem involving significant uncertainties in the range of expected asteroid characteristics, how those characteristics may affect the level of damage, and the fidelity of various modeling approaches and assumptions. The presented model is used to explore the sensitivity of impact risk estimates to these uncertainties in order to gain insight into what additional data or modeling refinements are most important for producing effective, meaningful risk assessments. In the extreme cases of very small or very large impacts, the results are generally insensitive to many of the characterization and modeling assumptions. However, the nature of the sensitivity can change across moderate-sized impacts. Results will focus on the value of additional information in this critical, mid-size range, and how this additional data can support more robust mitigation decisions.

Impact of searching clinical trial registries in systematic reviews of pharmaceutical treatments: methodological systematic review and reanalysis of meta-analyses.

PubMed

Baudard, Marie; Yavchitz, Amélie; Ravaud, Philippe; Perrodeau, Elodie; Boutron, Isabelle

2017-02-17

Objective  To evaluate the impact of searching clinical trial registries in systematic reviews. Design  Methodological systematic review and reanalyses of meta-analyses. Data sources  Medline was searched to identify systematic reviews of randomised controlled trials (RCTs) assessing pharmaceutical treatments published between June 2014 and January 2015. For all systematic reviews that did not report a trial registry search but reported the information to perform it, the World Health Organization International Trials Registry Platform (WHO ICTRP search portal) was searched for completed or terminated RCTs not originally included in the systematic review. Data extraction  For each systematic review, two researchers independently extracted the outcomes analysed, the number of patients included, and the treatment effect estimated. For each RCT identified, two researchers independently determined whether the results were available (ie, posted, published, or available on the sponsor website) and extracted the data. When additional data were retrieved, we reanalysed meta-analyses and calculated the weight of the additional RCTs and the change in summary statistics by comparison with the original meta-analysis. Results  Among 223 selected systematic reviews, 116 (52%) did not report a search of trial registries; 21 of these did not report the information to perform the search (key words, search date). A search was performed for 95 systematic reviews; for 54 (57%), no additional RCTs were found and for 41 (43%) 122 additional RCTs were identified. The search allowed for increasing the number of patients by more than 10% in 19 systematic reviews, 20% in 10, 30% in seven, and 50% in four. Moreover, 63 RCTs had results available; the results for 45 could be included in a meta-analysis. 14 systematic reviews including 45 RCTs were reanalysed. The weight of the additional RCTs in the recalculated meta-analyses ranged from 0% to 58% and was greater than 10% in five of 14

A multicenter study to quantify systematic variations and associated uncertainties in source positioning with commonly used HDR afterloaders and ring applicators for the treatment of cervical carcinomas

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

Awunor, O., E-mail: onuora.awunor@stees.nhs.uk; Berger, D.; Kirisits, C.

Purpose: The reconstruction of radiation source position in the treatment planning system is a key part of the applicator reconstruction process in high dose rate (HDR) brachytherapy treatment of cervical carcinomas. The steep dose gradients, of as much as 12%/mm, associated with typical cervix treatments emphasize the importance of accurate and precise determination of source positions. However, a variety of methodologies with a range in associated measurement uncertainties, of up to ±2.5 mm, are currently employed by various centers to do this. In addition, a recent pilot study by Awunor et al. [“Direct reconstruction and associated uncertainties of {sup 192}Irmore » source dwell positions in ring applicators using gafchromic film in the treatment planning of HDR brachytherapy cervix patients,” Phys. Med. Biol. 58, 3207–3225 (2013)] reported source positional differences of up to 2.6 mm between ring sets of the same type and geometry. This suggests a need for a comprehensive study to assess and quantify systematic source position variations between commonly used ring applicators and HDR afterloaders across multiple centers. Methods: Eighty-six rings from 20 European brachytherapy centers were audited in the form of a postal audit with each center collecting the data independently. The data were collected by setting up the rings using a bespoke jig and irradiating gafchromic films at predetermined dwell positions using four afterloader types, MicroSelectron, Flexitron, GammaMed, and MultiSource, from three manufacturers, Nucletron, Varian, and Eckert & Ziegler BEBIG. Five different ring types in six sizes (Ø25–Ø35 mm) and two angles (45° and 60°) were used. Coordinates of irradiated positions relative to the ring center were determined and collated, and source position differences quantified by ring type, size, and angle. Results: The mean expanded measurement uncertainty (k = 2) along the direction of source travel was ±1.4 mm. The standard

Comprehension of Randomization and Uncertainty in Cancer Clinical Trials Decision Making Among Rural, Appalachian Patients.

PubMed

Krieger, Janice L; Palmer-Wackerly, Angela; Dailey, Phokeng M; Krok-Schoen, Jessica L; Schoenberg, Nancy E; Paskett, Electra D

2015-12-01

Comprehension of randomization is a vital, but understudied, component of informed consent to participate in cancer randomized clinical trials (RCTs). This study examines patient comprehension of the randomization process as well as sources of ongoing uncertainty that may inhibit a patient's ability to provide informed consent to participate in RCTs. Cancer patients living in rural Appalachia who were offered an opportunity to participate in a cancer treatment RCT completed in-depth interviews and a brief survey. No systematic differences in randomization comprehension between patients who consented and those who declined participation in a cancer RCT were detected. Comprehension is conceptually distinct from uncertainty, with patients who had both high and low comprehension experiencing randomization-related uncertainty. Uncertainty about randomization was found to have cognitive and affective dimensions. Not all patients enrolling in RCTs have a sufficient understanding of the randomization process to provide informed consent. Healthcare providers need to be aware of the different types of randomization-related uncertainty. Efforts to improve informed consent to participate in RCTs should focus on having patients teach back their understanding of randomization. This practice could yield valuable information about the patient's cognitive and affective understanding of randomization as well as opportunities to correct misperceptions. Education about RCTs should reflect patient expectations of individualized care by explaining how all treatments being compared are appropriate to the specifics of a patient's disease.

Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

NASA Astrophysics Data System (ADS)

Engel, Dave W.; Reichardt, Thomas A.; Kulp, Thomas J.; Graff, David L.; Thompson, Sandra E.

2016-05-01

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.

Hierarchical Multi-Scale Approach To Validation and Uncertainty Quantification of Hyper-Spectral Image Modeling

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

Engel, David W.; Reichardt, Thomas A.; Kulp, Thomas J.

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensormore » level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.« less

Target Uncertainty Mediates Sensorimotor Error Correction

PubMed Central

Vijayakumar, Sethu; Wolpert, Daniel M.

2017-01-01

Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects’ scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one’s response. By suggesting that subjects’ decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated. PMID:28129323

Characterizing sources of uncertainty from global climate models and downscaling techniques

USGS Publications Warehouse

Wootten, Adrienne; Terando, Adam; Reich, Brian J.; Boyles, Ryan; Semazzi, Fred

2017-01-01

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example

NASA Astrophysics Data System (ADS)

Andres, Robert J.; Boden, Thomas A.; Higdon, David M.

2016-12-01

Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These proxies and distribution schemes introduce additional uncertainty into these maps. This paper examines the uncertainty associated with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughout this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty. The results of the uncertainty analysis reveal a range of 4-190 %, with an average of 120 % (2σ) for populated and FFCO2-emitting grid spaces over annual timescales. This paper also describes a methodological change specific to the creation of the Carbon Dioxide Information Analysis Center (CDIAC) FFCO2 emission maps: the change from a temporally fixed population proxy to a temporally varying population proxy.

Reducing Spatial Uncertainty Through Attentional Cueing Improves Contrast Sensitivity in Regions of the Visual Field With Glaucomatous Defects

PubMed Central

Phu, Jack; Kalloniatis, Michael; Khuu, Sieu K.

2018-01-01

Purpose Current clinical perimetric test paradigms present stimuli randomly to various locations across the visual field (VF), inherently introducing spatial uncertainty, which reduces contrast sensitivity. In the present study, we determined the extent to which spatial uncertainty affects contrast sensitivity in glaucoma patients by minimizing spatial uncertainty through attentional cueing. Methods Six patients with open-angle glaucoma and six healthy subjects underwent laboratory-based psychophysical testing to measure contrast sensitivity at preselected locations at two eccentricities (9.5° and 17.5°) with two stimulus sizes (Goldmann sizes III and V) under different cueing conditions: 1, 2, 4, or 8 points verbally cued. Method of Constant Stimuli and a single-interval forced-choice procedure were used to generate frequency of seeing (FOS) curves at locations with and without VF defects. Results At locations with VF defects, cueing minimizes spatial uncertainty and improves sensitivity under all conditions. The effect of cueing was maximal when one point was cued, and rapidly diminished when more points were cued (no change to baseline with 8 points cued). The slope of the FOS curve steepened with reduced spatial uncertainty. Locations with normal sensitivity in glaucomatous eyes had similar performance to that of healthy subjects. There was a systematic increase in uncertainty with the depth of VF loss. Conclusions Sensitivity measurements across the VF are negatively affected by spatial uncertainty, which increases with greater VF loss. Minimizing uncertainty can improve sensitivity at locations of deficit. Translational Relevance Current perimetric techniques introduce spatial uncertainty and may therefore underestimate sensitivity in regions of VF loss. PMID:29600116

The Inherent Uncertainty of In-Situ Observations and its Implications for Modeling Evapotranspiration

NASA Astrophysics Data System (ADS)

Alfieri, J. G.

2012-12-01

In-situ observations are essential to a broad range of applications including the development, calibration, and validation of both the numerical and remote sensing-based models. For example, observational data is requisite in order to evaluate the skill of these models both to represent the complex biogeophysical processes regulating evapotranspiration (ET) and to predict the magnitude of the moisture flux. As such, by propagating into these subsequent activities, any uncertainty or errors associated with the observational data have the potential to adversely impact the accuracy and utility of these models. It is, therefore, critical that the factors driving measurement uncertainty are fully understood so that the steps can be taken to account for its effects and mitigate its impact on subsequent analyses. Field measurements of ET can be collected using a variety of techniques including eddy covariance (EC), lysimetry (LY), and scintillometry (SC). Each of these methods is underpinned by a unique set of theoretical considerations and practical constraints; and, as a result, each method is susceptible to differing types of systematic and random error. Since the uncertainty associated with the field measurements is predicated on how well numerous factors - for example, environmental conditions - adhere to those prescribed by the underlying assumptions, the quality of in-situ observations collected via the differing methods can vary significantly both over time and from site-to-site. Using data from both site studies and large field campaigns, such as IHOP_2002 and BEAREX08, the sources of uncertainty in field observations will be discussed. The impact of measurement uncertainty on model validation will also be illustrated.

Collocation mismatch uncertainties in satellite aerosol retrieval validation

NASA Astrophysics Data System (ADS)

Virtanen, Timo H.; Kolmonen, Pekka; Sogacheva, Larisa; Rodríguez, Edith; Saponaro, Giulia; de Leeuw, Gerrit

2018-02-01

Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (˜ 0.5°) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the

Decision-Making under Criteria Uncertainty

NASA Astrophysics Data System (ADS)

Kureychik, V. M.; Safronenkova, I. B.

2018-05-01

Uncertainty is an essential part of a decision-making procedure. The paper deals with the problem of decision-making under criteria uncertainty. In this context, decision-making under uncertainty, types and conditions of uncertainty were examined. The decision-making problem under uncertainty was formalized. A modification of the mathematical decision support method under uncertainty via ontologies was proposed. A critical distinction of the developed method is ontology usage as its base elements. The goal of this work is a development of a decision-making method under criteria uncertainty with the use of ontologies in the area of multilayer board designing. This method is oriented to improvement of technical-economic values of the examined domain.

Embracing uncertainty in applied ecology.

PubMed

Milner-Gulland, E J; Shea, K

2017-12-01

Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.

Water Table Uncertainties due to Uncertainties in Structure and Properties of an Unconfined Aquifer.

PubMed

Hauser, Juerg; Wellmann, Florian; Trefry, Mike

2018-03-01

We consider two sources of geology-related uncertainty in making predictions of the steady-state water table elevation for an unconfined aquifer. That is the uncertainty in the depth to base of the aquifer and in the hydraulic conductivity distribution within the aquifer. Stochastic approaches to hydrological modeling commonly use geostatistical techniques to account for hydraulic conductivity uncertainty within the aquifer. In the absence of well data allowing derivation of a relationship between geophysical and hydrological parameters, the use of geophysical data is often limited to constraining the structural boundaries. If we recover the base of an unconfined aquifer from an analysis of geophysical data, then the associated uncertainties are a consequence of the geophysical inversion process. In this study, we illustrate this by quantifying water table uncertainties for the unconfined aquifer formed by the paleochannel network around the Kintyre Uranium deposit in Western Australia. The focus of the Bayesian parametric bootstrap approach employed for the inversion of the available airborne electromagnetic data is the recovery of the base of the paleochannel network and the associated uncertainties. This allows us to then quantify the associated influences on the water table in a conceptualized groundwater usage scenario and compare the resulting uncertainties with uncertainties due to an uncertain hydraulic conductivity distribution within the aquifer. Our modeling shows that neither uncertainties in the depth to the base of the aquifer nor hydraulic conductivity uncertainties alone can capture the patterns of uncertainty in the water table that emerge when the two are combined. © 2017, National Ground Water Association.

A Unified Approach for Reporting ARM Measurement Uncertainties Technical Report: Updated in 2016

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

Sisterson, Douglas

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is observationally based, and quantifying the uncertainty of its measurements is critically important. With over 300 widely differing instruments providing over 2,500 datastreams, concise expression of measurement uncertainty is quite challenging. ARM currently provides data and supporting metadata (information about the data or data quality) to its users through several sources. Because the continued success of the ARM Facility depends on the known quality of its measurements, ARM relies on Instrument Mentors and the ARM Data Quality Office to ensure, assess, and report measurement quality. Therefore, anmore » easily accessible, well-articulated estimate of ARM measurement uncertainty is needed. This report is a continuation of the work presented by Campos and Sisterson (2015) and provides additional uncertainty information from instruments not available in their report. As before, a total measurement uncertainty has been calculated as a function of the instrument uncertainty (calibration factors), the field uncertainty (environmental factors), and the retrieval uncertainty (algorithm factors). This study will not expand on methods for computing these uncertainties. As before, it will focus on the practical identification, characterization, and inventory of the measurement uncertainties already available to the ARM community through the ARM Instrument Mentors and their ARM instrument handbooks. This study continues the first steps towards reporting ARM measurement uncertainty as: (1) identifying how the uncertainty of individual ARM measurements is currently expressed, (2) identifying a consistent approach to measurement uncertainty, and then (3) reclassifying ARM instrument measurement uncertainties in a common framework.« less

Uncertainty in modeled upper ocean heat content change

NASA Astrophysics Data System (ADS)

Tokmakian, Robin; Challenor, Peter

2014-02-01

This paper examines the uncertainty in the change in the heat content in the ocean component of a general circulation model. We describe the design and implementation of our statistical methodology. Using an ensemble of model runs and an emulator, we produce an estimate of the full probability distribution function (PDF) for the change in upper ocean heat in an Atmosphere/Ocean General Circulation Model, the Community Climate System Model v. 3, across a multi-dimensional input space. We show how the emulator of the GCM's heat content change and hence, the PDF, can be validated and how implausible outcomes from the emulator can be identified when compared to observational estimates of the metric. In addition, the paper describes how the emulator outcomes and related uncertainty information might inform estimates of the same metric from a multi-model Coupled Model Intercomparison Project phase 3 ensemble. We illustrate how to (1) construct an ensemble based on experiment design methods, (2) construct and evaluate an emulator for a particular metric of a complex model, (3) validate the emulator using observational estimates and explore the input space with respect to implausible outcomes and (4) contribute to the understanding of uncertainties within a multi-model ensemble. Finally, we estimate the most likely value for heat content change and its uncertainty for the model, with respect to both observations and the uncertainty in the value for the input parameters.

Capturing the complexity of uncertainty language to maximise its use.

NASA Astrophysics Data System (ADS)

Juanchich, Marie; Sirota, Miroslav

2016-04-01

Uncertainty is often communicated verbally, using uncertainty phrases such as 'there is a small risk of earthquake', 'flooding is possible' or 'it is very likely the sea level will rise'. Prior research has only examined a limited number of properties of uncertainty phrases: mainly the probability conveyed (e.g., 'a small chance' convey a small probability whereas 'it is likely' convey a high probability). We propose a new analytical framework that captures more of the complexity of uncertainty phrases by studying their semantic, pragmatic and syntactic properties. Further, we argue that the complexity of uncertainty phrases is functional and can be leveraged to best describe uncertain outcomes and achieve the goals of speakers. We will present findings from a corpus study and an experiment where we assessed the following properties of uncertainty phrases: probability conveyed, subjectivity, valence, nature of the subject, grammatical category of the uncertainty quantifier and whether the quantifier elicits a positive or a negative framing. Natural language processing techniques applied to corpus data showed that people use a very large variety of uncertainty phrases representing different configurations of the properties of uncertainty phrases (e.g., phrases that convey different levels of subjectivity, phrases with different grammatical construction). In addition, the corpus analysis uncovered that uncertainty phrases commonly studied in psychology are not the most commonly used in real life. In the experiment we manipulated the amount of evidence indicating that a fact was true and whether the participant was required to prove the fact was true or that it was false. Participants produced a phrase to communicate the likelihood that the fact was true (e.g., 'it is not sure…', 'I am convinced that…'). The analyses of the uncertainty phrases produced showed that participants leveraged the properties of uncertainty phrases to reflect the strength of evidence but

Do (un)certainty appraisal tendencies reverse the influence of emotions on risk taking in sequential tasks?

PubMed

Bagneux, Virginie; Bollon, Thierry; Dantzer, Cécile

2012-01-01

According to the Appraisal-Tendency Framework (Han, Lerner, & Keltner, 2007), certainty-associated emotions increase risk taking compared with uncertainty-associated emotions. To date, this general effect has only been shown in static judgement and decision-making paradigms; therefore, the present study tested the effect of certainty on risk taking in a sequential decision-making task. We hypothesised that the effect would be reversed due to the kind of processing involved, as certainty is considered to encourage heuristic processing that takes into account the emotional cues arising from previous decisions, whereas uncertainty leads to more systematic processing. One hundred and one female participants were induced to feel one of three emotions (film clips) before performing a decision-making task involving risk (Game of Dice Task; Brand et al., 2005). As expected, the angry and happy participants (certainty-associated emotions) were more likely than the fearful participants (uncertainty-associated emotion) to make safe decisions (vs. risky decisions).

Analytical probabilistic proton dose calculation and range uncertainties

NASA Astrophysics Data System (ADS)

Bangert, M.; Hennig, P.; Oelfke, U.

2014-03-01

We introduce the concept of analytical probabilistic modeling (APM) to calculate the mean and the standard deviation of intensity-modulated proton dose distributions under the influence of range uncertainties in closed form. For APM, range uncertainties are modeled with a multivariate Normal distribution p(z) over the radiological depths z. A pencil beam algorithm that parameterizes the proton depth dose d(z) with a weighted superposition of ten Gaussians is used. Hence, the integrals ∫ dz p(z) d(z) and ∫ dz p(z) d(z)2 required for the calculation of the expected value and standard deviation of the dose remain analytically tractable and can be efficiently evaluated. The means μk, widths δk, and weights ωk of the Gaussian components parameterizing the depth dose curves are found with least squares fits for all available proton ranges. We observe less than 0.3% average deviation of the Gaussian parameterizations from the original proton depth dose curves. Consequently, APM yields high accuracy estimates for the expected value and standard deviation of intensity-modulated proton dose distributions for two dimensional test cases. APM can accommodate arbitrary correlation models and account for the different nature of random and systematic errors in fractionated radiation therapy. Beneficial applications of APM in robust planning are feasible.

SPIDER. V. MEASURING SYSTEMATIC EFFECTS IN EARLY-TYPE GALAXY STELLAR MASSES FROM PHOTOMETRIC SPECTRAL ENERGY DISTRIBUTION FITTING

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

Swindle, R.; Gal, R. R.; La Barbera, F.

2011-10-15

We present robust statistical estimates of the accuracy of early-type galaxy stellar masses derived from spectral energy distribution (SED) fitting as functions of various empirical and theoretical assumptions. Using large samples consisting of {approx}40,000 galaxies from the Sloan Digital Sky Survey (SDSS; ugriz), of which {approx}5000 are also in the UKIRT Infrared Deep Sky Survey (YJHK), with spectroscopic redshifts in the range 0.05 {<=} z {<=} 0.095, we test the reliability of some commonly used stellar population models and extinction laws for computing stellar masses. Spectroscopic ages (t), metallicities (Z), and extinctions (A{sub V} ) are also computed from fitsmore » to SDSS spectra using various population models. These external constraints are used in additional tests to estimate the systematic errors in the stellar masses derived from SED fitting, where t, Z, and A{sub V} are typically left as free parameters. We find reasonable agreement in mass estimates among stellar population models, with variation of the initial mass function and extinction law yielding systematic biases on the mass of nearly a factor of two, in agreement with other studies. Removing the near-infrared bands changes the statistical bias in mass by only {approx}0.06 dex, adding uncertainties of {approx}0.1 dex at the 95% CL. In contrast, we find that removing an ultraviolet band is more critical, introducing 2{sigma} uncertainties of {approx}0.15 dex. Finally, we find that the stellar masses are less affected by the absence of metallicity and/or dust extinction knowledge. However, there is a definite systematic offset in the mass estimate when the stellar population age is unknown, up to a factor of 2.5 for very old (12 Gyr) stellar populations. We present the stellar masses for our sample, corrected for the measured systematic biases due to photometrically determined ages, finding that age errors produce lower stellar masses by {approx}0.15 dex, with errors of {approx}0.02 dex

Uncertainty and inference in the world of paleoecological data

NASA Astrophysics Data System (ADS)

McLachlan, J. S.; Dawson, A.; Dietze, M.; Finley, M.; Hooten, M.; Itter, M.; Jackson, S. T.; Marlon, J. R.; Raiho, A.; Tipton, J.; Williams, J.

2017-12-01

Proxy data in paleoecology and paleoclimatology share a common set of biases and uncertainties: spatiotemporal error associated with the taphonomic processes of deposition, preservation, and dating; calibration error between proxy data and the ecosystem states of interest; and error in the interpolation of calibrated estimates across space and time. Researchers often account for this daunting suite of challenges by applying qualitave expert judgment: inferring the past states of ecosystems and assessing the level of uncertainty in those states subjectively. The effectiveness of this approach can be seen by the extent to which future observations confirm previous assertions. Hierarchical Bayesian (HB) statistical approaches allow an alternative approach to accounting for multiple uncertainties in paleo data. HB estimates of ecosystem state formally account for each of the common uncertainties listed above. HB approaches can readily incorporate additional data, and data of different types into estimates of ecosystem state. And HB estimates of ecosystem state, with associated uncertainty, can be used to constrain forecasts of ecosystem dynamics based on mechanistic ecosystem models using data assimilation. Decisions about how to structure an HB model are also subjective, which creates a parallel framework for deciding how to interpret data from the deep past.Our group, the Paleoecological Observatory Network (PalEON), has applied hierarchical Bayesian statistics to formally account for uncertainties in proxy based estimates of past climate, fire, primary productivity, biomass, and vegetation composition. Our estimates often reveal new patterns of past ecosystem change, which is an unambiguously good thing, but we also often estimate a level of uncertainty that is uncomfortably high for many researchers. High levels of uncertainty are due to several features of the HB approach: spatiotemporal smoothing, the formal aggregation of multiple types of uncertainty, and a

Probabilistic accident consequence uncertainty analysis -- Late health effects uncertain assessment. Volume 2: Appendices

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

Little, M.P.; Muirhead, C.R.; Goossens, L.H.J.

1997-12-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library ofmore » uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA late health effects models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the expert panel on late health effects, (4) short biographies of the experts, and (5) the aggregated results of their responses.« less

Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.

PubMed

Ferdous, Refaul; Khan, Faisal; Sadiq, Rehan; Amyotte, Paul; Veitch, Brian

2011-01-01

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed. © 2010 Society for Risk Analysis.

Uncertainty in the Himalayan energy-water nexus: estimating regional exposure to glacial lake outburst floods

NASA Astrophysics Data System (ADS)

Schwanghart, Wolfgang; Worni, Raphael; Huggel, Christian; Stoffel, Markus; Korup, Oliver

2016-07-01

Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia’s soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.

Development of robust building energy demand-side control strategy under uncertainty

NASA Astrophysics Data System (ADS)

Kim, Sean Hay

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

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

Yang, Xiu; Lei, Huan; Gao, Peiyuan

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a method for quantifying this uncertainty in solvation energies using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many moremore » types of atomic charges; therefore, construction of surrogate models for the charge parameter space required compressed sensing combined with an iterative rotation method to enhance problem sparsity. We present results for the uncertainty in small molecule solvation energies based on these approaches. Additionally, we explore the correlation between uncertainties due to radii and charges which motivates the need for future work in uncertainty quantification methods for high-dimensional parameter spaces.« less

Applied Mathematics in EM Studies with Special Emphasis on an Uncertainty Quantification and 3-D Integral Equation Modelling

NASA Astrophysics Data System (ADS)

Pankratov, Oleg; Kuvshinov, Alexey

2016-01-01

Despite impressive progress in the development and application of electromagnetic (EM) deterministic inverse schemes to map the 3-D distribution of electrical conductivity within the Earth, there is one question which remains poorly addressed—uncertainty quantification of the recovered conductivity models. Apparently, only an inversion based on a statistical approach provides a systematic framework to quantify such uncertainties. The Metropolis-Hastings (M-H) algorithm is the most popular technique for sampling the posterior probability distribution that describes the solution of the statistical inverse problem. However, all statistical inverse schemes require an enormous amount of forward simulations and thus appear to be extremely demanding computationally, if not prohibitive, if a 3-D set up is invoked. This urges development of fast and scalable 3-D modelling codes which can run large-scale 3-D models of practical interest for fractions of a second on high-performance multi-core platforms. But, even with these codes, the challenge for M-H methods is to construct proposal functions that simultaneously provide a good approximation of the target density function while being inexpensive to be sampled. In this paper we address both of these issues. First we introduce a variant of the M-H method which uses information about the local gradient and Hessian of the penalty function. This, in particular, allows us to exploit adjoint-based machinery that has been instrumental for the fast solution of deterministic inverse problems. We explain why this modification of M-H significantly accelerates sampling of the posterior probability distribution. In addition we show how Hessian handling (inverse, square root) can be made practicable by a low-rank approximation using the Lanczos algorithm. Ultimately we discuss uncertainty analysis based on stochastic inversion results. In addition, we demonstrate how this analysis can be performed within a deterministic approach. In the

Preparing Teachers for Uncertainty.

ERIC Educational Resources Information Center

Floden, Robert E.; Clark, Christopher M.

An examination of the various ways in which teaching is uncertain and how uncertainty pervades teachers' lives points out that teachers face uncertainties in their instructional content, ranging from difficult concepts, to unclarity about how teaching might be improved. These forms of uncertainty undermine teachers' authority, creating situations…

The state of the art of the impact of sampling uncertainty on measurement uncertainty

NASA Astrophysics Data System (ADS)

Leite, V. J.; Oliveira, E. C.

2018-03-01

The measurement uncertainty is a parameter that marks the reliability and can be divided into two large groups: sampling and analytical variations. Analytical uncertainty is a controlled process, performed in the laboratory. The same does not occur with the sampling uncertainty, which, because it faces several obstacles and there is no clarity on how to perform the procedures, has been neglected, although it is admittedly indispensable to the measurement process. This paper aims at describing the state of the art of sampling uncertainty and at assessing its relevance to measurement uncertainty.

Physical Uncertainty Bounds (PUB)

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

Vaughan, Diane Elizabeth; Preston, Dean L.

2015-03-19

This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switchingmore » out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.« less

Studying the effect of clinical uncertainty on physicians' decision-making using ILIAD.

PubMed

Anderson, J D; Jay, S J; Weng, H C; Anderson, M M

1995-01-01

The influence of uncertainty on physicians' practice behavior is not well understood. In this research, ILIAD, a diagnostic expert system, has been used to study physicians' responses to uncertainty and how their responses affected clinical performance. The simulation mode of ILIAD was used to standardize the presentation and scoring of two cases to 46 residents in emergency medicine, internal medicine, family practice and transitional medicine at Methodist Hospital of Indiana. A questionnaire was used to collect additional data on how physicians respond to clinical uncertainty. A structural equation model was developed, estimated, and tested. The results indicate that stress that physicians experience in dealing with clinical uncertainty has a negative effect on their clinical performance. Moreover, the way that physicians respond to uncertainty has positive and negative effects on their performance. Open discussions with patients about clinical decisions and the use of practice guidelines improves performance. However, when the physician's clinical decisions are influenced by patient demands or their peers, their performance scores decline.

A complete representation of uncertainties in layer-counted paleoclimatic archives

NASA Astrophysics Data System (ADS)

Boers, Niklas; Goswami, Bedartha; Ghil, Michael

2017-09-01

Accurate time series representation of paleoclimatic proxy records is challenging because such records involve dating errors in addition to proxy measurement errors. Rigorous attention is rarely given to age uncertainties in paleoclimatic research, although the latter can severely bias the results of proxy record analysis. Here, we introduce a Bayesian approach to represent layer-counted proxy records - such as ice cores, sediments, corals, or tree rings - as sequences of probability distributions on absolute, error-free time axes. The method accounts for both proxy measurement errors and uncertainties arising from layer-counting-based dating of the records. An application to oxygen isotope ratios from the North Greenland Ice Core Project (NGRIP) record reveals that the counting errors, although seemingly small, lead to substantial uncertainties in the final representation of the oxygen isotope ratios. In particular, for the older parts of the NGRIP record, our results show that the total uncertainty originating from dating errors has been seriously underestimated. Our method is next applied to deriving the overall uncertainties of the Suigetsu radiocarbon comparison curve, which was recently obtained from varved sediment cores at Lake Suigetsu, Japan. This curve provides the only terrestrial radiocarbon comparison for the time interval 12.5-52.8 kyr BP. The uncertainties derived here can be readily employed to obtain complete error estimates for arbitrary radiometrically dated proxy records of this recent part of the last glacial interval.

Validation of aerosol optical depth uncertainties within the ESA Climate Change Initiative

NASA Astrophysics Data System (ADS)

Stebel, Kerstin; Povey, Adam; Popp, Thomas; Capelle, Virginie; Clarisse, Lieven; Heckel, Andreas; Kinne, Stefan; Klueser, Lars; Kolmonen, Pekka; de Leeuw, Gerrit; North, Peter R. J.; Pinnock, Simon; Sogacheva, Larisa; Thomas, Gareth; Vandenbussche, Sophie

2017-04-01

fall within the range [-1, +1]. A non-zero mean of D indicates the presence of residual systematic errors. If the fraction is smaller than 68%, uncertainties are underestimated; if it is larger, uncertainties are overestimated. For the three ATSR algorithms, we provide statistics and an evaluation at a global scale (separately for land and ocean/coastal regions), for high/low AOD regimes, and seasonal and regional statistics (e.g. Europe, N-Africa, East-Asia, N-America). We assess the long-term stability of the uncertainty estimates over the 17-year time series, and the consistency between ATSR-2 and AATSR results (during their period of overlap). Furthermore, we exploit the possibility to adapt the uncertainty validation concept to the IASI datasets. Ten-year data records (2007-2016) of dust AOD have been generated with four algorithms using IASI observations over the greater Sahara region [80°W - 120°E, 0°N - 40°N]. For validation, the coarse mode AOD at 0.55 μm from the AERONET direct-sun spectral deconvolution algorithm (SDA) product may be used as a proxy for desert dust. The uncertainty validation results for IASI are still tentative, as larger IASI pixel sizes and the conversion of the IASI AOD values from infrared to visible wavelengths for comparison to ground-based observations introduces large uncertainties.

Cued uncertainty modulates later recognition of emotional pictures: An ERP study.

PubMed

Lin, Huiyan; Xiang, Jing; Li, Saili; Liang, Jiafeng; Zhao, Dongmei; Yin, Desheng; Jin, Hua

2017-06-01

Previous studies have shown that uncertainty about the emotional content of an upcoming event modulates event-related potentials (ERPs) during the encoding of the event, and this modulation is affected by whether there are cues (i.e., cued uncertainty) or not (i.e., uncued uncertainty) prior to the encoding of the uncertain event. Recently, we showed that uncued uncertainty affected ERPs in later recognition of the emotional event. However, it is as yet unknown how the ERP effects of recognition are modulated by cued uncertainty. To address this issue, participants were asked to view emotional (negative and neutral) pictures that were presented after cues. The cues either indicated the emotional content of the pictures (the certain condition) or not (the cued uncertain condition). Subsequently, participants had to perform an unexpected old/new task in which old and novel pictures were shown without any cues. ERP data in the old/new task showed smaller P2 amplitudes for neutral pictures in the cued uncertain condition compared to the certain condition, but this uncertainty effect was not observed for negative pictures. Additionally, P3 amplitudes were generally enlarged for pictures in the cued uncertain condition. Taken together, the present findings indicate that cued uncertainty alters later recognition of emotional events in relevance to feature processing and attention allocation. Copyright © 2017. Published by Elsevier B.V.

Cognitive remediation in schizophrenia: A methodological appraisal of systematic reviews and meta-analyses.

PubMed

Bryce, Shayden; Sloan, Elise; Lee, Stuart; Ponsford, Jennie; Rossell, Susan

2016-04-01

Systematic reviews and meta-analyses are a primary source of evidence when evaluating the benefit(s) of cognitive remediation (CR) in schizophrenia. These studies are designed to rigorously synthesize scientific literature; however, cannot be assumed to be of high methodological quality. The aims of this report were to: 1) review the use of systematic reviews and meta-analyses regarding CR in schizophrenia; 2) conduct a systematic methodological appraisal of published reports examining the benefits of this intervention on core outcome domains; and 3) compare the correspondence between methodological and reporting quality. Electronic databases were searched for relevant articles. Twenty-one reviews met inclusion criteria and were scored according to the AMSTAR checklist-a validated scale of methodological quality. Five meta-analyses were also scored according to PRISMA statement to compare 'quality of conduct' with 'quality of reporting'. Most systematic reviews and meta-analyses shared strengths and fell within a 'medium' level of methodological quality. Nevertheless, there were consistent areas of potential weakness that were not addressed by most reviews. These included the lack of protocol registration, uncertainty regarding independent data extraction and consensus procedures, and the minimal assessment of publication bias. Moreover, quality of conduct may not necessarily parallel quality of reporting, suggesting that consideration of these methods independently may be important. Reviews concerning CR for schizophrenia are a valuable source of evidence. However, the methodological quality of these reports may require additional consideration. Enhancing quality of conduct is essential for enabling research literature to be interpreted with confidence. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uncertainty and Clinical Psychology: Therapists' Responses.

ERIC Educational Resources Information Center

Bienenfeld, Sheila

Three sources of professional uncertainty have been described: uncertainty about the practitioner's mastery of knowledge; uncertainty due to gaps in the knowledge base itself; and uncertainty about the source of the uncertainty, i.e., the practitioner does not know whether his uncertainty is due to gaps in the knowledge base or to personal…

Uncertainty in Agricultural Impact Assessment

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Mearns, Linda O.; Rivington, Michael; Antle, John M.; Ruane, Alexander C.

2014-01-01

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.