The Experts below are selected from a list of 11583 Experts worldwide ranked by ideXlab platform
Divya Agrawal - One of the best experts on this subject based on the ideXlab platform.
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effect of simultaneous state parameter estimation and forcing uncertainties on root zone soil moisture for dynamic vegetation using enkf
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivaishuertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
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Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivais-huertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
Michael Markl - One of the best experts on this subject based on the ideXlab platform.
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Accelerated time-resolved 3D contrast-enhanced MR angiography at 3T: clinical experience in 31 patients
Magnetic Resonance Materials in Physics Biology and Medicine, 2006Co-Authors: Alex Frydrychowicz, Thorsten A. Bley, Jan Thorsten Winterer, Andreas Harloff, Mathias Langer, Jürgen Hennig, Michael MarklAbstract:Purpose : To evaluate whether time-resolved 3D MR-angiography at 3T with a net acceleration factor of eight is applicable in clinical routine and to evaluate whether good image quality and a low artifact level can be achieved with a Temporal Update rate that allows for additional information on pathologies. Materials and methods : Thirty-one consecutive patients underwent time-resolved 3D contrast-enhanced MR-angiography on a 3T system. Imaging consisted of accelerated 3D gradient echo sequences combining parallel imaging with an acceleration factor of four, partial Fourier acquisition along phase and slice encoding direction, and twofold Temporal acceleration using view sharing. Data volumes representing the arterial and venous contrast phases were independently evaluated by two experienced radiologists by grading of image quality and artifact level on a 0–3 scale. Results : Time-resolved MR-angiography was successfully performed in all subjects without the need for contrast agent bolus timing. Excellent arterial (average score = 2.65 ± 0.32) and good venous (average score = 2.56 ± 0.28) diagnostic image quality and little image degrading due to artifacts (average score = 2.20 ± 0.16) were confirmed by both independent readers (agreement in 65.2% of all evaluations). In 14 patients vascular pathologies were identified in the arterial phases. In eight examinations Temporal resolution and depiction of contrast agent dynamics provided additional information about pathology. Discussion : Without the necessity for additional bolus timing, time-resolved 3D contrast-enhanced MR-angiography with imaging acceleration along both the spatial encoding direction and Temporal domain revealed excellent diagnostic image quality in neurovascular and thoracic imaging. Despite the limited spatial resolution as compared to high-resolution imaging of the carotid artery bifurcation, the results demonstrate the applicability of contrast-enhanced MR-angiography in thoracic and abdominal MRA as well as cervical imaging with a Temporal Update rate allowing for additional information on pathologies. Future studies may include an evaluation of optimal trade-offs between spatial and Temporal resolution, different acceleration factors and a comparison to the gold-standard for accuracy.
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Accelerated time-resolved 3D contrast-enhanced MR angiography at 3T: clinical experience in 31 patients
Magma (New York N.Y.), 2006Co-Authors: Alex Frydrychowicz, Thorsten A. Bley, Jan Thorsten Winterer, Andreas Harloff, Mathias Langer, Jürgen Hennig, Michael MarklAbstract:Purpose: To evaluate whether time-resolved 3D MR-angiography at 3T with a net acceleration factor of eight is applicable in clinical routine and to evaluate whether good image quality and a low artifact level can be achieved with a Temporal Update rate that allows for additional information on pathologies.
Wendy D Graham - One of the best experts on this subject based on the ideXlab platform.
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effect of simultaneous state parameter estimation and forcing uncertainties on root zone soil moisture for dynamic vegetation using enkf
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivaishuertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
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Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivais-huertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
Jasmeet Judge - One of the best experts on this subject based on the ideXlab platform.
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effect of simultaneous state parameter estimation and forcing uncertainties on root zone soil moisture for dynamic vegetation using enkf
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivaishuertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
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Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF
Advances in Water Resources, 2010Co-Authors: Alejandro Monsivais-huertero, Wendy D Graham, Jasmeet Judge, Divya AgrawalAbstract:Abstract In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and Temporal Update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
Fei Chiang - One of the best experts on this subject based on the ideXlab platform.
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CIKM - CurrentClean: Interactive Change Exploration and Cleaning of Stale Data
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019Co-Authors: Zheng Zheng, Tri Minh Quach, Ziyi Jin, Fei Chiang, Mostafa MilaniAbstract:Enterprises often assume their data is up-to-date, where the presence of a timestamp in the recent past qualifies the data as current. However, entities modeled in the data experience varying rates of change that influence data currency. We argue that data currency is a relative notion based on individual spatio-Temporal Update patterns, and these patterns can be learned and predicted. We develop CurrentClean, a probabilistic system for identifying and cleaning stale values, and enables a user to interactively explore change in her data. Our system provides a Web-based user-interface, and a backend infrastructure that learns Update correlations among cell values in a database to infer and repair stale values. Our demonstration provides two motivating scenarios that highlight change exploration, and cleaning features using clinical, and sensor data from a data centre enterprise.
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ICDE - CurrentClean: Spatio-Temporal Cleaning of Stale Data
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Zheng Zheng, Mostafa Milani, Fei ChiangAbstract:Data currency is imperative towards achieving up-to-date and accurate data analysis. Data is considered current if changes in real world entities are reflected in the database. When this does not occur, stale data arises. Identifying and repairing stale data goes beyond simply having timestamps. Individual entities each have their own Update patterns in both space and time. These Update patterns can be learned and predicted given available query logs. In this paper, we present CurrentClean, a probabilistic system for identifying and cleaning stale values. We introduce a spatio-Temporal probabilistic model that captures the database Update patterns to infer stale values, and propose a set of inference rules that model spatio-Temporal Update patterns commonly seen in real data. We recommend repairs to clean stale values by learning from past Update values over cells. Our evaluation shows CurrentClean's effectiveness to identify stale values over real data, and achieves improved error detection and repair accuracy over state-of-the-art techniques.
