ML18114A191: Difference between revisions
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| number = ML18114A191 | | number = ML18114A191 | ||
| issue date = 04/25/2018 | | issue date = 04/25/2018 | ||
| title = Annual Report by Work Group 2- Data Assimilation, Uncertainty Assessment and Environmental Model Confirmation to the Interagency Collaborative for Environmental Modeling and Monitoring by Thomas Nicholson, NRC and WG2 Chair, April 25, 2018 | | title = Annual Report by Work Group 2- Data Assimilation, Uncertainty Assessment and Environmental Model Confirmation to the Interagency Collaborative for Environmental Modeling and Monitoring by Thomas Nicholson, NRC and WG2 Chair, April 25, 2018 | ||
| author name = Nicholson T | | author name = Nicholson T | ||
| author affiliation = NRC/RES/DRA | | author affiliation = NRC/RES/DRA | ||
Revision as of 09:05, 18 April 2019
| ML18114A191 | |
| Person / Time | |
|---|---|
| Issue date: | 04/25/2018 |
| From: | Thomas Nicholson NRC/RES/DRA |
| To: | |
| T. Nicholson 415-2471 | |
| References | |
| Download: ML18114A191 (16) | |
Text
Annual Report by Work Group 22018 ICEMM Annual Public MeetingApril 24 -25, 2018 U.S. Nuclear Regulatory Commission Rockville, MD
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Butler et al
Big Data and Data Assimilation
The concurrent, multivariate assimilation of various terrestrial hydrological datasets (soil moisture, snow depth, snow cover, terrestrial water storage, irrigation intensity) has been demonstrated for the NCA LDAS. Multivariate assimilation of satellite remote sensing datasets are helpful in improving water budget components, including streamflow
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In Clearance establish a base set of model sensitivity analysis and uncertainty evaluation measures, in
addition to the other performance measures
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