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mosartwmpy sample input data; 1980 - 1985
Sample input data spanning the years 1980-1985 for running the mosartwmpy water routing and management model: https://github.com/IMMM-SFA/mosartwmpy More information is available in the README.
mosartwmpy sample input data; 1980 - 1985
Sample input data spanning the years 1980-1985 for running the mosartwmpy water routing and management model: https://github.com/IMMM-SFA/mosartwmpy More information is available in the README.
mosartwmpy sample input data; 1980 - 1985
Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.
mosartwmpy sample input data; 1980 - 1985
Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.7: new reservoir files with corrected locations based on an exhaustive review by Dan Broman for the 9505 project; corresponding long term mean demand and flow files updated based on VIC4 simulations using Daymet forcing. v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.
mosartwmpy sample input data; 1980 - 1985
Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.8: Update with new CAP_MIN reservoir parameter, corresponds with mosartwmpy v1.0.0 v0.0.7: new reservoir files with corrected locations based on an exhaustive review by Dan Broman for the 9505 project; corresponding long term mean demand and flow files updated based on VIC4 simulations using Daymet forcing. v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.
Computer program samples digital data for CRT display
High volume, multichannel data reduction computer program permits selection of the rates at which digital data is sampled. The program, written in FORTRAN 4 source language, also permits accessibility to the original mass of data.
ASSESSMENT OF SYSTEMATIC CHROMATIC ERRORS THAT IMPACT SUB-1% PHOTOMETRIC PRECISION IN LARGE-AREA SKY SURVEYS
Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey’s stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. The residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.
Improving the Acquisition and Management of Sample Curation Data
This paper discusses the current sample documentation processes used during and after a mission, examines the challenges and special considerations needed for designing effective sample curation data systems, and looks at the results of a simulated sample result mission and the lessons learned from this simulation. In addition, it introduces a new data architecture for an integrated sample Curation data system being implemented at the NASA Astromaterials Acquisition and Curation department and discusses how it improves on existing data management systems.
Integrated sampling and data flow for Hanford vitrification - 15337
The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is a one-of-a-kind nuclear facility being designed, built, and commissioned for the U.S. Department of Energy (DOE) by Bechtel National, Inc. and its subcontractor URS Corporation. The WTP facilities are under construction to complete the design and construction, as well as planning to support the startup, commissioning, and operation for vitrifying the radioactive waste stored in aging underground tanks at the Hanford Site. The work progress includes development of waste feed acceptance and qualification program, including methodologies for processing feed batches. Qualification data from staged feed samples will support the verification that waste acceptance criteria (WAC) and processability requirements are met. This verification is required to ensure safe operation of WTP processes for vitrifying the waste, while establishing specific campaign and batch sheets to comply with glass form acceptance requirements. Sampling and data integration are critical for process control and monitoring of the feed through unit operations. Data collection begins with process knowledge of staged feed for determining the required WAC analyses and testing for unit operations. The waste feed qualification data form the baseline information for WTP operation. The qualification data will be tracked and compared with sample analyses at different steps in vitrification operation. All aspects of data life cycle will be managed by the WTP Plant Engineering organization. The data management approach is established in this manner to allow maintaining campaign traceability through glass production records, while safely processing the corresponding feed campaigns and complying with permitting requirements. (authors)
ARM: Carbon Dioxide Flux Gas Data: 4m samples
Carbon Dioxide Flux Gas Data: 4m samples
ARM: Carbon Dioxide Flux Wind Data: 25m samples
Carbon Dioxide Flux Wind Data: 25m samples
ARM: Carbon Dioxide Flux Wind Data: 4m samples
Carbon Dioxide Flux Wind Data: 4m samples
ARM: Carbon Dioxide Flux Wind Data: 60m samples
Carbon Dioxide Flux Wind Data: 60m samples
Simulation system for testing a radar system
A simulation system for use in testing a radar system comprises a coarse delay module, a fine delay module, and a doppler shift module. The coarse delay module is configured to receive a first stream of digital data samples that are sampled from a radar signal at a sample time period or a second stream of digital data samples that are processed by another simulation system component and delay the digital data samples by a selectable first delay time that is greater than or equal to the sample time period. The fine delay module is configured to receive the digital data samples and filter the digital data samples to represent delay by a selectable second delay time that is less than the sample time period. The doppler shift module is configured to receive the digital data samples and adjust a value of a frequency content of the fine delayed samples.