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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The microwave radiometer – 3-channel (MWR3C, RPG-LWP-U90) deployed by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides time-series measurements of brightness temperatures from three channels centered at 23.834, 30, and 89 GHz. These three channels are sensitive to the presence of liquid water and precipitable water vapor.
Abstract not provided.
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A new iteration of the Hydroscanner prototype was designed and built during the execution of the contract. Compared to the previous designs, the communication port was changed from ethernet to USB C, since it provides a more seamless integration as a peripheral unit. An additional advantage is that one single port can be used for both communication and power up of the infrared camera. Although the power requirements are higher than the typical amount provided by a laptop computer, the camera can be run as a peripheral from a docking station when connected to a thunderbolt charging port. The software interface was modified to accommodate two imaging modes, an 8-bit and 14-bit image display. The 8-bit images use a custom colorization scheme that divides all pixels in the image into bins of equal width, such that images containing a wide range of brightness display the intermediate pixel intensities in greyscale values that enhance the contrast. The 14-bit images provide the direct readout from the focal plane array of the camera and can be saved as a text file for later processing in other software packages (MATLAB, Python).
Abstract not provided.
Abstract not provided.
This manual covers the cloud, aerosol, and precipitation spectrometer (CAPS).
This year we will transfer the latest version of INCC6 with last year’s self-diagnostic features to the IAEA. In FY25 we completed the development of three automated self diagnostic features into INNC6. The completion of these features marked the final stable version of INCC 6.0 ready for release to the IAEA and other users. In the first half of the Fiscal Year 2026 we have worked with LANL’s Feynman Center and Export Control office to attain a license for exporting INCC6 to the IAEA. We have successfully attained the export license for INCC6, and contacts at the IAEA have been granted permission to receive INCC6. We are in the final stage of writing the version 6 user manual. Once this is complete, we will deliver INCC6 to the IAEA for testing.
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Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.
Aerosol Observing System (AOS) CCN200 data stream
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ASI project summaries for FY21
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