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Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.
High-resolution, long-term solar dataset is essential for characterizing the variability of solar energy resources and for informing strategies that ensure grid reliability and resilience in grid systems with high levels of solar energy integration. We present the development of a new 4-km, hourly Earth system dataset for the contiguous United States (CONUS), using a statistical downscaling approach that integrates the National Solar Radiation Database (NSRDB) with regional Earth system model projections. The new high-resolution Earth system dataset includes key variables - GHI, DNI, DHI, surface air temperature, and wind speed - under two future scenarios. Preliminary results show a reasonable agreement with NSRDB observations, with nBias less than 1% for GHI across CONUS. The dataset is expected to support in-depth analyses of extreme weather impacts and provide input to resource adequacy for future energy systems with diverse generation sources.
This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.
This short report provides a time-sensitive update on the 20-year wind resource data set (named “CA20”) produced in 2020 by the National Renewable Energy Laboratory (NREL) for the outer continental shelf (OCS) off the coast of California.
Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only. The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length
This dataset provides future supply curves representing the total resource potential for land-based wind and solar photovoltaic (PV) deployment in the conterminous United States after accounting for the impact of land-use and land-cover change (LULC). We use LULC projections from 2010 to 2050 developed based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios. The LULC projections are subsequently fed into the renewable energy potential model (reV), which estimates total available wind and solar capacity after excluding non-developable land. Supply curves are provided for four IPCC scenarios in 2050: A1B, A2, B1, and B2. As a baseline, we also provide the supply curve from the B2 scenario in 2010. In addition to the supply curves, we also provide representative wind and solar generation profiles for each supply curve point. These generation profiles are provided as capacity factors and are based on a 2012 weather year using NSRDB and WindToolkit resource data. For more details on the supply curve and profile datasets included here please refer to README. Additional information on the supply curves and the LULC projections used to generate them, as well as an analysis of their impact on wind and solar deployment under decarbonization can be found in the publication linked below: "U.S. Wind and Solar PV Supply Curves with Future Land-use Change Publication".
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
This data resource describes an open-source cell hardware that enables low temperature electrolysis (LTE) testing at elevated pressures. Existing commercial options have several downsides when it comes to R&D testing. They are often not designed for repeated reassembly, may not be able to accommodate porous transport layers with different thicknesses, and do not give state-of-the-art performance. Therefore, this hardware was developed specifically with LTE R&D in mind and its design is being made available to the global LTE community. This work was planned and funded by the U.S. Department of Energy's H2NEW consortium (https://h2new.energy.gov/). The hardware design package (.zip file) details the drawings, auxiliary materials, and procedures required to fabricate, assemble, and operate the National Renewable Energy Laboratory's (NREL's) high-pressure low-temperature electrolysis cells. While the hardware itself—end plates, current collectors, flow fields, bolts and washers, tube fittings—is always the same, the assembly and operating procedures may change depending on the active materials being tested, especially for the membrane. Material-specific assembly and operating procedures will be posted to the H2NEW website as they are developed and validated. Disclaimer: The documents and drawings included in this download package describe a design for a low temperature electrolysis hardware that is intended to comply with leak testing according to ASME B31.1. Safe operation at ambient and elevated pressures is the sole responsibility of the end user, which should be evaluated on a case-by-case basis for each individual cell. Factors affecting the sealing capability may depend on, for example, machining quality, cell assembly components, operating conditions, and operating history. Operation at pressure should only be performed on qualified test stands by qualified operators. NREL/ALLIANCE FOR SUSTAINABLE ENERGY, LLC/DOE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, AND MAKES NO WARRANTY AS TO THE ACCURACY, COMPLETENESS, OR USEFULNESS OF ANY INFORMATION PROVIDED HEREIN. USE OF THIS PACKAGE IS AT THE USER’S OWN RISK.
Earth resources data from Apollo 9 multispectral photography
Earth resources data from man operated airborne/ spaceborne sensors and data handling systems, examining Apollo Applications Program
Systems design of national system for management of scientific and technological information, including prototype subsystem for handling earth resources data
The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.
An investigation was made to determine the feasibility of applying earth resources data to the detection of air pollution, particularly pollution from coal burning. Efforts were also made to detect any damage caused by such pollution to vegetation growth and tree life. Results show that vegetative vigor even at low ambient concentrations was damaged and that Eastern white pine trees were severely damaged by the pollutants.
The studies considered are concerned with the magnitude of the atmospheric effects in multispectral earth resources data taken by satellites and aircraft. The atmospheric effects are present in all space oriented and aerial data even in the so-called atmospheric 'windows' through which all sensors observe the ground. Atmospheric effects are quite varied and depend on several parameters. An atmospheric model developed by Turner et al. (1971) is discussed. The model is applied to the test site, which is located in North Dakota. The results are used to obtain atmospheric correction data for ERTS-1 scanner data.
The Algorithm Simulation Test and Evaluation Program (ASTEP) is a modular computer program developed for the purpose of testing and evaluating methods of processing remotely sensed multispectral scanner earth resources data. ASTEP is written in FORTRAND V on the UNIVAC 1110 under the EXEC 8 operating system and may be operated in either a batch or interactive mode. The program currently contains over one hundred subroutines consisting of data classification and display algorithms, statistical analysis algorithms, utility support routines, and feature selection capability. The current program can accept data in LARSC1, LARSC2, ERTS, and Universal formats, and can output processed image or data tapes in Universal format.
Research performed on NASA and USDA remote sensing projects are reviewed and include: (1) the 1971 Corn Blight Watch Experiment; (2) crop identification; (3) soil mapping; (4) land use inventories; (5) geologic mapping; and (6) forest and water resources data collection. The extent to which ERTS images and airborne data were used is indicated along with computer implementation. A field and laboratory spectroradiometer system is described together with the LARSYS software system, both of which were widely used during the research. Abstracts are included of 160 technical reports published as a result of the work.
Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation.
The progress made and limitations encountered in using ERTS-1 data for resource management in Ohio is surveyed. Photo-opto-electronic techniques were used with special facility equipment and resolution to 10-30 meters was required to determine strip mine features. Lake Erie's sediment patterns were detected along with flooding conditions, large scale vegetative damage caused by toxic air pollutants was identified, Ohio land use categories were tabulated and thematic map containing forested areas was derived. The experimental findings regarding utility/relevance assessment were ranked in 4 classes for all the applications involved. Preliminary recommendations for operational satellite earth resources survey data requirements are presented and data analysis and product dissemination are proposed to be centralized in conjunction with thermal IR data and an increased resolution.