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Related Concept Videos

Downsampling01:20

Downsampling

260
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
260
Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Methods of Obtaining Topography01:25

Methods of Obtaining Topography

121
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Second-generation downscaled earth system model data using generative machine learning.

Grant Buster1, Brandon N Benton1, Deeksha Rastogi2

  • 1Strategic Energy Analysis Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA.

Data in Brief
|July 4, 2025
PubMed
Summary

The second-generation Sup3rCC dataset offers enhanced, high-resolution meteorological data for renewable energy analysis. It improves climate change impact assessments and energy system modeling with downscaled climate projections.

Keywords:
Energy system modellingMeteorological dataSevere weather

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Area of Science:

  • Climate Science
  • Renewable Energy
  • Data Science

Background:

  • Existing Earth System Model (ESM) data lack the resolution for detailed energy system analysis.
  • Previous Sup3rCC dataset provided 100-km daily-average ESM data, limiting granular insights.
  • Accurate, high-resolution meteorological data are crucial for understanding climate change impacts on energy systems.

Purpose of the Study:

  • To introduce the second-generation Sup3rCC dataset, offering enhanced resolution and accuracy for meteorological data.
  • To provide downscaled climate projections from multiple ESMs for improved energy system modeling and resilience studies.
  • To enable users to assess uncertainties and variability in future meteorological conditions impacting renewable energy.

Main Methods:

  • Downscaled multiple Earth System Models (ESMs) from CMIP6 using the Super-Resolution for Renewable Resource Data (sup3r) machine learning approach.
  • Applied improved bias correction methods and incorporated downscaled precipitation data.
  • Generated 4-km hourly resolution data for temperature, wind, pressure, solar radiation, and humidity over the contiguous US.

Main Results:

  • The dataset provides 400 years of data from six ESMs across two Shared Socioeconomic Pathways (SSPs).
  • Achieved a 25x spatial and 24x temporal enhancement compared to the source ESM data.
  • All data are double-bias corrected, ensuring minimal historical bias for immediate use in energy system analysis.

Conclusions:

  • The second-generation Sup3rCC dataset offers a significant advancement for renewable energy resource assessment and energy system modeling.
  • The high-resolution, bias-corrected data facilitate precise modeling of energy resilience and adaptation strategies.
  • This dataset is vital for operational planning, risk assessment, and understanding the effects of changing climate on energy infrastructure.