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cloud_fs (Cloud File System Utilities) [SWR-21-83]
cloud-fs (Cloud File System Utilities) is a generalized file-system handler that will dynamically determine if files are local or on the cloud (currently AWS) and perform basic file-system operations.
cloud_fs (Cloud File System Utilities) 10.6.2021 [SWR-21-83]
cloud-fs (Cloud File System Utilities) is a generalized file-system handler that will dynamically determine if files are local or on the cloud (currently AWS) and perform basic file-system operations.
Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns
The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.
Invigoration or Enervation of Convective Clouds by Aerosols?
Are convective clouds strengthened by the addition of aerosol particles? We present new theoretical calculations which starkly contrast previous results. Prior foundational work suggested that aerosols strongly invigorate convective cloud updrafts via changes to cold-phase processes. We show that the peak magnitude of invigoration by this mechanism is substantially reduced for cold-based storms. For warm-based storms, the updrafts are weakened, not strengthened, by aerosol-induced changes to cold-phase processes. Our calculations show that if invigoration occurs, it is driven primarily by changes to warm-phase processes and may be largest well before the storm reaches maturity. The calculations are based on a new formulation of the moist adiabatic lapse rate that accounts for freezing, supersaturation, and condensate loading. The results significantly reshape our understanding of the impact of aerosols on convective updrafts.
Convergence of Cloud Droplet Spectral Relative Dispersion During Entrainment‐Mixing Based on Particle‐Resolved Direct Numerical Simulations
Entrainment-mixing processes critically impact cloud microphysical properties, but their effects on the relative dispersion (d) of cloud droplet size distributions (CDSDs) remain elusive. A direct numerical simulation model is initialized with different CDSDs to fill the gap. These results show that d decreases for broad CDSDs and increases for narrow ones, ultimately converging to approximately 0.5 regardless of initial CDSDs during the evaporation-dominated mixing stage. The supersaturation fluctuation and the shape of CDSDs jointly influence the convergence behavior of d. Further sensitivity tests show that the initial microphysical/dynamical/thermodynamical conditions exert negligible effects on the final converged value of d but affect the convergence rate (k). The k generally increases with increasing droplet number concentration and dissipation rate, and increases with decreasing liquid water content, relative humidity of entrained air, and mixing fraction of cloudy air. A conceptual model with two timescales is proposed; k and the timescales are negatively correlated, meaning that slow mixing and/or evaporation process results in slow convergence of d. In conclusion, this finding provides an important reference for improving understanding and parameterization of d during the entrainment-mixing processes.
Cloud-based Testbed for Adaptive Under-Frequency Load Shedding with High DER Penetration
Increasing penetration of distributed energy resources and behind-the-meter renewables may soon disrupt the efficacy of critical protection schemes, such as under-frequency load shedding (UFLS). Improved data exchange and coordination across the transmission-distribution boundary will be required to maintain reliability of bulk electric system. Standards-based data integration platforms using agreed-upon semantic vocabularies, such as the Common Information Model, will be key to enabling adaptive protection schemes requiring synthesized data from both the bulk power system and behind-the-meter resources. This paper introduces a cloud-based open-source data integration environment and UFLS clustering algorithm being developed to enable adaptive relay coordination between transmission and distribution utilities in the state of Vermont.
New Understanding of Cloud Processes via Unsupervised Cloud Classification in Satellite Images
This white paper addresses DOE AI4ESP focus areas #1 and #3. We propose to leverage advances in machine learning and artificial intelligence to create a new unsupervised cloud classification framework that will produce new datasets and cloud process understanding to reduce biases in Earth system models.
Demonstration Trials of AI/ML Edge+Cloud Suite (CRADA Final Report)
PACE AI and LBNL partnered under this CRADA to test and evaluate the PACE5 edge node prototype, an AI/ML edge and cloud-based suite, at FLEXLAB.The objective of the test was to evaluate the PACE5 edge node prototype's ability to perform demand shed and take to dynamic price signals, and to demonstrate advanced fault detection and microgrid monitoring capabilities.
To Exascale and Beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud-Resolving Scales
The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.
The intrinsic reddening of the Magellanic Clouds as traced by background galaxies – III. The Large Magellanic Cloud
ABSTRACT We present a map of the total intrinsic reddening across ≃ 90 deg2 of the Large Magellanic Cloud (LMC) derived using optical (ugriz) and near-infrared (IR; YJKs) spectral energy distributions (SEDs) of background galaxies. The reddening map is created from a sample of 222 752 early-type galaxies based on the lephareχ2 minimization SED-fitting routine. We find excellent agreement between the regions of enhanced intrinsic reddening across the central (4 × 4 deg2) region of the LMC and the morphology of the low-level pervasive dust emission as traced by far-IR emission. In addition, we are able to distinguish smaller, isolated enhancements that are coincident with known star-forming regions and the clustering of young stars observed in morphology maps. The level of reddening associated with the molecular ridge south of 30 Doradus is, however, smaller than in the literature reddening maps. The reduced number of galaxies detected in this region, due to high extinction and crowding, may bias our results towards lower reddening values. Our map is consistent with maps derived from red clump stars and from the analysis of the star formation history across the LMC. This study represents one of the first large-scale categorizations of extragalactic sources behind the LMC and as such we provide the lephare outputs for our full sample of ∼ 2.5 million sources.
Giant Cloud Condensation Nuclei Facilitate Drizzle Formation in Stratocumulus—Insights From a Combined Observation‐Modeling Framework
The mechanism for initiating drizzle drop remains a gap in the current understanding of warm rain formation. One prevalent hypothesis suggests that the presence of Giant Cloud Condensation Nuclei (GCCN) generates drizzle‐sized drops necessary to trigger the Collision‐Coalescence (C‐C) process. Here, in this study, this hypothesis is investigated using a novel framework that integrates in situ observations, remote sensing measurements, and idealized models. Results show that GCCN can efficiently generate drizzle drops through condensation, producing a broad Droplet Size Distribution (DSD) comparable to in situ observations. The large drizzle drop and broad DSD strongly facilitate C‐C, further accelerating drizzle initiation. To compare with observation, the model‐generated DSDs are used to generate radar Doppler spectra where radar reflectivity and Doppler skewness is estimated. The simulated radar quantities correspond well with radar observations, providing critical evidence for the GCCN‐induced drizzle initiation mechanism.
The Development of a Lagrangian Cloud Microphysics Package in HiGrad for the Simulation of PyroCumulonimbus (PyroCb) Clouds [Slides]
PyroCumulonimbus (PyroCb) Clouds form by condensation of intensely heated, rising air becoming saturated due to adiabatic cooling. Extent of pyroCb development depends on atmospheric stratification + ambient moisture + fire fluxes of heat and moisture.
Development of Robust Fully Autonomous Machine Learning-based Cloud Processing for Infrared Cloud Imagers
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Improving middle and high latitude cloud liquid water path measurements from MODIS
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Cloud computing and rural globalization: Evidence for the U.S. nonfarm economy
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