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1. Pacific Low Cloud Occurrence Variability Associated with ENSO as Observed by CloudSat and CALIPSO
- Description:
- As strong cooling agents in the climate system, marine low-level clouds are an important component of the climate system. Demonstrating how marine low-level clouds respond to anomalies in the atmospheric general circulation in the present climate has the potential to be illustrative of how low clouds might change in a future climate. We examine how thermodynamic factors that control low cloud occurrence change during an ENSO cycle and then how low clouds observed by the CloudSat and CALIPSO satellites vary. In addition to the well-known decrease in marine low clouds in the Northeast Pacific during El Niño onset in June, July and August (JJA), we also find significant increases in the low cloud occurrence on the flanks of the anomalously warm water in the Equatorial Central Pacific during December, January and February (DJF). These low cloud changes are linked to measurable changes in the Earth’s energy budget with net warming of the Earth system during JJA and cooling of the Earth system during DJF. This is the python code to create the figures for the paper about the above research.
- Keyword:
- CloudSat , CALIPSO, El Niño–Southern Oscillation (ENSO), and marine
- Subject:
- ENSO
- Creator:
- Gombert, Peter M., Strong, Courtenay, and Mace, Gerald G. (Jay)
- Owner:
- Sally Benson
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- Python and English
- Date Uploaded:
- 06/18/2024
- Date Modified:
- 09/09/2024
- Date Created:
- 2023-06-01 to 2024-06-01 (collected) and 2007-2018 (created)
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S5d-64j3-1n2n
- Description:
- This dataset is a custom Kraken2 formatted database for the identification of Fungi from shotgun metagenomic data. Kraken2 is a k-mer based read classifier (Wood et al. 2019; https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1891-0). The dataset was built with the default k-mer length (k=35) from all publicly available fungal genomes at JGI Mycocosm ( https://mycocosm.jgi.doe.gov/mycocosm/home), and all archaea, bacteria, viral, plasmid, human, fungi, plant, and protozoa genomes, as well as the UniVec Core and nt reference database at NCBI ( https://www.ncbi.nlm.nih.gov/). The reference genomes and sequences were downloaded from JGI and NCBI in March 2020.
- Keyword:
- protozoa, Kraken2 database, plant, bacteria, genomes, fungi, human, archaea, plasmid, and virus
- Subject:
- Mycology and Genomics
- Creator:
- Dentinger, Bryn T. M.
- Owner:
- Bryn Dentinger
- Language:
- English
- Date Uploaded:
- 06/08/2022
- Date Modified:
- 06/03/2024
- Date Created:
- 2020-03-29
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/S50d-154b-fppf
- Description:
- The Differential Emissivity Imaging Disdrometer (DEID) is a new evaporation-based optical and thermal instrument designed to measure the mass, size, density, and type of individual hydrometeors and their bulk properties. Hydrometeor spatial dimensions are measured on a heated metal plate using an infrared camera by exploiting the much higher thermal emissivity of water compared with metal. As a melted hydrometeor evaporates, its mass can be directly related to the loss of heat from the hotplate assuming energy conservation across the hydrometeor. The heat-loss required to evaporate a hydrometeor is found to be independent of environmental conditions including ambient wind velocity, moisture level, and temperature. The difference in heat loss for snow versus rain for a given mass offers a method for discriminating precipitation phase. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm, respectively, determined by the size of the hotplate and the thermal camera specifications. Measurable snow water equivalent (SWE) precipitation rates range from 0.001 to 200 mm h−1, as validated against a standard weighing bucket. Preliminary field-experiment measurements of snow and rain from the winters of 2019 and 2020 provided continuous automated measurements of precipitation rate, snow density, and visibility. Measured hydrometeor size distributions agree well with canonical results described in the literature. and A new precipitation sensor, the Differential Emissivity Imaging Disdrometer (DEID), is used to provide the first continuous measurements of the mass, diameter, and density of individual hydrometeors. The DEID consists of an infrared camera pointed at a heated aluminum plate. It exploits the contrasting thermal emissivity of water and metal to determine individual particle mass by assuming that energy is conserved during the transfer of heat from the plate to the particle during evaporation. Particle density is determined from a combination of particle mass and morphology. A Multi-Angle Snowflake Camera (MASC) was deployed alongside the DEID to provide refined imagery of particle size and shape. Broad consistency is found between derived mass-diameter and density-diameter relationships and those obtained in prior studies. However, DEID measurements show a generally weaker dependence with size for hydrometeor density and a stronger dependence for aggregate snowflake mass.
- Keyword:
- multi-angle snowflake camera, differential emissivity imaging disdrometer, snow, density , mass, disdrometer, DEID, MASC, hydrometeors, and atmospheric science
- Subject:
- Atmospheric Science
- Creator:
- Rees, Karlie N., Pardyjak, Eric R., Garrett, Timothy J., and Singh, Dhiraj K.
- Contributor:
- Blackmer, Alex, Donovan, Spencer, Reaburn, Allan, and Roper, Peter
- Owner:
- Karlie Rees
- Based Near Label Tesim:
- Red Butte Canyon, Utah, United States
- Language:
- English
- Date Uploaded:
- 08/18/2021
- Date Modified:
- 06/03/2024
- Date Created:
- 2020-01-14 to 2020-02-06
- License:
- CC BY – Allows others to use and share your data, even commercially, with attribution.
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/S50D-SPT1-FNHH
- Description:
- We apply Bayesian inference to instrument calibration and experimental-data uncertainty analysis for the specific application of measuring radiative intensity with a narrow-angle radiometer. We develop a physics-based instrument model that describes temporally varying radiative intensity, the indirectly measured quantity of interest, as a function of scenario and model parameters. We identify a set of five uncertain parameters, find their probability distributions (the posterior or inverse problem) given the calibration data by applying Bayes’ Theorem, and employ a local linearization to marginalize the nuisance parameters resulting from errors-in-variables. We then apply the instrument model to a new scenario that is the intended use of the instrument, a 1.5 MW coal-fired furnace. Unlike standard error propagation, this Bayesian method infers values for the five uncertain parameters by sampling from the posterior distribution and then computing the intensity with quantifiable uncertainty at the point of a new, in-situ furnace measurement (the posterior predictive or forward problem). Given the instrument-model context of this analysis, the propagated uncertainty provides a significant proportion of the measurement error for each in-situ furnace measurement. With this approach, we produce uncertainties at each temporal measurement of the radiative intensity in the furnace, successfully identifying temporal variations that were otherwise indistinguishable from measurement uncertainty.
- Subject:
- Validation and Simulation
- Creator:
- Scheib, Kaitlyn, Spinti, Jennifer P., Smith, Sean T., Harding, N. Stanley, Smith, Philip J., and Draper, Teri S.
- Owner:
- Philip Smith
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 12/01/2020
- Date Modified:
- 01/28/2022
- Date Created:
- November 2020
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S50D6AFQ84VP
- Description:
- Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple mid-latitude and polar locations both with and without wind shielding. Here we present an analysis of Arctic field observations — with and without a Belfort double Alter shield — and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (< 5 m/s) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally < 0.5 m/s. For those events with wind speeds < 1.5 m/s, hydrometeors fall with an orientation angle mode of 12 degrees from the horizontal plane, and large, low-density aggregates are as much as five times more likely to be observed. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55% (74%) for a wind speed of 5 m/s (10 m/s). We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (< 5 m/s). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined.
- Subject:
- Meteorology and Atmospheric Science
- Creator:
- Talaei, Ahmad, Hang, Chaoxun, Fitch, Kyle E., and Garrett, Timothy J.
- Owner:
- Language:
- English
- Date Uploaded:
- 07/20/2020
- Date Modified:
- 12/14/2020
- Date Created:
- 20120521 to 20200719
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S50DQTX9K7QY
- Description:
- Thin boundary layer Arctic mixed-phase clouds are generally thought to precipitate pristine and aggregate ice crystals. Here we present automated surface photographic measurements showing that only 35\% of precipitation particles exhibit negligible riming and that graupel particles $\geq1\,\rm{mm}$ in diameter commonly fall from clouds with liquid water paths less than $50\,\rm{g\,m^{-2}}$. A simple analytical formulation predicts that significant riming enhancement can occur in updrafts with speeds typical of Arctic clouds, and observations show that such conditions are favored by weak temperature inversions and strong radiative cooling at cloud top. However, numerical simulations suggest that a mean updraft speed of $0.75\,\rm{m\,s^{-1}}$ would need to be sustained for over one hour. Graupel can efficiently remove moisture and aerosols from the boundary layer. The causes and impacts of Arctic riming enhancement remain to be determined.
- Subject:
- Atmospheric sciences, Computational research, and Arctic research
- Creator:
- Garrett, Timothy J. and Fitch, Kyle E.
- Contributor:
- Shkurko, Konstantin , Talaei, Ahmad, Gaustad, Krista, and Maahn, Maximilian
- Owner:
- BRIAN MCBRIDE
- Based Near Label Tesim:
- Oliktok Point, Alaska, United States
- Language:
- English
- Date Uploaded:
- 06/04/2020
- Date Modified:
- 07/21/2020
- Date Created:
- Code creation dates from 20161208 to 20180609 and Processed between 20170627 and 20190320
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/s50dva5jk2pd
- Description:
- . . .
- Keyword:
- Pluto, formation, and dynamical evolution
- Subject:
- dwarf planets: Pluto, planets and satellites: formation, and planets and satellites: dynamical evolution
- Creator:
- Bromley, Benjamin and Kenyon, Scott
- Owner:
- BENJAMIN BROMLEY
- Language:
- English
- Date Uploaded:
- 07/26/2019
- Date Modified:
- 10/03/2024
- Date Created:
- Unknown
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset and Software or Program Code
- Identifier:
- https://doi.org/10.7278/S50D-EFCY-ZC00
- Description:
- . . .
- Keyword:
- Charon, NASA, Pluto, astronomy, satellites, stability, dynamical evolution, and planets
- Subject:
- astronomy, Charon, and planets
- Creator:
- Bromley, Benjamin C. and Kenyon, Scott J.
- Depositor:
- BRIAN MCBRIDE
- Owner:
- BENJAMIN BROMLEY
- Language:
- English
- Date Uploaded:
- 07/11/2019
- Date Modified:
- 10/03/2024
- Date Created:
- 2018-05-15 to 2018-12-14
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/s50d-w273-1gg0
- Description:
- This SAS program can be used to calculate Grocery Purchase Quality Index-2016 (GPQI-2016) total and component scores from food purchase data (dollars and cents) that have been summarized into the 29 categories of the USDA Food Plans. The code can be adapted to calculate GPQI-2016 scores for data that use a smaller number of categories.
- Keyword:
- nutrition informatics, food expenditures, Healthy Eating Index, grocery shopping, grocery quality, nutrition, and USDA
- Subject:
- human nutrition and food purchasing
- Creator:
- Brewster, Philip J., Guenther, Patricia M., and Hurdle, John F.
- Contributor:
- Tran, Lethuy and Chidambaram, Valli
- Owner:
- BRIAN MCBRIDE
- Based Near Label Tesim:
- University of Utah, Utah, United States
- Language:
- English
- Date Uploaded:
- 07/10/2019
- Date Modified:
- 06/03/2024
- Date Created:
- 2018-01-01 to 2018-12-31
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Software or Program Code
- Identifier:
- https://doi.org/10.7278/S5HD7ST1