xsacrgridppi.c1
This datastream is made by xsacrgridppi VAP. It has (time, Y, X) gridded Ka-band PPI radar data, using nearest neighbor algorithm, with calibrated (.b1) input.
54 ENVIRONMENTAL SCIENCES↗
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Results for “vap”
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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Back trajectories for TBS flights (c1-level)
The ARMTRAJ VAP provides four trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The four trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the four datasets, HYSPLIT will also be run at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields will be reported.
54 ENVIRONMENTAL SCIENCES↗
Synoptic Weather Regime Classifications for June, July, August, and September, 2022
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A). This dataset includes the data in June, July, August, and September; the last year of the data is 2022.
54 ENVIRONMENTAL SCIENCES↗
Synoptic Weather Regime Classifications for the whole year, from 2014 to 2015
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
54 ENVIRONMENTAL SCIENCES↗
Synoptic Weather Regime Classifications for June, July, August, from 2000 to 2024
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
node_som_ml↗
Synoptic Weather Regime Classifications for March, April and May, from 2000 to 2025
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
node_som_ml↗
Synoptic Weather Regime Classifications for June, July and August, from 2000 to 2025
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
node_som_ml↗
Synoptic Weather Regime Classifications for December, January and February, from 2000 to 2025
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
{node_som_ml,synop_wea_reg}↗
Synoptic Weather Regime Classifications for September, October and November, from 2000 to 2025
The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).
node_som_ml↗
Recent advances in preparative electrophoresis
Various approaches for preparative electrophoresis, and three new instruments for preparative electrophoresis are discussed. Consideration is given to isoelectric focusing, isotachophoresis, and zone electrophoresis, three gel-based electrophoresis methods. The design, functions, and performance of the Elphor VaP 21 device of Hannig (1982), the shear-stabilized BIOSTREAM separator of Thompson (1983), and the recycling isoelectric focusing device are described.
Mosher, Richard A.↗
Design issues for Mars planetary rovers
The paper presents some of the design issues and vehicle requirements that need to be addressed for the Mars planetary rovers. Some of the designs currently being investigated, including the JPL rover, the Martin Marietta vehicle, and the French Space Agency's VAP project, are examined. The rover must satisfy such mission requirements as surveying the terrain, preparing the landing sites, loading and unloading components for base operations, and aiding in the recovery of in situ materials.
Lee, Gordon K. F.↗
Satellite and Surface Data Synergy for Developing a 3D Cloud Structure and Properties Characterization Over the ARM SGP. Stage 1: Cloud Amounts, Optical Depths, and Cloud Heights Reconciliation
One of the primary Atmospheric Radiation Measurement (ARM) Program objectives is to obtain measurements applicable to the development of models for better understanding of radiative processes in the atmosphere. We address this goal by building a three-dimensional (3D) characterization of the cloud structure and properties over the ARM Southern Great Plains (SGP). We take the approach of juxtaposing the cloud properties as retrieved from independent satellite and ground-based retrievals, and looking at the statistics of the cloud field properties. Once these retrievals are well understood, they will be used to populate the 3D characterization database. As a first step we determine the relationship between surface fractional sky cover and satellite viewing angle dependent cloud fraction (CF). We elaborate on the agreement intercomparing optical depth (OD) datasets from satellite and ground using available retrieval algorithms with relation to the CF, cloud height, multi-layer cloud presence, and solar zenith angle (SZA). For the SGP Central Facility, where output from the active remote sensing cloud layer (ARSCL) valueadded product (VAP) is available, we study the uncertainty of satellite estimated cloud heights and evaluate the impact of this uncertainty for radiative studies.
Genkova, I.↗
Analysis of Water Extraction From Lunar Regolith
Distribution of water concentration on the Moon is currently an area of active research. Recent studies suggest the presence of ice particles, and perhaps even ice blocks and ice-cemented regolith on the Moon. Thermal extraction of the in-situ water is an attractive means of sa tisfying water requirements for a lunar mission. In this paper, a model is presented to analyze the processes occurring during the heat-up of icy regolith and extraction of the evolved water vapor. The wet regolith is assumed to be present in an initially evacuated and sealed cell which is subsequently heated. The first step of the analysis invol ves calculating the gradual increase of vapor pressure in the closed cell as the temperature is raised. Then, in the second step, the cell is evacuated to low pressure (e.g., vacuum), allowing the water vapor to leave the cell and be captured. The parameters affecting water vap or pressure build-up and evacuation for the purpose of extracting water from lunar regolith are discussed in the paper. Some comparisons wi th available experimental measurements are also made.
Hegde, U.↗
Multispacecraft Observations and Modeling of the 22/23 June 2015 Geomagnetic Storm
The magnetic storm of 22-23 June 2015 was one of the largest in the current solar cycle. We present in situ observations from the Magnetospheric Multiscale Mission (MMS) and the Van Allen Probes (VAP) in the magnetotail, field-aligned currents from AMPERE (Active Magnetosphere and Planetary Electrodynamics Response), and ionospheric flow data from Defense Meteorological Satellite Program (DMSP). Our real-time space weather alert system sent out a "red alert," correctly predicting Kp indices greater than 8. We show strong outflow of ionospheric oxygen, dipolarizations in the MMS magnetometer data, and dropouts in the particle fluxes seen by the MMS Fast Plasma Instrument suite. At ionospheric altitudes, the AMPERE data show highly variable currents exceeding 20 MA. We present numerical simulations with the Block Adaptive Tree-Solarwind - Roe - Upwind Scheme (BATS-R-US) global magnetohydrodynamic model linked with the Rice Convection Model. The model predicted the magnitude of the dipolarizations, and varying polar cap convection patterns, which were confirmed by DMSP measurements.
Reiff, P. H.↗
Value Added Products for SeaWinds on QuikSCAT
The SeaWinds scatterometer generates near real-time surface wind data over the global oceans. Daily wind data is available to the public via the VAP website.
SeaWinds↗
Energetic Ion Variations During Substorm Intervals Using the Van Allen Probes Data
The study investigates ion flux variations for the substorms in the inner magnetosphere. The effect of substorm-induced magnetic field dipolarization on the O + and H + ion flux is analysed for 22 events from the year 2018 using the Helium, Oxygen, Proton, and Electron (HOPE) Mass Spectrometer data on board the Van Allen Probes (VAP/RBSP) satellite. The clear dipolarization signatures are observed using the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) suite from the VAP. These observations provide evidence of the ion flux enhancement at 1–50 keV energy after the substorm onset, in particular, the energy range of 20–50 shows dominance. The typical characteristics of magnetic field dipolarization and its time scales are investigated. It is found that the O + ion flux enhances greater in magnitude than H + ion flux at energies 20–50 keV after the substorm onset. In addition to it, the correlation of these enhanced oxygen fluxes with the related interplanetary magnetic field (IMF) B z , solar wind velocity (V sw ), and the auroral electrojet (AE) index are found good. The new results reports that the ion flux variation ratio shows the MLT dependence for different energy ranges and found that the peak of the O + / H + ion flux ratio centred near midnight for the energy range of 8–20 keV, whereas the O + / H + ion flux ratio of the energy 20–50 keV is high within the post-midnight. The possible mechanisms for the enhanced ion flux are discussed.
Trunali Shah↗