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Record W2785895951 · doi:10.1061/40976(316)390

Radar Visualization and Data Exporter Tools to Support Interoperability and the Global Earth Observation System of Systems (GEOSS), from the National Oceanographic and Atmospheric Administration's (NOAA's) National Climatic Data Center (NCDC)

2008· article· en· W2785895951 on OpenAlexaboutno aff
S. Del Greco, Steven Ansari

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2008 · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceInteroperabilityRadarRemote sensingGeospatial analysisData managementData centerVisualizationWeather radarMeteorologyDatabaseWorld Wide WebTelecommunicationsGeographyData miningOperating system

Abstract

fetched live from OpenAlex

In February 2005, 61 countries around the World agreed on a 10 year plan to build open systems for sharing geospatial data and services across different platforms worldwide. This system is known as the Global Earth Observation System of Systems (GEOSS). The objective of GEOSS focuses on easy access to environmental data and interoperability across different systems allowing participating countries to measure the "pulse" of the planet in an effort to advance society. In support of GEOSS goals, NOAA's National Climatic Data Center (NCDC) has developed radar visualization and data exporter tools using Open Source software libraries. The NCDC Interactive Radar Viewer and Data Exporter Toolkit load Weather Surveillance Radar 1988 Doppler (WSR-88D) volume scan (S-band) data, known as Level-II, and derived products, known as Level-III, into an OPEN Geographical Information System (GIS) compliant environment. The application is launched via Java Web Start and runs on the client machine while accessing these data remotely from the NCDC archive or in near real time from other NOAA servers. The Radar Interactive Viewer Toolkit provides custom data overlays, animations and basic queries. The export of images and movies is provided in multiple formats that support the "blending" of radar data with other types of data. The Data Exporter allows for data export in both vector polygon (Shapefile, Well-Known Text) and raster (GeoTIFF, ESRI Grid, VTK, NetCDF, GrADS) formats. NCDC recently decoded Sigmet C-band doppler radar data and plans to implement the functionality to read C-Band radar data into the Radar Viewer/Data Exporter Toolkit. This supports a bilateral agreement between the United States and Canada for data sharing and interoperability. In addition, the NCDC is implementing decoders to read a test bed of distributed X- band radars that are funded through the Collaborative Adaptive Sensing of the Atmosphere (CASA) project. This paper describes in further detail the NCDC Radar Visualization and Data Exporter Tools, and the NCDC hopes to establish collaboration with scientists participating in GEOSS to leverage these tools for interoperable use with other global radar networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.245
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2008
Admission routes1
Has abstractyes

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