Atmospheric sounding using GPS radio occultation
Bibliographic record
Abstract
This paper summarizes the radio occultation (RO) technique for remote sounding of the Earth’s atmosphere using the Global Positioning System (GPS) satellites and GPS receivers on low-Earth orbiting (LEO) satellites. As the LEO satellites rise and set with respect to the GPS satellites, the radio waves from the GPS satellites are refracted by the Earth’s atmosphere. Precise measurements of the bending angle of the radio waves are used to derive vertical profiles of atmospheric refractivity, which is a function of electron density in the ionosphere and temperature and water vapor in the stratosphere and troposphere. Results from the GPS/MET, CHAMP, and SAC-C RO missions are summarized, and examples of soundings are presented. Analysis of the CHAMP and SAC-C data indicates that approximately 45% of CHAMP and SAC-C retrieved radio occultation profiles reach below 1 km altitude, compared to only 35% for GPS/MET. All missions exhibit a negative refractivity bias in the lower troposphere of between 1% and 2% compared to NWP models. When constrained to the tropics, only about 20% of the CHAMP occultation profiles reach 1 km from the surface. Taiwan’s National Space Program Office (NSPO), the University Corporation for Atmospheric Research (UCAR), the Jet Propulsion Laboratory (JPL), the Naval Research Laboratory (NRL), and many partners in the university community are developing COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate), a follow-on project for weather and climate, space weather, and geodetic science. COSMIC plans to launch six satellites in 2005. Each satellite will retrieve 400-500 daily profiles† of key ionospheric and atmospheric properties from the tracked GPS radio signals as they are occulted behind the Earth limb. The radio occultation sounding data from COSMIC will contribute significantly to atmospheric research, weather forecasting, and climate modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".