NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0: Stratospheric Composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS‐CF) provides recent estimates and 5‐day forecasts of atmospheric composition to the public in near‐real time. To do this, the GEOS Earth system model is coupled with the GEOS‐Chem tropospheric‐stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS‐CF system is described, including updates made to the GEOS‐Chem UCX mechanism within GEOS‐CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar, and satellite observations for stratospheric composition, including measurements of ozone (O 3 ) and important nitrogen and chlorine species related to stratospheric O 3 recovery. The GEOS‐CF nudges the stratospheric O 3 toward the GEOS Forward Processing (GEOS FP) assimilated O 3 product; as a result the stratospheric O 3 in the GEOS‐CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS‐CF O 3 forecasts are more realistic than GEOS FP O 3 forecasts because of the inclusion of the complex GEOS‐Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS‐CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases—such as low NO x and HNO 3 in the polar regions and generally low HCl throughout the stratosphere—and future improvements to the chemistry mechanism and emissions are discussed. GEOS‐CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near‐real‐time three‐dimensional gridded information on atmospheric composition.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it