A L1 transformational operator for the objective evaluation of the EarthCARE Cloud Profiling Radar data products
Bibliographic record
Abstract
The value of permanent, multi-sensor surface-based observatories that collect continuous long-term observations for satellite L2 data products has grown significantly the last 10-15 years. Examples of such established surface-based networks include: The Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) network, the US Department of Energy Atmospheric Radiation Measurements (ARM) observatories and the recently established 94-GHz Miniature Network for EarthCARE Reference Measurements (FRM4Radar). At the same time, there is a significant increase in the availability of airborne platforms (e.g., DLR Halo, French Falcon and the NASA airborne program) with comprehensive instrument payloads that mimic the satellite primary measurements. Here, a simple L1 transformational operator that can convert L1 suborbital (surface-based or airborne) measurements to the EarthCARE CPR L1 observations is described. The L1 transformational operator ensures that the orbital-suborbital comparison accounts for differences in the sampling geometry, measurement uncertainty, and instrument sensitivity. Furthermore, the operator account for the impact of the surface echo on satellite-based radar observations. Examples of the application of the operator on surface-based observations measurement from the ESA FMR4Radar network are presented. Such long-time data sets are the optimal foundation for a statistical analysis of the CPR performance. The analysis will emphasis on clouds and precipitation processes near ground. In addition, it is show how important ground-based networks are that they can play an important role in the evaluation of future CPR satellite missions. The L1 transformational operator can be easily expanded other spaceborne radar systems. Our plans include the application of the L1 transformation operator to high resolution cloud resolving model output.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".