Supplimentary Data: Representativity of Cloud-Profiling Radar Observations for Data Assimilation in Numerical Weather Prediction
Bibliographic record
Abstract
The data published here were used in the research paper titled "Representativity of Cloud-Profiling Radar Observations for Data Assimilation in Numerical Weather Prediction". The atmospheric data were simulated with Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) NWP model (Côté et al. 1998; Girard et al. 1998; Milbrandt et al. 2016) for the purpose of testing cloud and aerosol retrieval algorithms for the EarthCARE satellite mission (Illingworth et al. 2015). The horizontal grid-spacing of the model is 0.25 km with 57 vertical levels. Two frames are available (Halifax and Pacific) with a size of 200 x 6200 km for each frame. In addition to the data from GEM, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP ) produced 94-GHz reflectivities commensurate with CloudSat’s CPR for each of GEM’s 0.25 km columns (Haynes et al. 2007; Bodas-Salcedo et al. 2011). Due to the low frequency of radiative transfer calculation in GEM, the top of atmosphere (TOA) upward flux were recalculated with RRTMG radiative transfer model (Clough et al. 2005) based on the atmospheric properties from GEM's simulation. The data are stored in netCDF format. The available variables are: 2D cloud mask: vertically integrated cloud mask; 3D cloud mask: cloud mask for each model level; Flux: includes the top of atmosphere upward VIS and IR flux (W m-2); Height: height (m) for each model level; Latitude: latitude (deg) for horizontal grids; Longitude: longitude (deg) for horizontal grids; Reflectivity: COSP simulated radar reflectivity. The vertical heights are different form the vertical levels of original GEM simulation. Please refer to the variable "altitude" within the radar reflectivity netCDF file for more details.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.056 |
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".