Adjoint Sensitivity of Surface Precipitation to Initial Conditions
Bibliographic record
Abstract
Abstract The adjoint version of the Global Environmental Multiscale model including a comprehensive package of simplified and linearized physical processes (large-scale condensation, deep moist convection, vertical diffusion, and subgrid-scale orographic effects) is used to evaluate the sensitivity of surface precipitation to initial conditions for up to 24 h for two meteorological systems: a midlatitude front and a tropical cyclone. Such diagnostics are useful to improve the understanding on variational assimilation of precipitation data. In agreement with a similar study, the largest sensitivity is found with respect to the temperature field for both stratiform and convective precipitation. Close to the observation time and for stratiform precipitation, the sensitivity with respect to specific humidity is rather large, which corroborates conclusions from previous one-dimensional variational data assimilation experimentations. The sensitivity is then reduced significantly after the observation time. The sensitivities of surface precipitation to the wind components and to specific humidity are comparable and are at a maximum at the observation time. The sensitivity to the surface pressure is always much smaller than the sensitivity to the other variables. In general, sensitivities are largest at the observation time and then decrease. However, for the midlatitude perturbation, the sensitivity is enhanced after 12 h for stratiform precipitation and also for convective precipitation using a scheme based on the moisture convergence closure. This results from a dynamical coupling upstream of the area of interest through baroclinic instability as evidenced by vertically backward-tilted sensitivities. Such enhancement is not observed for the tropical case. The tangent-linear approximation remains acceptable for accumulated precipitation up to 24 h but is rather poor for instantaneous rain rates. The variational assimilation of accumulated precipitation should thus be favored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".