For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part II: On the Observation Signal from Different Sensors
Bibliographic record
Abstract
Abstract The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In Part I, the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".