Bibliographic record
Abstract
Abstract. Initial conditions of current numerical weather prediction systems insufficiently represent the sharp vertical gradients across the midlatitude tropopause. Data assimilation may provide a means to improve the tropopause structure by correcting the erroneous background forecast towards the observations. In this paper, the influence of assimilating radiosonde observations on the tropopause structure, i.e., the sharpness and altitude, is investigated in the ECMWF IFS. We evaluate 9729 midlatitude radiosondes launched during one month in autumn 2016. About 500 of these radiosondes, launched on request during the North Atlantic Waveguide Downstream impact Experiment (NAWDEX) field campaign, were used to set up an observing system experiment (OSE). The OSE comprises two cycled assimilation forecast experiments, one with and one without the non-operational soundings. The influence on the tropopause is assessed in a statistical, tropopause–relative evaluation of observation departures of temperature, static stability (N2), wind speed and wind shear from the background forecast and the analysis. The background temperature is overestimated at the tropopause (warm bias, ~1 K) and underestimated in the lower stratosphere (cold bias, −0.3 K) leading to an underestimation of the abrupt vertical increase of N2 at the tropopause. We show that the increments (differences of analysis and background) reduce background biases and improve the tropopause sharpness. Profiles with sharper tropopause exhibit stronger background biases and, in turn, an increased positive influence of the observations on temperature and N2 in the analysis. Wind speed is underestimated in the background, especially in the upper troposphere (~1 m s−1), but the assimilation improves the wind profile. For the strongest winds the background bias is roughly halved. The positive influence on the analysis wind distribution is associated with an increase of vertical wind speed shear, which is underestimated above the tropopause. In addition to the tropopause sharpening, we detect a shift of the analysis tropopause altitude towards the observations. The comparison of the OSE runs highlights that the main contribution to the tropopause sharpening can be attributed to the radiosondes. This study shows that data assimilation improves wind and temperature gradients across the tropopause, but the sharpening is small compared to the model biases. Hence, the analysis still systematically underestimates the tropopause sharpness which may negatively impact weather and climate forecasts.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.211 | 0.166 |
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".