Impact of analyses on the dynamical balance of global and limited‐area atmospheric models
Bibliographic record
Abstract
Dynamical imbalances can induce spurious variability which can be diagnosed from the physical tendencies observed in the first moments of short‐term forecasts using as initial conditions analyses obtained from an assimilation system using this model. In this article this approach is taken to investigate differences in the balance obtained from 3D‐ and 4D‐Var analyses, using the forecast–assimilation system of the Meteorological Service of Canada (MSC). The results indicate that the model is then in good balance globally but the 4D‐Var analyses slightly upset the balance in the Tropics, thereby altering the characteristics of the Intertropical Convergence Zone (ITCZ). As the assimilation is driven by a particular model, the resulting analyses keep an imprint of the dynamics of that model and use of this analysis with another model may not be as well in balance due to the differences between the two models. To study this point, ERA‐Interim 4D‐Var reanalyses were used as initial conditions first at a lower horizontal and vertical resolution, and then at a resolution closer to that of the Global Environmental Multiscale (GEM) model. The higher‐resolution reanalyses led to a better balance than that with a lower‐resolution version of the ERA‐Interim reanalyses. The coarser analyses create significant imbalances in the Canadian global model which persist for more than 5 days. In particular, it was noted that convection is nearly absent early on as if at a lower resolution, the ERA‐interim analyses did not inject sufficient humidity to trigger convection. It was also noted that reducing the vertical resolution is more damaging than using a coarser horizontal resolution. In limited‐area regional climate models, external analyses are used to define the boundary conditions and the Canadian Regional Climate Model (CRCM) was used to assess the impact of different ways to define the boundary conditions. The CRCM is a limited‐area configuration of the GEM global model used in the 3D‐ and 4D‐Var assimilation. Experiments were conducted in which the boundary conditions driving the CRCM are provided every 6 h as is usually done for the CRCM climate simulations. When using 4D‐Var analyses and ERA‐Interim reanalyses (coarse and full resolution) to define the boundary conditions, the results indicate that imbalances persist even after 15 days and are more significant for the coarser analyses. Moreover, even though the model exhibits relatively good balance initially, after 5 days imbalances appear gradually in the interior of the regional model domain.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".