Adaptation Of The Canadian Land Data Assimilation System to Urban Areas
Bibliographic record
Abstract
Recent advances in surface and hydrological forecasting at Environment and Climate Change Canada (ECCC) were achieved through the more precise initialization of the surface state with the Canadian Land Data Assimilation System (CaLDAS). More recently, a satellite-based approach has been developed (Carrera et al. 2015, 2019; Bélair et al. 2023) and already used for daily operation of the ECCC’s National Surface and River Prediction System. A limitation of this system is that it doesn’t consider adequate representation of urban areas, albeit urban areas represent a tiny portion of the Canadian landscape. Such satellite-based surface data assimilation system is likely to be used also in the next generation of short-term weather prediction for a domain covering Canada and the northern part of USA. In this NWP system, detailed physical processes in the urban canopy are represented with the Town Energy Balance TEB urban scheme and the Soil, Vegetation and Snow scheme SVS. In this context, it is important to adapt the land surface data assimilation system to improve coherency and accuracy in urban areas. This study aims to show the impact of the different steps required towards a more realistic representation of urban areas in CaLDAS. The TEB scheme is added in background state in addition to SVS. Then, the surface canopy temperatures are added in the analyzed variables. Preliminary results for 2022 summer show a global improvement of 3-h forecasts of 2-m air temperature, reducing bias up to 0.5°C over large cities. Impacts of the new method on the soil moisture and snow prescription are also investigated. Implementation of such system could greatly improve the initial conditions used by surface-atmosphere coupled systems for weather and environmental forecasts and analysis in urban areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".