On the Reliability of Surface Observations and the Pitfalls of Verification Against Own Analyses
Bibliographic record
Abstract
ABSTRACT Near‐surface observations can suffer from significant representativeness errors, especially for Numerical Weather Prediction (NWP) at lower resolution in global applications. Therefore, in Data Assimilation (DA), many operational centers have long been reluctant to assimilate them (e.g., the European Center for Medium‐range Weather Forecast, ECMWF, started assimilating all 6‐h screen‐level temperature reports only in 2024). For forecast verification, some studies advocate that we should not rely on them and use only verification against our own near‐surface analyses. At Environment and Climate Change Canada (ECCC), both temperature and humidity observations from SYNOPs have been assimilated in our global NWP system for more than two decades and, in June 2024, METARs have been added following some positive impacts found only when comparing forecasts against near‐surface observations. To shed light on the impact of the assimilation of screen‐level observations, in this study we present an evaluation of the impact of removing the assimilation of all screen‐level temperature and humidity observations using various verification references: the NWP forecasts were evaluated against radiosondes and surface observations, independent (ECMWF) analysis, our own analysis and surface analysis. Results show that, despite the lack of a proper estimation of representativeness errors in the DA approach, the assimilation of screen‐level temperature and humidity leads to forecast improvements that can be detected from the verification against independent measurement sources, here radiosondes and ECMWF upper‐air analyses. Verification against own analyses, for both upper‐air and screen‐level variables, led instead to opposite and misleading conclusions. In fact, the removal of assimilated screen‐level temperature and humidity measurements renders the NWP forecast more similar to the own analysis, therefore leading to better scores but detachment from the observed world.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".