IMERG in the Canadian Precipitation Analysis (CaPA) System for Winter Applications
Bibliographic record
Abstract
Several configurations of the Canadian Precipitation Analysis system (CaPA) currently produce precipitation analyses at Environment and Climate Change Canada (ECCC). To improve CaPA’s performance during the winter season, the impact of assimilating the IMERG V06 product (IMERG: Integrated Multi-satellitE Retrievals for GPM—Global Precipitation Measurement mission) into CaPA is examined in this study. Tests are conducted with CaPA’s 10 km deterministic version, evaluated over Canada and the northern part of the United States (USA). Maps from a case study show that IMERG plays a contradictory role in the production of CaPA’s precipitation analyses for a synoptic-scale winter storm over North America’s eastern coast. While its contribution appears to be physically correct over southern portions of the meteorological system, and early in its intensification phase, IMERG displays unrealistic spatial structures over land later in the system’s life cycle when it is located over northern (colder) areas. Objective evaluation of CaPA’s analyses when IMERG is assimilated without any restrictions shows an overall decrease in precipitation, which has a mixed effect (positive and negative) on the bias indicators. But IMERG’s influence on the Equitable Threat Score (ETS), a measure of CaPA’s analyses accuracy, is clearly negative. Using IMERG’s quality index (QI) to filter out areas where it is less accurate improves CaPA’s objective evaluation, leading to better ETS versus the control experiment in which no IMERG data are assimilated. Several diagnostics provide insight into the nature of IMERG’s contribution to CaPA. For the most successful configuration, with a QI threshold of 0.3, IMERG’s impact is mostly found in the warmer parts of the domain, i.e., in northern US states and in British Columbia. Spatial means of the temporal sums of absolute differences between CaPA’s analyses with and without IMERG indicate that this product also contributes meaningfully over land areas covered by snow, and areas where air temperature is below −2 °C (where precipitation is assumed to be in solid phase).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".