Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products
Bibliographic record
Abstract
• CaPA and GPM (IMERG-E, IMERG-L & IMERG-F) in climatic extremes. • CaPA outperformed all the IMERG products in monitoring Hurricane Dorian & Fiona. • The IMERG-E has broader potential as additional data in CaPA assimilation. Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".