A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques
Bibliographic record
Abstract
Abstract Nowcasting is the short-range forecast obtained from the latest observed state. Currently, heuristic techniques, such as Lagrangian extrapolation, are the most commonly used for rainfall forecasting. However, the Lagrangian extrapolation technique does not account for changes in the motion field or growth and decay of precipitation. These errors are difficult to analytically model and are normally introduced by stochastic processes. According to the chaos theory, similar states, also called analogs, evolve in a similar way plus an error related with the predictability of the situation. Consequently, finding these states in a historical dataset provides a way of forecasting that includes all the physical processes such as growth and decay, among others. The difficulty of this approach lies in finding these analogs. In this study, recent radar observations are compared with a 15-yr radar dataset. Similar states within the dataset are selected according to their spatial rainfall patterns, temporal storm evolution, and synoptic patterns to generate ensembles. This ensemble of analog states is verified against observations for four different events. In addition, it is compared with the previously mentioned Lagrangian stochastic ensemble by means of different scores. This comparison shows the weaknesses and strengths of each technique. This could provide critical information for a future hybrid analog–stochastic nowcasting technique.
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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) | 0.001 | 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".