On the Filtering Properties of Ensemble Averaging for Storm-Scale Precipitation Forecasts
Bibliographic record
Abstract
Abstract The mean (ENM) of an ensemble of precipitation forecasts is generally more skillful than any of the members as verified against observations. A major reason is that the averaging filters out nonpredictable features on which the members disagree. Previous research showed that the nonpredictable features occur at small scales, in both numerical forecasts and Lagrangian persistence nowcasts. Hence, it is plausible that the unpredictable features filtered through ensemble averaging would also occur at small scales. In this study, the exact range of scales affected by averaging is determined by comparing the statistical properties of precipitation fields between the ENM and the individual members from a Storm-Scale Ensemble Forecasting (SSEF) system run during NOAA’s 2008 Hazardous Weather Testbed (HWT) Spring Experiment. The filtering effect of ensemble averaging results in a low-intensity bias for the ENM forecasts. It has been previously proposed to correct the ENM forecasts by recalibrating the intensities in the ENM using the probability density function (PDF) of rainfall values from the ensemble members. This procedure, probability matching (PM), leads to a new ensemble mean, the probability matched mean (PMM). Past studies have shown that the PMM appears more realistic and yields better skill as evaluated using traditional scores. However, the authors demonstrate here that despite the PMM having the same PDF of rainfall intensities as the ensemble members, the spectral structure and the spatial distribution of the precipitation field differs from that of the members. It is the lesser variability of the PMM fields at small scales that causes the better scores of the PMM relative to the ensemble members.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".