Postprocessing Model-Predicted Rainfall Fields in the Spectral Domain Using Phase Information from Radar Observations
Bibliographic record
Abstract
Abstract In an attempt to combine the short-term skill of radar nowcasting and the long-term skill of numerical models, successive 15-min rainfall accumulations obtained from the U.S. national radar composites and predicted by the Weather Research and Forecasting (WRF) model are corrected by adjusting the phases of Fourier components in the spectral domain while keeping the amplitudes invariant. The phase information is first obtained by decomposing the radar and model fields into Fourier space. Then, to correct the model outputs, a new set of Fourier components is constructed using the phases from the radar image and amplitudes from the model image. The corrected model image can be obtained by inverse transformation of the new set of Fourier components. Scales of wavelengths starting from 50 to 500 km are playing a crucial role in correcting the positional and intensity errors of continental-scale precipitating systems predicted by the WRF model, while scales greater than 500 km are contributing very little, which is negligible. The model errors are characterized in terms of phase and power. Compared to power/amplitude correction, phase correction plays a major role in eliminating the positional and intensity errors. Scales that are responsible for the errors or not predicted by the model have a decorrelation time of about 2 h in both the zonal and meridional directions. The higher critical success index (CSI) and smaller RMS error values of the radar-extrapolated and phase-corrected fields compared with the WRF model–predicted fields indicate that the extrapolated phase information can be used to correct the model outputs up to a lead time of about 4 h.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".