Performance of 4D‐Var <scp>NWP</scp>‐based nowcasting of precipitation at the Met Office for summer 2012
Bibliographic record
Abstract
The Met Office has developed and demonstrated an hourly cycling 1.5 km resolution NWP‐based nowcast system (0–6 h forecasts) using four‐dimensional variational data assimilation (4D‐Var). This was known as the Nowcasting Demonstration Project (NDP), and was principally for prediction of convective storms for flood forecasting. The NDP was run in real time from March 2012 to April 2013 to cover the period of the London Olympics 2012. The system was run on a domain covering southern England and Wales nested in the UK variable resolution model (UKV). The UKV used a UK‐wide 1.5 km domain with 3 hourly cycling three‐dimensional variational data assimilation (3D‐Var) and produced 36 h forecasts every 6 h. The NDP 4D‐Var included standard observations, Doppler radar radial winds, humidity derived from a 3D cloud cover analysis and geostationary satellite upper‐tropospheric water vapour radiances not contaminated by cloud. This was used in combination with latent heat nudging of radar‐derived precipitation rates. Example case‐studies compare the NDP precipitation forecasts to both the operational extrapolation/merged nowcast system and the UKV forecasts. Objective comparison of fraction skill score for the period June to August 2012 shows that the NDP skill was greater than the latest UKV forecasts, available to forecasters at the same time as the NDP, for the whole 6 h forecast period. The skill of the NDP was greater than the operational extrapolation/merged nowcast beyond T + 2 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".