AEMET-γSREPS: The Spanish Convection-permitting LAM-EPS on AEMET forecasting offices
Bibliographic record
Abstract
AEMET-γSREPS is a 2.5km multi-NWP models and multi-boundaries LAM-EPS system operating up to 72 hours since 2016 over Iberian Peninsula, Canary Islands and Antarctica Peninsula which is quite extensively used by AEMET forecasting offices. The main γSREPS goal is to improve operationally issued forecasts and warmings with a consistent measure of their predictability.The γSREPS 20 members come up crossing four regional mesoscale non-hydrostatic convection-permitting NWP models: HARMONIE-AROME (ACCORD-HIRLAM), ALARO (ACCORD-ALADIN), WRF-ARW (NCAR-NOAA) and NMMB (NCEP-NOAA); with five Global NWP models’ boundary conditions: ECMWF-IFS, NCEP-GFS, MétéoFrance-ARPÈGE, JMA-GSM (Japanese) and CMC-GEM (Canadian). Multi-model and multi-boundary approaches have been selected to take into account the NWP model and boundary condition uncertainties respectively. The combination of both prove to hold better skill-spread relationship than other EPS techniques based on uni-NWP models, especially for precipitation and/or convective High Impact Weather (HIW) events.γSREPS has a big number of spatial and point products available for forecasting offices through an integrated visualisation framework called PANEL. Interestingly, the latter integrates them with other forecasting systems in AEMET such as deterministic HARMONE-AROME and ECMWF IFS and also its ensemble IFS-ENS, allowing forecasters to carry out a “poor man ensemble” conceptual prediction integration in order to issue the best possible predictions and warnings. Static spatial plots comprise mean, maximum and minimum fields, quartiles, probabilities, member “spaghettis” and EFI/SOT (Extreme Forecast Index). Precipitation “spaghetti” product depicting every member’s contour precipitation in a number of distinct thresholds, are very appreciated in forecasting offices because it allows them to evaluate spatial uncertainty and look for possible extreme events represented only by a few members. Moreover, the new dynamic visualisation based on ADAGUC facilitates zoom up to a very local region for detailed forecasts. For point-based products, a new generation of meteograms (gSREPSgrams) which includes the most extreme single member are produced along the probabilistic vertical-profile product, which shows the most unstable member. Both products try to highlight possible extreme events.The planned foreseeable evolution of AEMET-γSREPS system is to increase the number of members, incorporating more boundary conditions such as the ones from Global ICON, and including more mesoscale NWP models like ICON-LAM and GEM-LAM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".