Comparison of real‐time refractivity measurements by radar with automatic weather stations, AROME‐WMED and WRF forecast simulations during SOP1 of the HyMeX campaign
Bibliographic record
Abstract
Weather radars, originally designed to detect and quantify precipitation, can be used to estimate and map the refractivity at low levels, a proxy for humidity. As highlighted by previous studies, this presents a definite meteorological interest, both for numerical weather prediction and for atmospheric process studies. Recent works have given keys to performing high‐accuracy measurements with operational radar without decreasing the quality of reflectivity and Doppler wind classical measurements, and the retrieval of radar refractivity is now performed in real time with the Météo‐France Application Radar à la Météorologie Infra‐Synoptique (ARAMIS) operational network. Taking advantage of the Hydrological cycle in Mediterranean eXperiment (HyMeX) field campaign (September–November 2012), refractivity measured by a few radars located in southeast France has been compared with in situ measurement by Automatic Weather Stations (AWSs), and correlation between these two independent observations is quite good, in particular giving high quality for the diurnal cycle and during the pre‐convection period measurement by the radar. To go further in the evaluation of the usefulness of such a product, we compared refractivity derived from radar measurements and from two different kinds of model: the numerical prediction model Applications de la Recherche à l'Opérationnel à Méso‐Echelle – Western MEDiterranean (AROME‐WMED) using a 2.5 km resolution grid mesh over southern France and a coarser resolution simulation (54 × 54 km) performed with the Weather Research and Forecasting (WRF) model, which both ran in a forecasting mode during the HyMeX Special Observing Period 1. These two models give access to the variability of the modelling and thus enable us to quantify the uncertainty of the refractivity modelling. The result of this comparison is generally fairly good, with a pattern of refractivity field similar to observations (AWS and radar), although obviously with locally strong differences. We finally illustrate the usefulness of refractivity mapping by radar by investigating a typical meteorological situation of a convective system observed during the HyMeX campaign.
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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) | 0.000 | 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) | 0.001 | 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 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".