DWD Pilotstation – Evaluating ground-based remote sensing systems for future observing networks
Bibliographic record
Abstract
The latest generation of active and passive ground-based remote sensing instruments, often called “profilers”, has shown its potential for continuous and high-resolution measurements of thermodynamic and kinematic vertical profiles as well as particle-related profiles. It is precisely these observations of the atmospheric boundary layer that are increasingly needed to improve the forecast quality of high-resolution numerical weather prediction (NWP) and nowcasting. For this purpose, the DWD has initiated the project “Pilotstation” to evaluate options for a qualitative network expansion with suitable surface remote sensing profilers. Currently, we assess the following profilers in a dedicated testbed at Lindenberg Observatory: Doppler lidar, microwave radiometer, water vapor broadband-DIAL, and cloud radar. Furthermore, we plan to evaluate a compact Raman lidar in the future. At DWD, the assessment of candidate systems takes place holistically focusing on all aspects of instrument reliability, operational sustainability, data quality, and on the potential benefit for the NWP using assimilation experiments. This implies efforts to standardize data processing steps and data formats, the development of software tools to support network operations and the proper integration of the observations in the data assimilation system. After the initial testing and evaluation at the Lindenberg Observatory, a suite of instruments will be installed at the weather station in Aachen-Orsbach to enable an end-to-end testing in an operational framework. We give an overview of the ongoing project and present results regarding the various aspects: operations and sustainability, data quality and assimilation tests for the different testbed instruments and observations. This contribution complements the efforts of network development for future operational use within the frame of the EUMETNET's E-PROFILE observations program and the COST action PROBE.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".