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Record W4283652580 · doi:10.5194/ems2022-119

DWD Pilotstation – Evaluating ground-based remote sensing systems for future observing networks

2022· preprint· en· W4283652580 on OpenAlexaff
Christine Knist, Markus Kayser, Moritz Löffler, Jasmin Vural, Annika Schomburg, Ulrich Görsdorf, Felix Lauermann, Ronny Leinweber, Stefan Klink, Volker Lehmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsSteinbach Bible College
Fundersnot available
KeywordsRemote sensingLidarEnvironmental scienceComputer scienceCeilometerTestbedRadarData assimilationMeteorologyNowcastingTelecommunicationsGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.134
GPT teacher head0.318
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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