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Record W2149534362

Re-Introduction of ERS-2 Scatterometer Data in the Operational ECMWF Assimilation System

2005· article· en· W2149534362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueESASP · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsnot available
Fundersnot available
KeywordsScatterometerEnvironmental scienceData assimilationMeteorologyBuoyWind speedClimatologyNumerical weather predictionGeographyGeologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT At the European Centre for Medium-Range WeatherForecasts (ECMWF), scatterometer data from the ERS1/2 platformshavebeensuccessfully assimilated betweenJanuary 1996 up to an ERS-2 on-board failure in Jan-uary 2001. This surface vector wind product over theglobal oceans had proven to give a positive impact onthe ECMWF forecast skill in general, and the analysis oftropical cyclones in specific.On 21 August 2003 ERS-2 scatterometer data was pub-licly re-distributed by ESA. Off-line experiments per-formed at ECMWF, confirmed a positive impact, wherethe wind product was based on the improved geophysicalmodel function CMOD5. Scatterometerdata from ERS-2were re-introduced in the ECMWF operational assimila-tion system on 9 March 2004.Key words: data assimilation; scatterometry. 1. INTRODUCTION Surface wind observations over the oceans are needed fora wide range of meteorological and oceanographic ap-plications. High quality surface winds are required todrive ocean circulation models and surface wave models.Knowledge of surface winds is also essential to calculatemomentum, heat and moisture fluxes. Furthermore, sur-face wind data have the potential to provide unique andvaluable information on the initial condition for numeri-cal weather prediction.Conventional surface wind observations from buoys andships are important components of the global observingsystem, but are limited in coverage. Buoy wind observa-tions have high accuracy but sparse coverage as they aremostly located in coastal areas. Ships only cover limitedregions, tend to avoid the worst weather and their obser-vations have at times poor accuracy.Space-borne scatterometer data obtained from theADEOS I and II, QuikSCAT, and ERS 1/2 missions, arefound to be of consistent high quality in comparison toother surface wind observations. Scatterometer data fromthe Active Microwave Instrument (AMI) aboard the ERSplatforms have been assimilated successfully at variousweather centres(e.g., theUK Met Office, CMC (Canada),ECMWF, KNMI (The Netherlands), DNMI (Norway)).The AMI instrument is a C-band (5.7 cm wavelength)active backscatter radar instrument (Attema 1991). Dueto the choice of wavelength rain and clouds do not con-taminate the observations. Therefore, ERS is able to de-liver wind measurements even near tropical cyclones andextra-tropical lows whereas temperature and humidity in-formation from satellite sounding instruments is unreli-able due to cloud and precipitation effects.ECMWF has a long experience with the usage of scat-terometer data. Scatterometer data from ERS-2 and itspredecessor ERS-1 have been successfully assimilatedfrom January 1996 up to an ERS-2 on-board failure inJanuary 2001. Indeed, it had shown to improve globalforecast scores in general and the analysis of tropical cy-clones in specific (IsaksenandJanssen, 2004,IsaksenandStoffelen 2000). The four-dimensional variational assim-ilation system (4D-Var) at ECMWF allows for a dynam-ically consistent use of observations. In this way, infor-mation of the scatterometer surface winds is propagatedto the entire troposphere (Thepaut´

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.514
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.235
Teacher spread0.207 · 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