Re-Introduction of ERS-2 Scatterometer Data in the Operational ECMWF Assimilation System
Bibliographic record
Abstract
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".