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Record W2809750982 · doi:10.1029/2018jb015641

Tongji‐Grace02s and Tongji‐Grace02k: High‐Precision Static GRACE‐Only Global Earth's Gravity Field Models Derived by Refined Data Processing Strategies

2018· article· en· W2809750982 on OpenAlex
Qiujie Chen, Yunzhong Shen, Olivier Francis, Wu Chen, Xingfu Zhang, Houze Hsu

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

VenueJournal of Geophysical Research Solid Earth · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
FundersDanmarks Tekniske UniversitetChinese Academy of SciencesState Key Laboratory of Geo-Information EngineeringNational Natural Science Foundation of ChinaTU Graz, Internationale Beziehungen und Mobilitätsprogramme
KeywordsAccelerometerGeopotentialGravitational fieldGeodesyGravitational accelerationCalibrationAccelerationNoise (video)Gravity of EarthPolynomialOrbit determinationMathematicsComputer scienceSatelliteStatisticsPhysicsGeologyMathematical analysisAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract In order to derive high‐precision static Gravity Recovery and Climate Experiment (GRACE)‐only gravity field solutions, the following strategies were implemented in this study: (1) a refined accelerometer calibration model that treats monthly accelerometer scales as a third‐order polynomial and daily accelerometer biases as a fifth‐order polynomial was developed to calibrate accelerometer measurements; (2) the errors of the acceleration and attitude data were estimated together with the geopotential coefficients and accelerometer parameters on the basis of the weighted least squares adjustments; (3) a nearly complete observation series of GRACE mission was used to decrease the condition number of normal equation; and (4) the GRACE data collected in lower orbit altitude were also included to decrease the condition number. Our results show that (1) the refined accelerometer calibration model with much less parameters performs as well as previous methods (i.e., solving daily scales and hourly biases or estimating biases along with bias rates every 2 hr). However, it provides a system of more stable normal equation and less high‐frequency noise in gravity field solutions; (2) high‐frequency noise in the gravity field solution is reduced by modeling the errors of the acceleration and attitude data; (3) the geopotential coefficients at all degrees is greatly enhanced by using longer GRACE time series (especially the data by the end of 2010); and (4) due to lower orbit altitude, the GRACE data collected since 2014 lead to a significant improvement of the gravity field solution as the satellites are more sensitive to higher‐frequency signal. Using the refined strategies, an unconstrained static solution (named Tongji‐Grace02s) up to degree and order 180 was derived. For further suppressing the high‐frequency noise, a regularization strategy based on the Kaula rule is applied to the degrees and orders beyond 80, leading to a regularized model Tongji‐Grace02k. To validate the quality of the derived models, both Tongji‐Grace02s and Tongji‐Grace02k were compared to the latest GRACE‐only models (i.e., GGM05S, ITU_GRACE16, ITSG‐Grace2014s, and ITSG‐Grace2014k) and validated using independent data (i.e., Global Navigation Satellite Systems (GNSS)/Leveling data and DTU13 oceanic gravity data). Compared to other models, much less spatial noise in terms of global gravity anomalies with respect to the state‐of‐the‐art model EIGEN6C4 and far higher accuracy at high degrees are achieved by Tongji‐Grace02s. The same conclusions can be drawn for Tongji‐Grace02k when the same analyses were applied to the regularized solutions ITSG‐Grace2014k and Tongji‐Grace02k. Validations with independent data confirm that Tongji‐Grace02s has the least noise among the unconstrained GRACE‐only models and Tongji‐Grace02k is the one with the best accuracy among the regularized GRACE‐only solutions. For the tests up to degree and order 180 using GNSS/Leveling data, the improvements of Tongji‐Grace02s with respect to ITSG‐Grace2014s reach 13% over Canada and 23% in Mexico. Even better, no less than 58% of improvement is achieved by both Tongji‐Grace02s relative to ITSG‐Grace2014s and Tongji‐Grace02k with respect to ITSG‐Grace2014k in the validation based on DTU13 data.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.366
Teacher spread0.280 · 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