Tongji‐Grace02s and Tongji‐Grace02k: High‐Precision Static GRACE‐Only Global Earth's Gravity Field Models Derived by Refined Data Processing Strategies
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Résumé
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.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle