HUST-Grace2016s: A new GRACE static gravity field model derived from a modified dynamic approach over a 13-year observation period

Zhou, Hao; Luo, Zhicai; Zhou, Zebing; Zhong, Bo; Hsu, Houtse

doi:10.1016/j.asr.2017.04.026

Cited by 23 publications

(23 citation statements)

References 20 publications

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“…The processing strategy has been explicitly described in Zhou et al [12]. The new model has been uploaded to the website of International Centre for Global Earth Models (ICGEM, http://icgem.gfz-potsdam.de/home).…”

Section: Grace Datamentioning

confidence: 99%

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

et al. 2017

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Accurate terrestrial water storage (TWS) estimation is important to evaluate the situation of the water resources over the Yangtze River Basin (YRB). This study exploits the TWS observation from the new temporal gravity field model, HUST-Grace2016 (Huazhong University of Science and Technology), which is developed by a new low-frequency noise processing strategy. A novel GRACE (Gravity Recovery and Climate Experiment) post-processing approach is proposed to enhance the quality of the TWS estimate, and the improved TWS is used to characterize the drought and flood events over the YRB. The HUST-Grace2016-derived TWS presents good agreement with the CSR (Center for Space Research) mascon solution as well as the PCR-GLOBWB (PCRaster Global Water Balance) hydrological model. Particularly, our solution provides remarkable performance in identifying the extreme climate events e.g., flood and drought over the YRB and its sub-basins. The comparison between GRACE-derived TWS variations and the MODIS-derived (Moderate Resolution Imaging Spectroradiometer) inundated area variations is then conducted. The analysis demonstrates that the terrestrial reflectance data can provide an alternative way of cross-comparing and validating TWS information in Poyang Lake and Dongting Lake, with a correlation coefficient of 0.77 and 0.70, respectively. In contrast, the correlation is only 0.10 for Tai Lake, indicating the limitation of cross-comparison between MODIS and GRACE data. In addition, for the first time, the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) vertical velocity data is incorporated with GRACE TWS in the exploration of the climate-induced hydrological activities. The good agreement between non-seasonal NCEP/NCAR vertical velocities and non-seasonal GRACE TWSs is found in flood years

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Section: Grace Datamentioning

confidence: 99%

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

et al. 2017

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“…Zhao et al (2011) have discussed two different low-frequency noise processing strategies, and they concluded that the spherical harmonic coefficients and empirical parameters should be simultaneously determined. Based on this study, we deduced the mathematical calculation formulas of these two different low-frequency noise processing strategies in Zhou et al (2017a), and a new strategy was created to simultaneously filter the design matrix and observation vector of observation equation. Using this new strategy, we have developed a new time series of monthly gravity field models HUST-Grace2016 (Zhou et al 2017b), which have good agreement with CSR Release05, JPL Release05, and GFZ Release05.…”

Section: Sst Data Processingmentioning

confidence: 99%

HUST-GOGRA2018s: A new gravity field model derived from the combination of GRACE and GOCE data

Zhou¹,

Xu²,

Luo³

et al. 2019

Terr. Atmos. Ocean. Sci.

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A new satellite-only gravity field model entitled HUST-GOGRA2018s is developed by the combination of GRACE and GOCE data in this study. The modified dynamic approach is applied for GRACE data processing, while the space-wise least square method with a cascade filter is utilized for GOCE data processing. The GRACE-only model HUST-Grace2016s and GOCE-only model HUST-GOCE2018s are then computed, respectively. Our new developed GRACE-only model HUST-Grace2016s performs better than AIUB-GRACE03S, GGM05S, Tongji-GRACE01S at higher degrees, and the quality of our GOCE-only model HUST-GOCE2018s is also better than that of GO_CONS_GCF_2_TIM_R2 and GO_CONS_GCF_2_SPW_ R2. The combination is subsequently implemented by the superposition of GRACE and GOCE full normal equations. During the combination, the optimal weight is determined by the least-squares combined adjustment method with parametric covariance approach (LS-PCA) and the spectral combination method, respectively. The comparison result demonstrates that LS-PCA is more proper for the combination. As a result, the final HUST-GOGRA2018s model is developed. Validated by external gravity field models, the results demonstrate that the HUST-GOGRA2018s is dominated by GRACE data for the spherical harmonic coefficients lower than degree 60 and GOCE data for the spherical harmonic coefficients higher than degree 150, and its performance is better than that of GOCO01S.

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“…Hence, different noise modeling strategies were applied by various research centers. To account for the effects of the frequency‐dependent noise in the observations on gravity field modeling, empirical parameters are generally introduced (Liu et al, ; Zhao et al, ; Zhou et al, ). The frequency‐dependent noise can also be suppressed by frequency‐dependent data weighting (FDDW) techniques (Farahani et al, ; Guo et al, ; Klees & Ditmar, ).…”

Section: Introductionmentioning

confidence: 99%

An Optimized Short‐Arc Approach: Methodology and Application to Develop Refined Time Series of Tongji‐Grace2018 GRACE Monthly Solutions

Chen

Shen

et al. 2019

JGR Solid Earth

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Considering the unstable inversion of ill‐conditioned intermediate matrix required in each integral arc in the short‐arc approach presented in Chen et al. (2015, https://doi.org/10.1002/2014JB011470), an optimized short‐arc method via stabilizing the inversion is proposed. To account for frequency‐dependent noise in observations, a noise whitening technique is implemented in the optimized short‐arc approach. Our study shows that the optimized short‐arc method is able to stabilize the inversion and eventually prolong the arc length to 6 hr. In addition, the noise whitening method is able to mitigate the impacts of low‐frequency noise in observations. Using the optimized short‐arc approach, a refined time series of Gravity Recovery and Climate Experiment (GRACE) monthly models called Tongji‐Grace2018 has been developed. The analyses allow us to derive the following conclusions: (a) During the analyses over the river basins (i.e., Amazon, Mississippi, Irrawaddy, and Taz) and Greenland, the correlation coefficients of mass changes between Tongji‐Grace2018 and others (i.e., CSR RL06, GFZ RL06, and JPL RL06 Mascon) are all over 92% and the corresponding amplitudes are comparable; (b) the signals of Tongji‐Grace2018 agree well with those of CSR RL06, GFZ RL06, ITSG‐Grace2018, and JPL RL06 Mascon, while Tongji‐Grace2018 and ITSG‐Grace2018 are less noisy than CSR RL06 and GFZ RL06; (c) clearer global mass change trend and less striping noise over oceans can be observed in Tongji‐Grace2018 even only using decorrelation filtering; and (d) for the tests over Sahara, over 36% and 19% of noise reductions are achieved by Tongji‐Grace2018 relative to CSR RL06 in the cases of using decorrelation filtering and combined filtering, respectively.

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HUST-Grace2016s: A new GRACE static gravity field model derived from a modified dynamic approach over a 13-year observation period

Cited by 23 publications

References 20 publications

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

HUST-GOGRA2018s: A new gravity field model derived from the combination of GRACE and GOCE data

An Optimized Short‐Arc Approach: Methodology and Application to Develop Refined Time Series of Tongji‐Grace2018 GRACE Monthly Solutions

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