Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4691-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-4691-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Sep 2025
Data description paper |
| 23 Sep 2025
| 23 Sep 2025
CRA-LICOM: a global high-frequency atmospheric and oceanic temporal gravity field product (2002–2024)
Fan Yang
Geodesy Group, Department of Sustainability and Planning, Aalborg University, Aalborg 9000, Denmark
School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Hailong Liu
Laoshan Laboratory, Qingdao 266237, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Weihang Zhang
School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
Yi Wu
School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
Shuhao Liu
School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
Chunxiang Shi
National Meteorological Information Center, China Meteorological Administration (CMA), Beijing 100081, China
Tao Zhang
National Meteorological Information Center, China Meteorological Administration (CMA), Beijing 100081, China
Min Zhong
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
Zitong Zhu
State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan 430077, China
Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Leibniz Universität Hannover, Hannover 30167, Germany
Changqing Wang
State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan 430077, China
Ehsan Forootan
Geodesy Group, Department of Sustainability and Planning, Aalborg University, Aalborg 9000, Denmark
Jiangfeng Yu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Zipeng Yu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yun Xiao
Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China
Related authors
Fan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev., 18, 6195–6217, https://doi.org/10.5194/gmd-18-6195-2025, https://doi.org/10.5194/gmd-18-6195-2025, 2025
Short summary
Short summary
Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.
Fan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev., 18, 6195–6217, https://doi.org/10.5194/gmd-18-6195-2025, https://doi.org/10.5194/gmd-18-6195-2025, 2025
Short summary
Short summary
Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
Short summary
Short summary
Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Bin Liu, Zhenghui Xie, Shuang Liu, Yujing Zeng, Ruichao Li, Longhuan Wang, Yan Wang, Binghao Jia, Peihua Qin, Si Chen, Jinbo Xie, and ChunXiang Shi
Hydrol. Earth Syst. Sci., 25, 387–400, https://doi.org/10.5194/hess-25-387-2021, https://doi.org/10.5194/hess-25-387-2021, 2021
Short summary
Short summary
We implemented both urban water use schemes in a model (Weather Research and Forecasting model) and assessed their cooling effects with different amounts of water in different parts of the city (center, suburbs, and rural areas) for both road sprinkling and urban irrigation by model simulation. Then, we developed an optimization scheme to find out the optimal water use strategies for mitigating high urban temperatures.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Cited articles
Avery, S., Vincent, R., Phillips, A., Manson, A., and Fraser, G.: High-latitude tidal behavior in the mesosphere and lower thermosphere, J. Atmos. Terr. Phys., 51, 595–608, https://doi.org/10.1016/0021-9169(89)90057-3, 1989. a
Bonin, J. A. and Save, H.: Evaluation of sub-monthly oceanographic signal in GRACE “daily” swath series using altimetry, Ocean Sci., 16, 423–434, https://doi.org/10.5194/os-16-423-2020, 2020. a, b
Boy, J.-P. and Chao, B. F.: Precise evaluation of atmospheric loading effects on Earth's time-variable gravity field, J. Geophys. Res.-Sol. Ea., 110, https://doi.org/10.1029/2002JB002333, 2005. a, b
Boy, J.-P., Gegout, P., and Hinderer, J.: Reduction of surface gravity data from global atmospheric pressure loading, Geophys. J. Int., 149, 534–545, https://doi.org/10.1046/j.1365-246X.2002.01667.x, 2002. a
Boy, J.-P., Longuevergne, L., Boudin, F., Jacob, T., Lyard, F., Llubes, M., Florsch, N., and Esnoult, M.-F.: Modelling atmospheric and induced non-tidal oceanic loading contributions to surface gravity and tilt measurements, J. Geodyn., 48, 182–188, https://doi.org/10.1016/j.jog.2009.09.022, 2009. a
Canuto, V., Howard, A., Cheng, Y., and Dubovikov, M.: Ocean turbulence. Part I: One-point closure model – Momentum and heat vertical diffusivities, J. Phys. Oceanogr., 31, 1413–1426, https://doi.org/10.1175/1520-0485(2002)032<0240:OTPIVD>2.0.CO;2, 2001. a
Canuto, V., Howard, A., Cheng, Y., and Dubovikov, M.: Ocean turbulence. Part II: Vertical diffusivities of momentum, heat, salt, mass, and passive scalars, J. Phys. Oceanogr., 32, 240–264, https://doi.org/10.1175/1520-0485(2002)032<0240:OTPIVD>2.0.CO;2, 2002. a
Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., and Blewitt, G.: GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science, Geophys. Res. Lett., 45, 2203–2212, https://doi.org/10.1002/2017gl076644, 2018. a, b
Cerri, L., Berthias, J., Bertiger, W., Haines, B., Lemoine, F., Mercier, F., Ries, J., Willis, P., Zelensky, N., and Ziebart, M.: Precision orbit determination standards for the Jason series of altimeter missions, Mar. Geod., 33, 379–418, https://doi.org/10.1080/01490419.2010.488966, 2010. a
Chao, B. F. and Liau, J. R.: Gravity Changes Due to Large Earthquakes Detected in GRACE Satellite Data via Empirical Orthogonal Function Analysis, J. Geophys. Res.-Sol. Ea., 124, 3024–3035, https://doi.org/10.1029/2018jb016862, 2019. a
Chassignet, E. P., Yeager, S. G., Fox-Kemper, B., Bozec, A., Castruccio, F., Danabasoglu, G., Horvat, C., Kim, W. M., Koldunov, N., Li, Y., Lin, P., Liu, H., Sein, D. V., Sidorenko, D., Wang, Q., and Xu, X.: Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, 2020. a
Chen, J., Tapley, B., Seo, K.-W., Wilson, C., and Ries, J.: Improved Quantification of Global Mean Ocean Mass Change Using GRACE Satellite Gravimetry Measurements, Geophys. Res. Lett., 46, 13984–13991, https://doi.org/10.1029/2019GL085519, 2019. a
Chen, J. L., Tapley, B. D., Save, H., Tamisiea, M. E., Bettadpur, S., and Ries, J.: Quantification of Ocean Mass Change Using Gravity Recovery and Climate Experiment, Satellite Altimeter, and Argo Floats Observations, J. Geophys. Res.-Sol. Ea., 123, 10212–10225, https://doi.org/10.1029/2018jb016095, 2018. a, b
Chen, J. L., Tapley, B., Tamisiea, M. E., Save, H., Wilson, C., Bettadpur, S., and Seo, K.: Error Assessment of GRACE and GRACE Follow-On Mass Change, J. Geophys. Res.-Sol. Ea., 126, https://doi.org/10.1029/2021jb022124, 2021. a
Chen, J. L., Cazenave, A., Dahle, C., Llovel, W., Panet, I., Pfeffer, J., and Moreira, L.: Applications and Challenges of GRACE and GRACE Follow-On Satellite Gravimetry, Surv. Geophys., 43, 305–345, https://doi.org/10.1007/s10712-021-09685-x, 2022. a
Chen, K., English, S., Bormann, N., and Zhu, J.: Assessment of FY-3A and FY-3B MWHS observations, ECMWF, https://doi.org/10.21957/s2hmm4nht, 2014. a
Chen, L., Yang, J., and Wu, L.: Topography Effects on the Seasonal Variability of Ocean Bottom Pressure in the North Pacific Ocean, J. Phys. Oceanogr., 53, 929–941, https://doi.org/10.1175/JPO-D-22-0140.1, 2023. a
Cheng, X., Ou, N., Chen, J., and Huang, R. X.: On the seasonal variations of ocean bottom pressure in the world oceans, Geosci. Lett., 8, 29, https://doi.org/10.1186/s40562-021-00199-3, 2021. a
Craig, A., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2011. a
Daras, I. and Pail, R.: Treatment of temporal aliasing effects in the context of next generation satellite gravimetry missions, J. Geophys. Res.-Sol. Ea., 122, 7343–7362, https://doi.org/10.1002/2017JB014250, 2017. a
Dill, R. and Dobslaw, H.: Numerical simulations of global-scale high-resolution hydrological crustal deformations, J. Geophys. Res.-Sol. Ea., 118, 5008–5017, https://doi.org/10.1002/jgrb.50353, 2013. a
Dobslaw, H., Bergmann-Wolf, I., Dill, R., Poropat, L., and Flechtner, F.: Product description document for AOD1B release 06, rev. 6.0., GFZ Potsdam, Potsdam, Germany, ftp://isdcftp.gfz-potsdam.de/grace/DOCUMENTS/Level-1/GRACE_AOD1B_Product_Description_Document_for_RL06.pdf (last access: 29 August 2022), 2016. a, b, c, d, e
Dobslaw, H., Bergmann-Wolf, I., Dill, R., Poropat, L., Thomas, M., Dahle, C., Esselborn, S., König, R., and Flechtner, F.: A new high-resolution model of non-tidal atmosphere and ocean mass variability for de-aliasing of satellite gravity observations: AOD1B RL06, Geophys. J. Int., 211, 263–269, https://doi.org/10.1093/gji/ggx302, 2017. a, b, c, d, e
Duan, J., Shum, C., Guo, J., and Huang, Z.: Uncovered spurious jumps in the GRACE atmospheric de-aliasing data: potential contamination of GRACE observed mass change, Geophys. J. Int., 191, 83–87, https://doi.org/10.1111/j.1365-246X.2012.05640.x, 2012. a
Flechtner, F., Neumayer, K.-H., Dahle, C., Dobslaw, H., Fagiolini, E., Raimondo, J.-C., and Güntner, A.: What can be expected from the GRACE-FO laser ranging interferometer for earth science applications?, Remote sensing and water resources, 263–280, https://doi.org/10.1007/s10712-015-9338-y, 2016. a
Forootan, E., Didova, O., Kusche, J., and Löcher, A.: Comparisons of atmospheric data and reduction methods for the analysis of satellite gravimetry observations, J. Geophys. Res.-Sol. Ea., 118, 2382–2396, https://doi.org/10.1002/jgrb.50160, 2013. a, b
Forootan, E., Didova, O., Schumacher, M., Kusche, J., and Elsaka, B.: Comparisons of atmospheric mass variations derived from ECMWF reanalysis and operational fields, over 2003–2011, J. Geodesy, 88, 503–514, https://doi.org/10.1007/s00190-014-0696-x, 2014. a, b
Gegout, P.: Dealiasing Products: Time-variable Atmospheric and Oceanic Gravitational Potential from 1980 to 2017 [data set], https://grace.obs-mip.fr/catalogue/?uuid=27cadfb2-2000-485d-a81f-7902a820e712 (last access: 12 April 2024) 2020. a
Gent, P. R. and McWilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Ghobadi-Far, K., Han, S.-C., McCullough, C. M., Wiese, D. N., Yuan, D.-N., Landerer, F. W., Sauber, J., and Watkins, M. M.: GRACE Follow-On Laser Ranging Interferometer Measurements Uniquely Distinguish Short-Wavelength Gravitational Perturbations, Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL089445, 2020. a
Ghobadi-Far, K., Han, S.-C., McCullough, C. M., Wiese, D. N., Ray, R. D., Sauber, J., Shihora, L., and Dobslaw, H.: Along-Orbit Analysis of GRACE Follow-On Inter-Satellite Laser Ranging Measurements for Sub-Monthly Surface Mass Variations, J. Geophys. Res.-Sol. Ea., 127, e2021JB022983, https://doi.org/10.1029/2021JB022983, 2022. a
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a
Greatbatch, R. J.: A note on the representation of steric sea level in models that conserve volume rather than mass, J. Geophys. Res.-Oceans, 99, 12767–12771, https://doi.org/10.1029/94JC00847, 1994. a
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A., Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., et al.: Concepts and terminology for sea level: Mean, variability and change, both local and global, Surv. Geophys., 40, 1251–1289, https://doi.org/10.1007/s10712-019-09525-z, 2019. a, b
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a
Güntner, A., Reich, M., Mikolaj, M., Creutzfeldt, B., Schroeder, S., and Wziontek, H.: Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure, Hydrol. Earth Syst. Sci., 21, 3167–3182, https://doi.org/10.5194/hess-21-3167-2017, 2017. a
Hagan, M. E.: Comparative effects of migrating solar sources on tidal signatures in the middle and upper atmosphere, J. Geophys. Res.-Atmos., 101, 21213–21222, https://doi.org/10.1029/96JD01374, 1996. a
Hagan, M. E. and Forbes, J. M.: Migrating and nonmigrating diurnal tides in the middle and upper atmosphere excited by tropospheric latent heat release, J. Geophys. Res.-Atmos., 107, ACL 6-1–ACL 6-15, https://doi.org/10.1029/2001JD001236, 2002. a
Hagan, M. E. and Forbes, J. M.: Migrating and nonmigrating semidiurnal tides in the upper atmosphere excited by tropospheric latent heat release, J. Geophys. Res.-Space, 108, https://doi.org/10.1029/2002JA009466, 2003. a
Han, S.-C. and Razeghi, S. M.: GPS recovery of daily hydrologic and atmospheric mass variation: A methodology and results from the Australian continent, J. Geophys. Res.-Sol. Ea., 122, 9328–9343, https://doi.org/10.1002/2017JB014603, 2017. a
Han, S.-C., Jekeli, C., and Shum, C. K.: Time-variable aliasing effects of ocean tides, atmosphere, and continental water mass on monthly mean GRACE gravity field, J. Geophys. Res.-Sol. Ea., 109, https://doi.org/10.1029/2003JB002501, 2004. a
Han, S.-C., Ray, R. D., and Luthcke, S. B.: Ocean tidal solutions in Antarctica from GRACE inter-satellite tracking data, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031540, 2007. a
Hardy, R. A., Nerem, R. S., and Wiese, D. N.: The impact of atmospheric modeling errors on GRACE estimates of mass loss in Greenland and Antarctica, J. Geophys. Res.-Sol. Ea., 122, 10–440, 2017. a
Hauk, M. and Pail, R.: Treatment of ocean tide aliasing in the context of a next generation gravity field mission, Geophys. J. Int., 214, 345–365, https://doi.org/10.1093/gji/ggy145, 2018. a
He, B., YU, Y., Bao, Q., Lin, P., Liu, H., Li, J., Lei, W., Liu, Y., WU, G., CHEN, K., GUO, Y., Zhao, S., Zhang, X., Song, M., and Xie, J.: CAS FGOALS-f3-L model dataset descriptions for CMIP6 DECK experiments, Atmospheric and Oceanic Science Letters, 13, 1–7, https://doi.org/10.1080/16742834.2020.1778419, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huang, X., Wang, C., Wei, J., Yu, Z., Tian, Z., and Liu, H.: An assessment of global ocean tide simulation by a coupled climate model FGOALS-g3 (in Chinese), Haiyang Xuebao, 46, 63–73, http://www.hyxbocean.cn/cn/article/doi/10.12284/hyxb2024091, 2024. a
Jiang, L., Shi, C., Zhang, T., Guo, Y., and Yao, S.: Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System, Remote Sens., 12, https://doi.org/10.3390/rs12162511, 2020. a
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D., Mikolajewicz, U., Notz, D., and von Storch, J. S.: Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model, J. Adv. Model. Earth Sy., 5, 422–446, https://doi.org/10.1002/jame.20023, 2013. a
Klos, A., Kusche, J., Leszczuk, G., Gerdener, H., Schulze, K., Lenczuk, A., and Bogusz, J.: Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy, J. Geophys. Res.-Sol. Ea., 128, e2023JB026988, https://doi.org/10.1029/2023JB026988, 2023. a
Kurtenbach, E., Mayer-Gürr, T., and Eicker, A.: Deriving daily snapshots of the Earth's gravity field from GRACE L1B data using Kalman filtering, Geophys. Res. Lett., 36, https://doi.org/10.1029/2009GL039564, 2009. a
Kusche, J.: Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models, J. Geodesy, 81, 733–749, https://doi.org/10.1007/s00190-007-0143-3, 2007. a
Kvas, A. and Mayer-Gürr, T.: GRACE gravity field recovery with background model uncertainties, J. Geodesy, 93, 2543–2552, https://doi.org/10.1007/s00190-019-01314-1, 2019. a
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water storage estimates, Water Resour. Res., 48, https://doi.org/10.1029/2011wr011453, 2012. a, b
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H.-Y., Wiese, D. N., and Yuan, D.-N.: Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance, Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL088306, 2020. a
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6KK98Q6, 2004. a
Lawrence, H., Bormann, N., Geer, A. J., Lu, Q., and English, S. J.: Evaluation and Assimilation of the Microwave Sounder MWHS-2 Onboard FY-3C in the ECMWF Numerical Weather Prediction System, IEEE T. Geosci. Remote Sens., 56, 3333–3349, https://doi.org/10.1109/TGRS.2018.2798292, 2018. a
Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., de Goncalves, L. G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I. B., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele-Dunne, S. C., Save, H., and Bettadpur, S.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018WR024618, 2019. a
Li, H., Xu, F., Zhou, W., Wang, D., Wright, J. S., Liu, Z., and Lin, Y.: Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res.-Oceans, 122, 866–889, https://doi.org/10.1002/2016JC012285, 2017. a
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T., Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L., Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang, L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020. a
Li, Z., von Storch, J.-S., and Müller, M.: The M2 Internal Tide Simulated by a 1/10° OGCM, J. Phys. Oceanogr., 45, 3119–3135, https://doi.org/10.1175/JPO-D-14-0228.1, 2015. a
Lin, P., Liu, H., Xue, W., Li, H., Jiang, J., Song, M., Song, Y., Wang, F., and Zhang, M.: A coupled experiment with LICOM2 as the ocean component of CESM1, J. Meteorol. Res., 30, 76–92, https://doi.org/10.1007/s13351-015-5045-3, 2016. a
Lin, P., Yu, Z., Liu, H., Yu, Y., Li, Y., Jiang, J., Xue, W., Chen, K., Yang, Q., Zhao, B., Wei, J., Ding, M., Sun, Z., Wang, Y., Meng, Y., Zheng, W., and Ma, J.: LICOM model datasets for the CMIP6 ocean model intercomparison project, Adv. Atmos. Sci., 37, 239–249, https://doi.org/10.1007/s00376-019-9208-5, 2020. a, b, c
Liu, H., Lin, P., Yu, Y., and Zhang, X.: The baseline evaluation of LASG/IAP climate system ocean model (LICOM) version 2, Acta Meteorologica Sinica, 26, 318–329, https://doi.org/10.1007/s13351-012-0305-y, 2012. a, b
Liu, H., Yang, F., Zhang, T., and Bai, J.: CRA-LICOM: A global high-frequency atmospheric and oceanic temporal gravity field product (2002–2024), TPDC [data set] https://doi.org/10.11888/SolidEar.tpdc.302016, 2025a. a, b
Liu, S., Yang, F., and Forootan, E.: SAGEA: A toolbox for comprehensive error assessment of GRACE and GRACE-FO based mass changes, Comput. Geosci., 196, 105825, https://doi.org/10.1016/j.cageo.2024.105825, 2025b. a
Liu, W. and Sneeuw, N.: Aliasing of ocean tides in satellite gravimetry: a two-step mechanism, J. Geodesy, 95, 134, https://doi.org/10.1007/s00190-021-01586-6, 2021. a
Liu, Z., Jiang, L., Shi, C., Zhang, T., Zhou, Z., Liao, J., Yao, S., Liu, J., Wang, M., Wang, H., Liang, X., Zhang, Z., Yao, Y., Zhu, T., Chen, Z., Xu, W., Cao, L., Jiang, H., and Hu, K.: CRA-40/atmosphere—the first-generation Chinese atmospheric reanalysis (1979–2018): system description and performance evaluation, J. Meteorol. Res., 37, 1–19, https://doi.org/10.1007/s13351-023-2086-x, 2023. a, b
Loomis, B. D., Rachlin, K. E., Wiese, D. N., Landerer, F. W., and Luthcke, S. B.: Replacing GRACE/GRACE-FO With Satellite Laser Ranging: Impacts on Antarctic Ice Sheet Mass Change, Geophys. Res. Lett., 47, https://doi.org/10.1029/2019gl085488, 2020. a
Mayer-Gürr, T., Behzadpour, S., Kvas, A., Ellmer, M., Klinger, B., Strasser, S., and Zehentner, N.: ITSG-Grace2018: Monthly, Daily and Static Gravity Field Solutions from GRACE, ICGEM, https://doi.org/10.5880/ICGEM.2018.003, 2018. a
Mayer-Gürr, T., Savcenko, R., Bosch, W., Daras, I., Flechtner, F., and Dahle, C.: Ocean tides from satellite altimetry and GRACE, J. Geodyn., 59–60, 28–38, https://doi.org/10.1016/j.jog.2011.10.009, 2012. a
Morton, Y. T., Lieberman, R. S., Hays, P. B., Ortland, D. A., Marshall, A. R., Wu, D., Skinner, W. R., Burrage, M. D., Gell, D. A., and Yee, J.-H.: Global mesospheric tidal winds observed by the high resolution Doppler imager on board the Upper Atmosphere Research Satellite, Geophys. Res. Lett., 20, 1263–1266, https://doi.org/10.1029/93GL00826, 1993. a
Mungov, G., Eblé, M., and Bouchard, R.: DART® Tsunameter Retrospective and Real-Time Data: A Reflection on 10 Years of Processing in Support of Tsunami Research and Operations, Pure Appl. Geophys., 170, 1369–1384, https://doi.org/10.1007/s00024-012-0477-5, 2013. a
National Oceanic and Atmospheric Administration: Deep-Ocean Assessment and Reporting of Tsunamis (DART®), NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5F18WNS, 2005. a
Ohlmann, J. C.: Ocean Radiant Heating in Climate Models, J. Climate, 16, 1337–1351, https://doi.org/10.1175/1520-0442(2003)16<1337:ORHICM>2.0.CO;2, 2003. a
Pawlowicz, R., Beardsley, B. J., and Lentz, S. J.: Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE, Comput. Geosci., 28, 929–937, https://doi.org/10.1016/S0098-3004(02)00013-4, 2002. a
Petit, G. and Luzum, B.: IERS conventions (2010), https://iers-conventions.obspm.fr/content/tn36.pdf (last access: 6 January 2023), 2010. a
Purkhauser, A. F. and Pail, R.: Next generation gravity missions: Near-real time gravity field retrieval strategy, Geophys. J. Int., 217, 1314–1333, https://doi.org/10.1093/GJI/GGZ084, 2019. a
Ray, R. D.: Ocean self‐attraction and loading in numerical tidal models, Mar. Geod., 21, 181–192, https://doi.org/10.1080/01490419809388134, 1998. a, b
Redi, M. H.: Oceanic isopycnal mixing by coordinate rotation, J. Phys. Oceanogr., 12, 1154–1158, https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2, 1982. a
Rodell, M. and Reager, J. T.: Water cycle science enabled by the GRACE and GRACE-FO satellite missions, Nature Water, 1, 47–59, https://doi.org/10.1038/s44221-022-00005-0, 2023. a
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., and Lo, M.-H.: Emerging trends in global freshwater availability, Nature, 557, 651–659, https://doi.org/10.1038/s41586-018-0123-1, 2018. a
Roemmich, D. and Gilson, J.: The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program, Prog. Oceanogr., 82, 81–100, https://doi.org/10.1016/j.pocean.2009.03.004, 2009. a
Rudenko, S., Dettmering, D., Esselborn, S., Fagiolini, E., and Schöne, T.: Impact of Atmospheric and Oceanic De-aliasing Level-1B (AOD1B) products on precise orbits of altimetry satellites and altimetry results, Geophys. J. Int., 204, 1695–1702, https://doi.org/10.1093/gji/ggv545, 2016. a
Scanlon, B.R., Zhang, Z., Save, H., Sun, A.Y., Müller Schmied, H., van Beek, L.P.H., Wiese, D.N., Wada, Y. , Long, D., Reedy, R.C., Longuevergne, L., Döll, P., and Bierkens, M.F.P.: Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data, P. Natl. Acad. Sci., 115, E1080–E1089, https://doi.org/10.1073/pnas.1704665115, 2018. a
Schindelegger, M. and Dobslaw, H.: A global ground truth view of the lunar air pressure tide L2, J. Geophys. Res.-Atmos., 121, 95–110, https://doi.org/10.1002/2015JD024243, 2016. a
Schindelegger, M., Harker, A. A., Ponte, R. M., Dobslaw, H., and Salstein, D. A.: Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability, J. Geophys. Res.-Oceans, 126, e2020JC017031, https://doi.org/10.1029/2020JC017031, 2021. a
Seo, K.-W., Wilson, C. R., Chen, J. L., and Waliser, D. E.: GRACE's spatial aliasing error, Geophys. J. Int., 172, 41–48, https://doi.org/10.1111/j.1365-246X.2007.03611.x, 2008. a
Shen, C., Zha, J., Wu, J., Zhao, D., Azorin-Molina, C., Fan, W., and Yu, Y.: Does CRA-40 outperform other reanalysis products in evaluating near-surface wind speed changes over China?, Atmos. Res., 266, 105948, https://doi.org/10.1016/j.atmosres.2021.105948, 2022. a
Shihora, L., Balidakis, K., Dill, R., Dahle, C., Ghobadi-Far, K., Bonin, J., and Dobslaw, H.: Non-Tidal Background Modeling for Satellite Gravimetry Based on Operational ECWMF and ERA5 Reanalysis Data: AOD1B RL07, J. Geophys. Res.-Sol. Ea., 127, e2022JB024360, https://doi.org/10.1029/2022JB024360, 2022a. a, b, c, d
Shihora, L., Sulzbach, R., Dobslaw, H., and Thomas, M.: Self-attraction and loading feedback on ocean dynamics in both shallow water equations and primitive equations, Ocean Model., 169, 101914, https://doi.org/10.1016/j.ocemod.2021.101914, 2022b. a, b
Shihora, L., Liu, Z., Balidakis, K., Wilms, J., Dahle, C., Flechtner, F., Dill, R., and Dobslaw, H.: Accounting for residual errors in atmosphere–ocean background models applied in satellite gravimetry, J. Geodesy, 98, 27, https://doi.org/10.1007/s00190-024-01832-7, 2024. a, b
Sneeuw, N.: Global spherical harmonic analysis by least-squares and numerical quadrature methods in historical perspective, Geophys. J. Int., 118, 707–716, https://doi.org/10.1111/j.1365-246X.1994.tb03995.x, 1994. a
Springer, A., Mielke, C. A., Liu, Z., Dixit, S., Friederichs, P., and Kusche, J.: A Regionally Refined and Mass-Consistent Atmospheric and Hydrological De-Aliasing Product for GRACE, GRACE-FO and Future Gravity Missions, J. Geophys. Res.-Sol. Ea., 129, e2023JB027883, https://doi.org/10.1029/2023JB027883, 2024. a
Steele, M., Morley, R., and Ermold, W.: PHC: A global ocean hydrography with a high-quality Arctic Ocean, J. Climate, 14, 2079–2087, https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2, 2001. a
Stewart, K., Hogg, A., Griffies, S., Heerdegen, A., Ward, M., Spence, P., and England, M.: Vertical resolution of baroclinic modes in global ocean models, Ocean Model., 113, 50–65, https://doi.org/10.1016/j.ocemod.2017.03.012, 2017. a
Swarr, M. J., Martens, H. R., and Fu, Y.: Sensitivity of GNSS-derived estimates of terrestrial water storage to assumed Earth structure, J. Geophys. Res.-Sol. Ea., 129, e2023JB027938, https://doi.org/10.1029/2023JB027938, 2024. a
Swenson, S. and Wahr, J.: Estimated effects of the vertical structure of atmospheric mass on the time-variable geoid, J. Geophys. Res.-Sol. Ea., 107, ETG 4-1–ETG 4-11, https://doi.org/10.1029/2000JB000024, 2002. a, b, c
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.: GRACE Measurements of Mass Variability in the Earth System, Science, 305, 503–505, https://doi.org/10.1126/science.1099192, 2004. a
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E. and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019. a
Thomas, M., Sündermann, J., and Maier-Reimer, E.: Consideration of ocean tides in an OGCM and impacts on subseasonal to decadal polar motion, Geophys. Res. Lett., 28, 2457–2460, https://doi.org/10.1029/2000GL012234, 2001. a
Treguier, A. M., de Boyer Montégut, C., Bozec, A., Chassignet, E. P., Fox-Kemper, B., McC. Hogg, A., Iovino, D., Kiss, A. E., Le Sommer, J., Li, Y., Lin, P., Lique, C., Liu, H., Serazin, G., Sidorenko, D., Wang, Q., Xu, X., and Yeager, S.: The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies, Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, 2023. a
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020. a
Uebbing, B., Kusche, J., Rietbroek, R., and Landerer, F. W.: Processing Choices Affect Ocean Mass Estimates From GRACE, J. Geophys. Res.-Oceans, 124, 1029–1044, https://doi.org/10.1029/2018jc014341, 2019. a, b
Velicogna, I. and Wahr, J.: Measurements of Time-Variable Gravity Show Mass Loss in Antarctica, Science, 311, 1754–1756, https://doi.org/10.1126/science.1123785, 2006. a
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res., 103, 30205–30229, https://doi.org/10.1029/98jb02844, 1998. a, b, c
Wang, P., Jiang, J., Lin, P., Ding, M., Wei, J., Zhang, F., Zhao, L., Li, Y., Yu, Z., Zheng, W., Yu, Y., Chi, X., and Liu, H.: The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application , Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, 2021. a
White, A. M., Gardner, W. P., Borsa, A. A., Argus, D. F., and Martens, H. R.: A Review of GNSS/GPS in Hydrogeodesy: Hydrologic Loading Applications and Their Implications for Water Resource Research, Water Resour. Res., 58, e2022WR032078, https://doi.org/10.1029/2022WR032078, 2022. a
Wiese, D. N., Visser, P., and Nerem, R. S.: Estimating low resolution gravity fields at short time intervals to reduce temporal aliasing errors, Adv. Space Res., 48, 1094–1107, https://doi.org/10.1016/j.asr.2011.05.027, 2011. a
Wu, Y., Yang, F., Liu, S., and Forootan, E.: PyHawk: An efficient gravity recovery solver for low–low satellite-to-satellite tracking gravity missions, Comput. Geosci., 201, 105934, https://doi.org/10.1016/j.cageo.2025.105934, 2025. a
Xiao, C. and Yu, Y.: Adoption of a two-step shape-preserving advection scheme in an OGCM, Progress in Natural Science, 16, 1442–1448, https://doi.org/10.3321/j.issn:1002-008X.2006.11.011, 2006. (in Chinese). a
Yang, F., Forootan, E., Schumacher, M., Shum, C., and Zhong, M.: Evaluating non-tidal atmospheric products by measuring GRACE K-band range rate residuals, Geophys. J. Int., 215, 1132–1147, https://doi.org/10.1093/gji/ggy340, 2018. a
Yang, F., Forootan, E., Wang, C., Kusche, J., and Luo, Z.: A New 1-Hourly ERA5-Based Atmosphere De-Aliasing Product for GRACE, GRACE-FO, and Future Gravity Missions, J. Geophys. Res.-Sol. Ea., 126, e2021JB021926, https://doi.org/10.1029/2021JB021926, 2021. a, b, c, d
Yang, F., Luo, Z., Zhou, H., and Kusche, J.: On study of the Earth topography correction for the GRACE surface mass estimation, J. Geodesy, 96, https://doi.org/10.1007/s00190-022-01683-0, 2022. a
Yang, F., Forootan, E., Liu, S., and Schumacher, M.: A Monte Carlo Propagation of the Full Variance-Covariance of GRACE-Like Level-2 Data With Applications in Hydrological Data Assimilation and Sea-Level Budget Studies, Water Resour. Res., 60, e2023WR036764, https://doi.org/10.1029/2023WR036764, 2024a. a
Yang, F., Liu, S., and Forootan, E.: A spatial-varying non-isotropic Gaussian-based convolution filter for smoothing GRACE-like temporal gravity fields, J. Geodesy, 98, 66, https://doi.org/10.1007/s00190-024-01875-w, 2024b. a
Yu, R.: A two-step shape-preserving advection scheme, Adv. Atmos. Sci., 11, 479–490, https://doi.org/10.1007/BF02658169, 1994. a
Yu, Y., Tang, S., Liu, H., Lin, P., and Li, X.: Development and Evaluation of the Dynamic Framework of an Ocean General Circulation Model with Arbitrary Orthogonal Curvilinear Coordinate, Chinese Journal of Atmospheric Sciences, 42, 877–889, https://doi.org/10.3878/j.issn.1006-9895.1805.17284, 2018 (in Chinese). a
Zenner, L., Gruber, T., Jäggi, A., and Beutler, G.: Propagation of atmospheric model errors to gravity potential harmonics – impact on GRACE de-aliasing, Geophys. J. Int., 182, 797–807, https://doi.org/10.1111/j.1365-246X.2010.04669.x, 2010. a, b
Zhang, W., Yang, F., Yi, W., Hailong, L., Zhang, T., Luo, Z., and Forootan, E.: HUST-CRA: A New Atmospheric De-aliasing Model for Satellite Gravimetry, Adv. Atmos. Sci., 42, 382–396, https://doi.org/10.1007/s00376-024-4045-6, 2025. a
Zhang, X. and Liang, X.: A numerical world ocean general circulation model, Adv. Atmos. Sci., 6, 44–61, https://doi.org/10.1007/BF02656917, 1989. a
Zhou, H., Luo, Z., Zhou, Z., Yang, F., and Yang, S.: What Can We Expect from the Inclined Satellite Formation for Temporal Gravity Field Determination?, Surv. Geophys., https://doi.org/10.1007/s10712-021-09641-9, 2021. a
Short summary
We introduce China's first de-aliasing computation platform, incorporating China's Atmospheric Reanalysis and an in-house ocean circulation model. This platform produces CRA-LICOM, a high-frequency atmospheric and oceanic gravity de-aliasing product with a 6-hourly, 50 km resolution covering 2002–2024 globally. This product is reliable for de-aliasing, signal separation in satellite gravity missions, and climate change studies.
We introduce China's first de-aliasing computation platform, incorporating China's Atmospheric...
Altmetrics
Final-revised paper
Preprint