部分承担课题
1996-1998 NSF, Development of the Adjoint and 4-D Variational Data Assimilation for the NCEP medium range forecasting system，33万美元，参与。
2004-2007 NASA， Impact of Pacific Climate Variability on Ocean Circulation, Marine Ecosystems and Living Resources: A Multi-Scale Modeling and Data Assimilation Approach to Forecasting， 120万美元，子项目负责人。
2009-2014, DOE，Continuous Evaluation of Fast Processes in Climate Models Using ARM Measurements ，1400万美元，子项目负责人。
2013-2016, NASA，Multi-Scale Data Assimilation of Satellite Atimetry for Real-time Current Prediction in Coastal Oceans，55万美元，主持。
2014-2017, NASA, Megacities Carbon Project: Assessing the Impact of Policy and Management Decisions on the Los Angeles Urban Dome of CO2 and CH4， 120万美元，子项目负责人。
2015-2018, NASA, Multi-Scale Data Assimilation, Forecasting and Modeling in Support of SPURS-2， 58万美元，主持。
2015-2019, DOE, Development of the LES ARM Symbiotic Simulation and Observation (LASSO) Workflow， 160万美元，子项目负责人。
2016-2019, NASA, Assessing the Ability of CYGNSS to Provide Sea Surface Topography for Mesoscale Studies, 70万美元，参与。
发表论文 （2015年以后）
（本人名称加粗，通讯作者加*号）
1. Jiang, X., L. Liu, Z. Li, L. Liu, K. Kam Sian, C. Dong, 2022: A Two-Dimensional Variational Scheme for Blending Multiple Satellite Altimetry Data and Eddy Analysis, Remote Sensing, 14，3206.
2. Zang Z., Y. Liang, W. You, Y. Li, X. Pan, and Z. Li, 2022: Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: methodology and application Sci. in China(series D), in press.
3. Archer, M., Z. Li, J. Wang, and L.-L. Fu, 2021: Data assimilative modeling in support of the SWOT satellite mission: Reconstructing fine-scale ocean variability via data assimilation of an in-situ observing system J. Geophy. Res., DOI 10.1029/2021JC17362.
4. Bingham, F. M., Z. Li, S. Katsura, and J. Sprintall, 2020: Barrier Layers in a High-resolution Model in the Eastern Tropical Pacific, J. Geophy. Res., DOI: 10.1029/ 2020JC016643.
5. Bingham, F. M., Z. Li, 2020: Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions, Remote Sensing, 12, 3996; doi:10.3390/rs12233996.
6. Archer M, Z. Li, and L.-L. Fu, 2020: Increasing the space-time effective resolution of mapped sea surface height from altimetry, J. Geophy. Res. DOI: 10.1029/2019JC015878.
7. Liu L., X. Jiang, J. Fei, and Z. Li: 2020: Development of new merged data products from multi-satellite altimetry and evaluation, Chinese Scientific Bulletin, doi 10.1360/TB-2020-0097.
8. Liu L. X Jiang, Z. Li, J. Fei, et al., A review on development of mapped data products of satellite altimetry measurements. Chinese J. Remote Sensing, 00(1): 1-24.
9. Gustafson, W., I., A. M. Vogelmann, Z. Li, X. Cheng, K. Kyle, K. K. Dumas, K. K. S. Endo, K. Johnson, B. Krishna, T. Toto, and H. Heng, 2019: Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Workflow for Continental Shallow Convection. Bulletin of the American Meteorological Society, 101 (2019), 4, E462-E479.
10. Li., Z., J. Wang, and L. Fu., 2019: An Observing System Simulation Experiment for ocean state estimation to assess the performance of the SWOT Mission. Part 1: A twin experiment, J. Geophy. Res., 124, 4838-4855.
11. Peng, S. Y. Zhu, Z. Li, and co-authors, 2019: Improving the Real-time Marine Forecasting of the Northern South China Sea by Assimilation of Glider-observed T/S Profiles. Scientific Reports, https://doi.org/10.1038/s41598-019-54241-8.
12. Li Z., F. M. Bingham, and P. Y. Li, 2019: Multiscale simulation, data assimilation and forecasting in support of the SPURS-2 field campaign. Oceanography, 32, 2, 76-83.
13. Wang, J-H, J. Shi, X. Liang, M. Peng, Z. Li, C. Miao, 2019: Air-sea fluxes of heat and momentum over the Yellow Sea during cold air outbreaks, Marine Science Bulletin,21, 2, 16-35.
14. Shi, H., B. Zhao, Z. Jiang, Z. Li, K. Bowman, Y. Chen, Y. Gu, J. H. Jiang, M. Lee, K.-N. Liou, J. Neu, V. Payne, H. Su, Y. Wang, M. Witek, and John Worden, 2019: Modeling study of the air quality impact of record-breaking Southern California wildfires in December 2017, J. Geophy. Res.-Atmosphere, 124, 12, 6554-6570.
15. Benveniste J, Cazenave A, Vignudelli S, Fenoglio-Marc L, Shah R, Almar R, Andersen O, Birol F, Bonnefond P, Bouffard J, Calafat F, Cardellach E, Cipollini P, Le Cozannet G, Dufau C, Fernandes J, Frappart F, Garrison J, Gommenginger C, Han G, Høyer JL, Kourafalou V, Leuliette E, Li Z, Loisel H, Madsen KS, Marcos M, Melet A, Meyssignac B, Pascual A, Passaro M, Ribó S, Scharroo R, Song YT, Speich S, Wilkin J, Woodworth P and Wöppelmann G (2019) Requirements for a Coastal Hazards Observing System. Front. Mar. Sci. 6:348. doi: 10.3389/fmars.2019.00348
16. Ma, C., T Wang, Z Zang, Z Li, 2018 Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, Adv. Atmos. Sci., 35,813-825.
17. Wickert, J., Co-authors, Z. Li, 2016: GEROS-ISS: GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. IEEE Trans. Geosci. Remote Sens., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, .
18. Li. Z., C. Zuffada, S. T. Lowe, T. Lee and V. Zlotnicki, 2016: Analysis of GNSS-R Altimetry for Mapping Ocean Mesoscale Sea Surface Heights Using High-Resolution Model Simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 9, doi10.1109/JSTARS.2016.2581699.
19. Zang, Z., Hao, Z., Y. Li, X. Pan, W. You, Z. Li, and D. Chen, 2016: Background error covariance with balance constraints for aerosol species and applications in data assimilation. Geosci. Model Dev., doi:10.5194/gmdd-8-1-2015.
20. Zang, Z., Z. Li, Z. Hao, X. Pan, and Y. Wei, 2016: Aerosol data assimilation and forecasting experiments using aircraft and surface observations. Tellus B, 68, 29812, http://dx.doi.org/10.3402/tellusb.v68.29812.
21. Li, Z., X. Chen, W. I. Gustafson, and A. Vogelmann, 2016: Spectral Characteristics of Background Error Covariance and Multiscale Data Assimilation. Int. J. Numer. Meth. Fluids, doi:10.1002/fld.4253.
22. Feng, S., T. Lauvaux, S. Newman, P. Rao, R. Patarasuk, R. Ahmadov, A. Deng, K.W. Wong, D. O’Keeffe, J. Huang, Y. Song, K. Gurney, L.I. Diaz-Isaac, S. Jeong, M.L. Fischer, C.E. Miller, R.M. Duren, Z. Li, Y.L. Yung, S.P. Sander, 2016: Network Assessment for Atmospheric Monitoring of Urban CO2 Emissions Using a High-Resolution Land-Atmosphere Modelling System, Atmos. Chems. Phy., doi:10.5194/acp-2016-143.
23. Peng, S., X. Zeng, and Z. Li, 2016: A three-dimensional variational data assimilation system for the South China Sea: Preliminary results from Observing System Simulation Experiments. Ocean Dynamics, doi:10.1007/s10236-016-0946-y.
24. You, W, Z. Zang, L. Zhang, Z. Li, D. Chen, and G. Zhang, 2015: Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count. Remote Sensing of Environment, 168, 276-285.
25. Wang X., L. Zhao, Z. Li, and D. Menemenlis, 2015: Regional ocean forecasting systems and their applications: Designing consideration of such a system for the South China Sea. Aquatic Ecosystem Health & Management. 18, 4, 443-453.
26. Li, Z., J.C. McWilliams, K. Ide, and J.D. Fararra, 2015: A Multi-Scale Data Assimilation Scheme: Formulation and Illustration. Monthly Weather Review, 143, 3804-3822.
27. Li, Z., J.C. McWilliams, K. Ide, and J.D. Fararra, 2015: Coastal Ocean Data Assimilation Using A Multi-Scale Three-Dimensional Variational Scheme. Ocean Dynamics, 65, 1001-1015, doi10.1007/s10236-015-0850-x.
28. Zang, Z., Z. Hao, X. Pan, Z. Li, D. Chen, L. Zhang, and Q. Li, 2015: Background error statistics for aerosol variables from WRF/Chem Predictions in Southern California, Asia-Pacific Journal of Atmospheric Sciences, 51, 2, 123-135, doi:10.1007/s13143-015-0063-8.
29. Vogelmann, A. M., A. Fridlind, Lin, T. Toto, S. Endo, W. Lin, J Wang, S. Feng, Y. Zhang, D. Turner, Y. Liu, Z. Li, S. Xie, A. S. Acherman, M. Zhang, and M. Khairoutdinov, 2015: RACORO Continental Boundary Layer Cloud Investigations. Part I: Case Study Development and Ensemble Large-Scale Forcings. J. Geophy. Res., doi:10.1022/2014JD022713.
30. Bingham, F. M., P. P. Li, Z. Li, Q. Vu, and Y. Chao, 2015: Data Management Support for the SPURS Atlantic Field Campaign, Oceanography, 28, 42-51.
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