[1] Tao, F., Huang, Y., Hungate, B.A. ... , Huang, X(*), Luo YX(*). (2023). Microbial carbon use efficiency promotes global soil carbon storage. Nature. https://doi.org/10.1038/s41586-023-06042-3

[2] Wang, M., Wang, D., Xiang, Y., Liang, Y., Xia, R., Yang, J., ... & Huang, X(*). (2023). Fusion of ocean data from multiple sources using deep learning: Utilizing sea temperature as an example. Frontiers in Marine Science.

[3]   Chen, Y., Huang, X.(*), Luo, J. J., Lin, Y., Wright, J. S., Lu, Y., ... & Lin, P. (2023). Prediction of ENSO using multivariable deep learning. Atmospheric and Oceanic Science Letters, 100350.

[4] Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., ... & Huang, X(*). (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin.

[5] Zhang, B., Ma, M., Wang, M., Hong, D., Yu, L., Wang, J., ... & Huang, X.(*) (2022). Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 7391-7399.

[6]   Huang, P., Chen, Q., Wang, D., Wang, M., Wu, X., & Huang, X.(*). (2022). TripleConvTransformer: A deep learning vessel trajectory prediction method fusing discretized meteorological data. Frontiers in Environmental Science, 1720.

[7] Chen, Q., Ding, W., Huang, X.(*), & Wang, H. (2022). Generalized interval type ii fuzzy rough model based feature discretization for mixed pixels. IEEE Transactions on Fuzzy Systems.

[8]   Liao, C., Huang, W., Wells, J., Zhao, R., Allen, K., Hou, E., …, Huang, X. & Luo, Y. (2022). Microbe-iron interactions control lignin decomposition in soil. Soil Biology and Biochemistry, 173, 108803.

[9] Liao, C., Chen, Y., Wang, J., Liang, Y., Huang, Y., Lin, Z., ... Huang, X. & Luo, Y. (2022). Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP). Ecological Processes, 11(1), 1-16.

[10] Lei Lin, Hao Liu, Xiaomeng Huang, Qingjun Fu, and Xinyu Guo. Effect of tides on river water behavior over the eastern shelf seas of China. Hydrol. Earth Syst. Sci., 26, 5207–5225, 2022

[11]   Jin, W., Luo, Y., Wu, T., Huang, X., Xue, W., & Yu, C. (2022). Deep Learning for Seasonal Precipitation Prediction over China. Journal of Meteorological Research, 36(2), 271-281.

[12] Tang, Q., Huang, X.(*), Lin, L., Xiong, W., Wang, D., Wang, M. and Huang, X.(2021). MERF v3.0, a highly computationally efficient non-hydrostatic ocean model with implicit parallelism: algorithms and validation experiments. Ocean Modelling. https://doi.org/10.1016/j.ocemod.2021.101877.

[13] Wang J.M., Li W., Ciais P., Li L., Chang J., Goll D., Gasser T., Huang X., Devaraju N., Bouche O. (2021). Global cooling induced by biophysical effects of bioenergy crop cultivation. Nature Communications. 12(1):7255. doi: 10.1038/s41467-021-27520-0.

[14] Huang X.(*), Lin Lin, Yuwen Chen, Yue Chen, Xing Huang, Mingqing Wang, Jonathon S. Wright.(2021).  Improving machine learning-based weather forecast post-processing with clustering and transfer learning. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021 

[15] Hu J. , Tian C. , Ge B., Huang X.(*), Wu X.(*)(2021). A climate downscaling method based on deep back-projection neural network. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[16] Li X., Zhang L., Wang J., Huang X., Zhong Q., Wu X.(2021). Forecasting of Time Series Significant Wave Height based on ConvLSTM. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[17] Li L., Zhang H,, Wang J. , Huang X.(*), Zhong Q. , Wu X.(*). (2021). Forecasting Significant Wave Height based on Transformer. CLIVAR Exchanges (Special Issue: Advances in Climate Prediction Using Artificial Intelligence ). No.81, Nov. 2021

[18] Shi, P., Lu, H., Leung, L.R., He, Y., Wang, B., Yang, K., Yu, L., Liu, L., Huang, W., Xu, S. , Liu, J., Huang, X., Li, L. and Lin, Y.(2021). Significant land contributions to interannual predictability of East Asian summer monsoon rainfall. Earth's Future, 9(2), p.e2020EF001762.

[19] Dou, X., Liao, C., Wang, H., Huang, Y., Tu, Y., Huang, X., Peng, Y., Zhu, B., Tan, J., Deng, Z. and Wu, N.(2021). Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means. Advances in Applied Energy, 2, p.100017.

[20] Xiao, Q., Geng, G., Cheng, J., Liang, F., Li, R., Meng, X., Xue, T., Huang, X., Kan, H., Zhang, Q. and He, K.(2021). Evaluation of gap-filling approaches in satellite-based daily PM2. 5 prediction models. Atmospheric Environment, 244, p.117921.

[21] Tao, F., Huang, Y., Hungate, B. A., Lu, X., Hocking, T. D., Mishra, U., Hugelius, G., Huang, X., and Luo, Y.(2021). PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage, 3rd ISMC Conference ─ Advances in Modeling Soil Systems, online, 18–22 May 2021, ISMC2021-10.

[22] Xiao, Q., Zheng, Y., Geng, G., Chen, C., Huang, X., Che, H., Zhang, X., He, K. and Zhang, Q. (2021). Separating emission and meteorological contributions to long-term PM 2.5 trends over eastern China during 2000–2018. Atmospheric Chemistry and Physics, 21(12), pp.9475-9496.

[23] Wang, J., Li, W., Ciais, P., Ballantyne, A., Goll, D., Huang, X., Zhao, Z. and Zhu, L.(2021). Changes in biomass turnover times in tropical forests and their environmental drivers from 2001 to 2012. Earth's Future, 9(1).

[24] Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., Wang, L., Xue, W., Fu, H., Zhang, G., Wang, B., Li, R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao, W., Zhang, F., Xu, J., Zhao, X., Lu, C., Chen, Y., Luo, Y., Hu, Y., Tang, Q., Chen, D., Yang, G. and Gong, P.(2020). Community Integrated Earth System Model (CIESM): Description and Evaluation. Journal of Advances in Modeling Earth Systems, 12(8), p.e2019MS002036.

[25] Han, Y., Zhang, G.J.(*), Huang, X(*). and Wang, Y.(2020). A moist physics parameterization based on deep learning. Journal of Advances in Modeling Earth Systems, 12(9), p.e2020MS002076. 

[26] Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., Jiang, L. and Doughty, R.(2020). Deep learning optimizes data-driven representation of soil organic carbon in Earth system model over the conterminous United States. Frontiers in big Data, 3, p.17.

[27] He, Y., Wang, B., Liu, L., Huang, W., Xu, S., Liu, J., Wang, Y., Li, L., Huang, X., Peng, Y. and Lin, Y.(2020). A DRP‐4DVar‐based coupled data assimilation system with a simplified off‐line localization technique for decadal predictions. Journal of Advances in Modeling Earth Systems, 12(4).

[28] Yao, P., Lu, H., Wang, W., Shao, C., Yang, K., Gianotti, D., Liu, Z., Huang, X. and Entekhabi, D.(2020). Soil Moisture Retrieval Only Using Smap L-Band Radar Observations. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4586-4589). IEEE.

[29] He, Q., Yue, S., Lu, H., Liu, Z., Huang, X. and Entekhabi, D.(2020). Identifying Terrestrial Vegetation-Soil Moisture Oscillation from Satellite Observations. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4570-4573). IEEE.

[30] He, Y., Wang, B., Huang, W., Xu, S., Wang, Y., Liu, L., Li, L., Liu, J., Yu, Y., Lin, Y. and Huang, X.(2020). A new DRP-4DVar-based coupled data assimilation system for decadal predictions using a fast online localization technique. Climate Dynamics, 54(7), pp.3541-3559.

[31] Xiao, Q., Geng, G., Liang, F., Wang, X., Lv, Z., Lei, Y., Huang, X., Zhang, Q., Liu, Y. and He, K.(2020). Changes in spatial patterns of PM2. 5 pollution in China 2000–2018: Impact of clean air policies. Environment international, 141, p.105776.

[32] Zhao, J., Yu, L., Xu, Y., Li, X., Zhou, Y., Peng, D., Liu, H., Huang, X., Zhou, Z., Wang, D. and Ren, C.(2020). Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities. International Journal of Remote Sensing, 41(23), pp.8965-8985.

[33] Dong, W. H. , Lin, Y. L. , Zhang, M. H.  and Huang, X.(2020). Footprint of tropical mesoscale convective system variability on stratospheric water vapor. Geophysical Research Letters, 47(5), e2019GL086320.

[34] Chen, Y., Huang, X., Li, Y., Chen, Y., Xu, Z. and Huang X.(2020). Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products. Journal of Applied Meteorological Science,31(4): 494-503. 

[35] Liu, X., Yu, L., Dong, Q., Peng, D., Wu, W., Yu, Q., Cheng, Y., Xu, Y., Huang, X., Zhou, Z. and Wang, D.(2020). Cropland heterogeneity changes on the Northeast China Plain in the last three decades (1980s–2010s). PeerJ, 8, p.e9835.

[36] Zhang Y., Li J., Yu R., Liu Z., Zhou Y., Li X. and Huang X. (2020). A multiscale dynamical model in a dry-mass coordinate for weather and climate modeling: Moist dynamics and its coupling to physics. Monthly Weather Review, v 148, n 7, p 2671-2699.

[37] Xu, L., Wang, A., Wang, C., Chen, Y., Chen, Y., Zhou, Z., Chen, X., Xing, J., Liu, K. and Huang, X.(*).(2020). Research on correction method of marine environment prediction based on machine learning. Marine Science Bulletin. 39(6):695-704.

[38] Huang, X.(*), Huang, X., Wang, D., Wu, Q., Li, Y., Zhang, S., Chen, Y., Wang, M., Gao, Y., Tang, Q. and Chen, Y.(2019). OpenArray v1. 0: a simple operator library for the decoupling of ocean modeling and parallel computing. Geoscientific Model Development, 12(11), pp.4729-4749. 

[39] Chen, D., Guo, J., Yao, D., Lin, Y., Zhao, C., Min, M., Xu, H., Liu, L., Huang, X., Chen, T. and Zhai, P.(2019). Mesoscale convective systems in the Asian monsoon region from Advanced Himawari Imager: Algorithms and preliminary results. Journal of Geophysical Research: Atmospheres, 124(4), pp.2210-2234.

[40] Xu, Y., Yu, L., Feng, D., Peng, D., Li, C., Huang, X., Lu, H. and Gong, P.(2019). Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30. International Journal of Remote Sensing, 40(16), pp.6185-6202.

[41] Zhao, J., Yu, L., Xu, Y., Ren, H., Huang, X. and Gong, P.(2019). Exploring the addition of Landsat 8 thermal band in land-cover mapping. International Journal of Remote Sensing, 40(12), pp.4544-4559.

[42] Yu, C., Huang, X., Chen, H., Godfray, H.C.J., Wright, J.S., Hall, J.W., Gong, P., Ni, S., Qiao, S., Huang, G., Xiao, Y., Zhang, J., Feng, Z., Ju, X., Philippe C. Nils S., Dag H., Sun, Z., Yu, L., Cai, W., Fu, H., Huang, X., Zhang, C. and Liu, H.(2019). Managing nitrogen to restore water quality in China. Nature, 567(7749), pp.516-520.

[43] Chen, Y., Lu, Y., Zhao, P., Xu, F., Huang, X. and Huang, X.(*)(2019). Seasonal and interannual variations of sea temperature influenced by Galápagos Islands in eastern tropical Pacific Ocean. Journal of Geophysical Research: Oceans, 124(5), pp.3007-3020.

[44] Li, Y., Wang, W., Lu, H., Khem, S., Yang, K. and Huang, X.(2019). Evaluation of three satellite-based precipitation products over the lower mekong river basin using rain gauge observations and hydrological modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), pp.2357-2373.

[45] Lin, Y., Huang, X. , Liang, Y. , Qin, Y. , Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., Wang,L., Xue, W., Fu, H., Zhang, G., Wang, B., Li, R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao, W., Zhang, F., Xu, J., Zhao, X., Lu, C., Luo, Y., Chen, Y., Hu, Y., Tang, Q., Chen, D., Yang, G. and Gong, P. (2019). The community integrated Earth system model(CIESM)from tsinghua university and its plan for cmip6 experiments. Climate Change Research. 15(5): 545-550.

[46] Wu, Q., Ni, Y. and Huang X.(*)(2019). Regional Ocean Model Parallel Optimization in "Sunway TaihuLight". Journal of Computer Research and Development. 56(7):1556-1566.

[47] Huang, X.(*), Hu, C., Huang, X., Chu, Y., Tseng, Y.H., Zhang, G.J. and Lin, Y.(*) (2018). A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm. Climate dynamics, 51(7), pp.3145-3159.

[48] Li, C., Lu, H., Yang, K., Han, M., Wright, J.S., Chen, Y., Yu, L., Xu, S., Huang, X. and Gong, W.(2018). The evaluation of SMAP enhanced soil moisture products using high-resolution model simulations and in-situ observations on the Tibetan Plateau. Remote Sensing, 10(4), p.535.

[49] Xu, S., Xu, Y., Xue, W., Shen, X., Zheng, F., Huang, X. and Yang, G.(2018). Taming the" Monster": Overcoming program optimization challenges on SW26010 through precise performance modeling. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 763-773). IEEE.

[50] Huang, X., Yu, C., Fang, J., Huang, G., Ni, S., Hall, J., Zorn, C., Huang, X. and Zhang, W.(2018). A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer. Computers and electronics in agriculture, 154, pp.400-410.

[51] Huang, X., Ni, S., Yu, C., Hall, J., Zorn, C. and Huang, X.(2018). Identifying precipitation uncertainty in crop modelling using Bayesian total error analysis. European Journal of Agronomy, 101, pp.248-258.

[52] Lv, M., Lu, H., Yang, K., Xu, Z., Lv, M. and Huang, X. (2018). Assessment of runoff components simulated by GLDAS against UNH–GRDC dataset at global and hemispheric scales. Water, 10(8), 969.

[53] Xu, Y., Yu, L., Peng, D., Cai, X., Cheng, Y., Zhao, J., Zhao, Y., Feng, D., Hackman, K., Huang, X. and Lu, H.(2018). Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt, Ethiopia, and South Africa. International Journal of Remote Sensing, 39(21), pp.7328-7349.

[54] Wang, X., Gan, L., Xu, J., Yang, J., Xia, M., Fu, H., Huang, X. and Yang, G.(2018). PLZMA: a parallel data compression method for cloud computing. In International Conference on Algorithms and Architectures for Parallel Processing (pp. 504-518). Springer, Cham.

[55] Cui, W., Zhang, J., Schmidt, F., Cui, D., Huang, X., Li, T. and Tian, F.(2018). Simultaneous characterization of the atmospheres, surfaces, and exomoons of nearby rocky exoplanets. Earth and Planetary Physics, 2(3), pp.247-256.

[56] Tseng, Y. H.(*), Ding, R. and Huang, X.(*)(2017). The warm blob in the northeast pacific—the bridge leading to the 2015/16 El Niño. Environmental Research Letters, 12(5), 054019.

[57] Liu, F., Guo, J., Huang, X. and Lui, J. C. S. (2017). eBA: efficient bandwidth guarantee under traffic variability in datacenters. IEEE/ACM Transactions on Networking, 25(1), 506-519.

[58] Li, C., Lu, H., Yang, K., Wright, J. S., Yu, L., Chen, Y., Huang, X. and Xu, S. (2017). Evaluation of the common land model (CoLM) from the perspective of water and energy budget simulation: towards inclusion in CMIP6. Atmosphere, 8.

[59] Fu, H., Gan, L., Yang, C., Xue, W., Wang, L.,  Wang, X.,Huang,X. and Yang, G. (2017). Solving global shallow water equations on heterogeneous supercomputers. Plos One, 12(3), e0172583.

[60] Zhao, J., Tian, F., Ni, Y. and Huang, X. (2017). DR-induced escape of O and C from early MARS. Icarus, 284.

获得2021年国家杰出青年科学基金

获得2016年戈登贝尔奖（ACM Gordon Bell Prize）奖提名

获得2014年清华大学—浪潮集团计算地球科学青年人才奖

获得2013年清华大学地学中心先进工作者

获得2012年清华大学地学中心先进工作者

获得2011年中兴通讯产学研优秀成果一等奖

地球系统数值模拟装置——超级模拟支撑与管理系统,国家发改委

发展通用的地球系统模式高效并行计算框架,国家科学技术部高技术研究发展中心

智能数值模式发展,国家自然科学基金

高分辨率区域海洋模式软件子系统研制,国家科学技术部高技术研究发展中心

高效自动并行的海洋模式计算框架研究,国家自然科学基金

人工智能辅助数值天气预报关键技术研究,国家自然科学基金

面向海洋模式的高效自动并行三维算子库研制,海洋科学与技术试点国家实验室

三维海洋要素场智能化重构技术研究,海洋环境科学与数值模拟重点实验室

高分辨率区域气候动力降尺度预报技术研发与应用,自然资源部北海预报中心

全球大规模高效并行海浪数值模拟技术,自然资源部第一海洋研究所

基于深度学习的ENSO预报模型研究,自然资源部海洋环境预报中心

区域动态高时空分辨率大气污染源排放清单,生态环境部(总理基金项目)