许磊，男，国家地理信息系统工程技术研究中心特任教授，地图学与地理信息系统专业，博士毕业于武汉大学测绘遥感信息工程国家重点实验室，研究方向为时空预测、时空大数据分析、时空统计、环境遥感等，目前在Earth-Science Reviews、Remote Sensing of Environment、Geophysical Research Letters、Water Resources Research、Journal of Geophysical Research:Atmospheres等期刊发表SCI论文 50余篇，参与国家重点研发计划、国家自然科学基金重大项目多项。

招生方向：

欢迎对时空智能预测、时空大数据挖掘、人工智能、深度学习、遥感大数据分析、水文气象、灾害预测预警、空间统计与分析、地理学、资源与环境遥感等研究方向感兴趣的学生报考

科研项目：

国家自然科学基金青年基金项目，42201509 , 强降水人工智能短期预测可靠性传播分析方法研究，主持

中国博士后面上基金，数据驱动的短时降水预测可靠性建模方法研究，2022M722930，主持

湖北省自然科学基金面上项目，因果学习和物理模型耦合的长江流域降水短期预测方法研究，主持

科研论文：      

1.        Xu, L., Chen, N., Chen, Z., Zhang C., Yu H. (2021). Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions. Earth-Science Reviews, 103828. doi: 10.1016/j.earscirev.2021.103828

2.        Xu, L., Chen N.C., Zhang, X., Moradkhani H., Zhang, C., Hu C. (2021). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products, Remote Sensing of Environment.（高被引论文）

3.        Xu, L., Abbaszadeh, P., Moradkhani, H., Chen, N., & Zhang, X. (2020). Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index. Remote Sensing of Environment, 250, 112028.（高被引论文）

4.        Xu, L., Chen, N., Yang, C., Yu, H., and Chen, Z. Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning, Hydrology and Earth System Sciences, 26, 2923–2938, https://doi.org/10.5194/hess-26-2923-2022, 2022.

5.        Xu, L., Chen, N., Yang C., Zhang C., Yu H. (2021). A parametric multivariate drought index for drought monitoring and assessment under climate change. Agricultural and Forest Meteorology, 310, 108657. doi: 10.1016/j.agrformet.2021.108657

6.        Xu, L., Zhang, C., Chen N.C., Moradkhani H., Chu P.S., Zhang, X. Potential precipitation predictability decreases under future warming, Geophysical Research Letters, 2020.

7.        Xu, L., Chen, N., Moradkhani, H., Zhang, X., & Hu, C. (2020). Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets. Water Resources Research, 56(3), e2019WR026444. 

8.        Xu, L., Chen, N., Zhang, X. and Chen, Z., 2019. Spatiotemporal changes in China's terrestrial water storage from GRACE satellites and its possible drivers. Journal of Geophysical Research: Atmospheres, 124(22), pp.11976-11993. 

9.        Xu, L., Chen, N., Zhang, X., Chen, Z., Hu, C. and Wang, C., 2019. Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning. Climate Dynamics, 53(1-2), pp.601-615. 

10.     Xu, L., Chen, N., Zhang, X. and Chen, Z., 2020. A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale. Climate Dynamics, pp.1-20. 

11.     Xu, L., Chen, N., Zhang, X. and Chen, Z., 2018. An evaluation of statistical, NMME and hybrid models for drought prediction in China. Journal of Hydrology, 566, pp.235-249. 

12.     Xu, L., Chen, N. and Zhang, X., 2018. A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China. Journal of Hydrology, 557, pp.378-390. 

13.     Xu, L., Chen, N. and Chen, Z., 2017. Will China make a difference in its carbon intensity reduction targets by 2020 and 2030?. Applied Energy, 203, pp.874-882. 

14.     Xu, L., Chen, N. and Zhang, X., 2019. Global drought trends under 1.5 and 2 C warming. International Journal of Climatology, 39(4), pp.2375-2385. 

15.     Xu L, Yu H, Chen Z, Du W, Chen N, Zhang C. Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory. Remote Sensing. 2023; 15(5):1417. https://doi.org/10.3390/rs15051417

16.     Xu L, Yu H, Chen Z, Du W, Chen N, Huang M. Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting. Remote Sensing. 2023; 15(13):3410. https://doi.org/10.3390/rs15133410

17.     Xu L., J. Liu and N. Chen, Spatiotemporal dynamics of remote-sensed Forel-Ule Index for inland waters across China during the COVID-19 Pandemic, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2023.3298108.

18.     Xu, L., Zhang, X., et al. Pentad-mean air temperature prediction using spatial autocorrelation and attention-based deep learning model. Theoretical and Applied Climatology (2023). https://doi.org/10.1007/s00704-023-04763-z

19.     Xu, L., Cai R., Yu H., Du W., Chen Z. and Chen N., Monthly NDVI prediction using spatial autocorrelation and nonlocal attention networks, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2024.3350053.

20.     Xu, L., Zhang X., Yu H., Chen Z., Du W. and Chen N., Incorporating spatial autocorrelation into Deformable ConvLSTM for hourly precipitation forecasting, Computers and Geosciences, 2024

21.     Xu, L., Zhang, X., Wu, T., Yu, H., Du, W., Zhang, C., & Chen, N. (2024). Global prediction of flash drought using machine learning. Geophysical Research Letters, 51(21), e2024GL111134.

22.     Xu, L., Lv, Y. & Moradkhani, H. Daily multistep soil moisture forecasting by combining linear and nonlinear causality and attention-based encoder-decoder model. Stochastic Environmental Research and Risk Assessment (2024). https://doi.org/10.1007/s00477-024-02846-5

23.     Cai, R., Xu, L.^*, Lv, Y., Wu, T., Li, X., Pan, Z., Yu, H., Du, W. and Chen, N., 2024. Geographically weighted convolutional long short-term memory neural networks: a geospatial deep learning model for monthly NDVI prediction. IEEE Transactions on Geoscience and Remote Sensing.