清华大学黄小猛--中文主页--学术论文

黄小猛

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联系方式：010-62798365
电子邮箱：hxm@tsinghua.edu.cn

学科：大气科学

中文主页 > 科学研究 > 学术论文

[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.

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[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.

[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.

个人信息

[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.

[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.

[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

[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