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Atmosphere, v.11 no.8, 2020년, pp.823 -
Peng, Ting (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)) , Zhi, Xiefei (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)) , Ji, Yan (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)) , Ji, Luying (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)) , Tian, Ye
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The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead...
DelSole Predictability of week-3-4 average temperature and precipitation over the contiguous United States J. Clim. 2017 10.1175/JCLI-D-16-0567.1 30 3499
Black The predictors and forecast skill of Northern Hemisphere teleconnection patterns for lead times of 3-4 weeks Mon. Weather Rev. 2017 10.1175/MWR-D-16-0394.1 145 2855
National Research Council (2010). Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, The National Academies Press.
Brunet Collaboration of the Weather and Climate Communities to Advance Subseasonal-to-Seasonal Prediction Bull. Amer. Meteorol. Soc. 2010 10.1175/2010BAMS3013.1 91 1397
The National Academies of Sciences, Engineering, Medicine (2016). Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts, The National Academies Press.
Mariotti Progress in subseasonal to seasonal prediction through a joint weather and climate community effort npj Clim. Atmos. Sci. 2018 10.1038/s41612-018-0014-z 1 4
Pegion Prospects for improving subseasonal predictions Mon. Weather Rev. 2011 10.1175/MWR-D-11-00004.1 139 3648
Li Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems Mon. Weather Rev. 2015 10.1175/MWR-D-14-00277.1 143 2871
Pegion The subseasonal experiment (SUBX) Bull. Amer. Meteorol. Soc. 2019 10.1175/BAMS-D-18-0270.1 100 2043
Raftery Using Bayesian model averaging to calibrate forecast ensembles Mon. Weather Rev. 2005 10.1175/MWR2906.1 133 1155
Gneiting Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation Mon. Weather Rev. 2005 10.1175/MWR2904.1 133 1098
Krishnamurti Improved weather and seasonal climate forecasts from multimodel superensemble Science 1999 10.1126/science.285.5433.1548 285 1548
Zhi A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data Acta Meteorol. Sin. 2012 10.1007/s13351-012-0104-5 26 41
He Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method Meteorol. Atmos. Phys. 2015 10.1007/s00703-015-0377-1 127 467
Ji Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation Mon. Weather Rev. 2020 10.1175/MWR-D-19-0266.1 148 2591
Ji Probabilistic precipitation forecasting over East Asia using Bayesian model averaging Weather Forecast. 2019 10.1175/WAF-D-18-0093.1 34 377
10.1201/b22400-15 Neapolitan, R.E., and Neapolitan, R.E. (2018). Neural Networks and Deep Learning. Artif. Intell., 389-411.
LeCun Deep learning Nature 2015 10.1038/nature14539 521 436
Angermueller Deep learning for computational biology Mol. Syst. Biol. 2016 10.15252/msb.20156651 12 878
Goh Deep learning for computational chemistry J. Comput. Chem. 2017 10.1002/jcc.24764 38 1291
Chao Research on real-time local rainfall prediction based on MEMS sensors J. Sens. 2018 10.1155/2018/6184713 2018 1
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2017, January 4-9). Deep learning for precipitation nowcasting: A benchmark and a new model. Proceedings of the neural information processing systems (NIPS 2017), Long Beach, CA, USA.
Yang Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks J. Geophys. Res. Atmos. 2018 123 12543
Rasp Neural networks for postprocessing ensemble weather forecasts Mon. Weather Rev. 2018 10.1175/MWR-D-18-0187.1 146 3885
Duan, T., Avati, A., Ding, D.Y., Thai, K.K., Basu, S., Ng, A.Y., and Schuler, A. (2020, June 09). NGBoost: Natural Gradient Boosting for Probabilistic Prediction. Available online: https://arxiv.org/abs/1910.03225.
Friedman Greedy function approximation: A gradient boosting machine Ann. Stat. 2001 10.1214/aos/1013203451 29 1189
Ren, L., Sun, G., and Wu, J. (2020, June 09). RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping. Available online: https://arxiv.org/abs/1912.02338.
Fan A global monthly land surface air temperature analysis for 1948-present J. Geophys. Res. Atmos. 2008 10.1029/2007JD008470 113 D01103
Glahn The Use of Model Output Statistics (MOS) in Objective Weather Forecasting J. Appl. Meteorol. 1972 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2 11 1203
Hamill A probabilistic forecast contest and the difficulty in assessing short-range forecast uncertainty Weather Forecast. 1995 10.1175/1520-0434(1995)010<0620:APFCAT>2.0.CO;2 10 620
Stefanova Interpretation of seasonal climate forecast using Brier skill score, the Florida State University Superensemble, and the AMIP-I dataset J. Clim. 2002 10.1175/1520-0442(2002)015<0537:IOSCFU>2.0.CO;2 15 537
Jordan Evaluating probabilistic forecasts with scoringRules J. Stat. Softw. 2019 10.18637/jss.v090.i12 90 1
Kingma, D.P., and Ba, J.L. (2015, January 7-9). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA.
Amari Natural Gradient Works Efficiently in Learning Neural Comput. 1998 10.1162/089976698300017746 10 251
Martens, J. (2020, June 09). New insights and perspectives on the natural gradient method. Available online: https://arxiv.org/abs/1412.1193.
Gneiting Strictly Proper Scoring Rules, Prediction, and Estimation J. Am. Stat. Assoc. 2007 10.1198/016214506000001437 102 359
Gebetsberger Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood Mon. Weather Rev. 2018 10.1175/MWR-D-17-0364.1 146 4323
Gneiting Probabilistic and sharpness forecasts, calibration J. R. Stat. Soc. Ser. B-Stat. Methodol. 2013 10.1111/j.1467-9868.2007.00587.x 69 243
Anderson A Method for Producing and Evaluating Proababilistic Forecasts from ensemble Model Integrations J. Clim. 1996 10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2 9 1518
Hamill Verification of Eta-RSM Short-Range Ensemble Forecasts Mon. Weather Rev. 1997 10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2 125 1312
Talagrand, O., Vautard, R., and Strauss, B. (1997, January 20-22). Evaluation of probabilistic prediction systems. Proceedings of the Workshop on Predictability, Reading, UK.
Hamill Interpretation of Rank Histograms for Verifying Ensemble Forecasts Mon. Weather Rev. 2001 10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2 129 550
Hersbach Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems Weather Forecast. 2000 10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2 15 559
Zhu Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System J. Geophys. Res. Atmos. 2018 10.1029/2018JD028506 123 6732
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