Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.H. ); [EMAIL_ADDRESS] (L.Z. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
Bijing University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology Campus, 1  
Bijing 100049, China 1  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.H. ); [EMAIL_ADDRESS] (Z. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.H. ); [EMAIL_ADDRESS] (Z. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
[PERSON]  
3 Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China; [EMAIL_ADDRESS]  
[PERSON]  
4 China General Nuclear Power Group, Shenzhen 518038, China; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (Y.W. )5 Hong Kong Observatory, Hong Kong 999077, China; [EMAIL_ADDRESS]  
[PERSON]  
4 China General Nuclear Power Group, Shenzhen 518038, China; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (Y.W. )5 Hong Kong Observatory, Hong Kong 999077, China; [EMAIL_ADDRESS]  
[PERSON]  
5 Hong Kong Observatory, Hong Kong 999077, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.H. ); [EMAIL_ADDRESS] (L.Z. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
Bijing University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology Campus, 1  
Bijing 100049, China 1  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.H. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
Bijing University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology Campus, 1  
Bijing 100049, China 1  
[PERSON]  
1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [EMAIL_ADDRESS] (L.Z. ); [EMAIL_ADDRESS] (N.L. )1 Shenzhen Institute of Advanced Technology Campus, 1  
Bijing University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology Campus, 1
enhancement, physical parameterization scheme improvement, observation network enhancement, and data assimilation enhancement [1]. These techniques have also been used to improve TC precipitation forecasting. For example, [PERSON] et al. (2021b) used microwave radiation assimilation to obtain better hydrometeorological field analysis and more accurate rainfall forecasts [7]. [PERSON] et al. (2010) discovered that the precipitation analysis achieved by incorporating atmospheric infrared sounder (AIRS) cloud inversion captures the heavy precipitation areas associated with TCs better than that without incorporating AIRS information or using AIRS clear-sky radiation [8]. [PERSON] et al. (2007) used a combined dynamical-statistical approach to improve TC precipitation forecasts from numerical models [1]. By using the contiguous rain area (CRA) to assess the systematic error in Australian Community Climate and Earth System Simulator TC (ACCESS-TC) precipitation forecasts, [PERSON] et al. (2018) discovered that ACCESS-TC produced more extreme rainfall near the TC center (eye wall) [9]. Despite the advancements in NWP techniques, predicting the precipitation associated with a TC remains a challenge [10]. There is a need to enhance the prediction capability of NWP models, especially in forecasting extreme precipitation related to TCs [1,11].