DeepAI AI Chat
Log In Sign Up

Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling

by   Takeyoshi Nagasato, et al.

Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully applied to precipitation estimation. This study implements a three-dimensional (3D) CNN to estimate watershed-scale daily precipitation from 3D atmospheric data and compares the results with those for a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the vertical direction (3D-CNN-Vert). The precipitation estimates of these extended CNNs are compared with those of the 2D CNN in terms of the root-mean-square error (RMSE), Nash-Sutcliffe efficiency (NSE), and 99th percentile RMSE. It is found that both 3D-CNN-Time and 3D-CNN-Vert improve the model accuracy for precipitation estimation compared to the 2D CNN. 3D-CNN-Vert provided the best estimates during the training and test periods in terms of RMSE and NSE.


page 3

page 5

page 9

page 12


Estimating galaxy masses from kinematics of globular cluster systems: a new method based on deep learning

We present a new method by which the total masses of galaxies including ...

A New Method for Atlanta World Frame Estimation

In this paper, we propose a new Atlanta frame estimation method by consi...

Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs

In this paper, we present a new model for Direction of Arrival (DOA) est...

Global canopy height estimation with GEDI LIDAR waveforms and Bayesian deep learning

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate m...

Convolution Neural Network based Mode Decomposition for Degenerated Modes via Multiple Images from Polarizers

In this paper, a mode decomposition (MD) method for degenerated modes ha...