Improving Precipitation Estimation Using Convolutional Neural Network

Abstract

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation‐related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation‐related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach.

PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

“…The CNN offers a viable tool for precipitation estimation problems since it can gain more abstract and more expressive information from multispectral channels. Recently, a CNN was implemented to estimate precipitation based on the dynamic and moisture fields from numerical weather model analysis (Pan et al 2018). Pan et al (2018) showed that the CNN technique can improve numerical precipitation estimation on the west and east coasts of United States.…”

“…Recently, a CNN was implemented to estimate precipitation based on the dynamic and moisture fields from numerical weather model analysis (Pan et al 2018). Pan et al (2018) showed that the CNN technique can improve numerical precipitation estimation on the west and east coasts of United States. Miao et al (2019) applied a combination of CNN and long short-term memory (LSTM) to improve the resolution and accuracy of precipitation estimates based on dynamical simulations.…”

ARPEGE cloud cover forecast post-processing with convolutional neural network

“…Generally, CNNs using NWP data as predictors are used to produce either a classification or a pointwise regression, meaning that the CNN produces a zero dimension result from two dimensional data. For example to correct the precipitation forecast integrated over a region, to estimate if a thunderstorm will produce large hailstones, or to predict if a storm will generate a tornado (Pan et al, 2019;Gagne II et al, 2019;Lagerquist et al, 2019a). Few NWP postprocessing using CNNs has been performed on a grid scale.…”

Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

“…Effective use of the available big data from multi-sensors is one direction to improve the accuracy of precipitation estimation products [15]. Recent developments of Machine Learning (ML) techniques from the fields of computer science have been extended to the geosciences community and is another direction to improve the accuracy of satellite-based precipitation estimation products [9,[15][16][17][18][19][20][21][22][23]. Deep Neural Networks (DNNs) are a specific type of ML model framework with great capability to handle a huge amount of data.…”