Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

By Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, D. Lavers, K. Kunkel, Michael F. Wehner, William D. Collins. 2016

387 citations recorded by the source corpus.

Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in climate simulations and observational data archives is critical for understanding the trends and potential impacts of such events in a climate change content. This study presents the first application of Deep Learning techniques as alternative methodology for climate extreme events detection. Deep neural networks are able to learn high-level representations of a broad class of patterns from labeled data. In this work, we developed deep Convolutional Neural Network (CNN) classification system and demonstrated the usefulness of Deep Learning technique for tackling climate pattern detection problems. Coupled with Bayesian based hyper-parameter optimization scheme, our deep CNN system achieves 89\%-99\% of accuracy in detecting extreme events (Tropical Cyclones, Atmospheric Rivers and Weather Fronts

Subfields: Artificial Intelligence, Building and Construction, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Building and Construction

In Environmental Engineering

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[84], a CNN architecture is used for detection of extreme climate events, such as tropical cyclones, atmospheric rivers and weather fronts.”
Neural networks for post-processing ensemble weather forecasts (2018, methodology): “In the atmospheric sciences, neural networks have been used to detect extreme weather in climate datasets (Liu et al., 2016) and parameterize sub-grid processes in global circulation models (Gentine et al., 2018).”
Machine learning and artificial intelligence to aid climate change research and preparedness (2019, background): “Liu et al (2016) Climate extremes Predict a drought index usingmeteor- ological and climate indices as inputs.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “In a study carried out at the Lawrence Berkeley National Lab, Liu et al. (2016) trained a CNN with two convolutional layers to detect extreme events using thousands of images of tropical cyclones, weather fronts, and atmospheric rivers.”
Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere (2020, methodology): “esolution, and [38] used NNs to estimate the uncertainty in weather forecasts. Deep NNs have also been used to identify extreme weather and climate patterns in observed and modeled atmospheric states [24, 19, 20], to predict extreme weather events [e.g., 15], and to provide operational guidance and ”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “[208] utilize the deep learning model to detect climate extreme events, such as hurricanes and heat waves, based on climate image data.”
The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset (2020, background): “CNNs not only succeed in the image classification domain but are also successfully applied in text classification [4], climate change detection [5], and speech recognition [6], among others.”
Tackling Climate Change with Machine Learning (2019, background): “le data sets are strongly skewed because extreme events are, by deﬁnition, rare. ML has been used successfully to classify some extreme weather events. Researchers have used deep learning to classify [501], detect [480] and segment [502] cyclones and atmospheric rivers, as well as tornadoes [503], i”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “Application domains Related works Transportation [189], [199], [125], [32], [30], [6], [33], [136], [60], [121], [27], [114], [177], [90], [23], [73], [117], [135], [89], [148], [85], [137], [155], [157], [79], [107], [201], [22], [24], [108], [105], [16], [106], [93], [15], [175], [176], [104], [14”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, methodology): “Indeed, the use of CNNs for detecting extreme weather events from climate model simulations has recently been demonstrated in [89], [90].”
Graph Neural Controlled Differential Equations for Traffic Forecasting (2021, background): “…data frequently happens in real-world applications, ranging from trafﬁc to climate forecasting (Zaytar and El Amrani 2016; Shi et al. 2015, 2017; Liu et al. 2016; Racah et al. 2016; Kurth et al. 2018; Cheng et al. 2018a,b; Hossain et al. 2015; Ren et al. 2021; Tekin et al. 2021; Li et al.…”
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, methodology): “ML models currently outperform state-of-the-art techniques at some of the most sophisticated domain problems across the Natural Sciences (e.g., AlQuraishi, 2019; He et al., 2019; Liu et al., 2016; Mayr et al., 2016).”
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent (2018, methodology): “DL algorithms have demonstrated remarkable improvements in many Computer Vision tasks [1, 2] and science domains such as High Energy Physics [3], and Climate Modeling [4].”