ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weather fronts, and blocking events among others. Existing labeled data for these patterns can be incomplete in various ways, such as covering only certain years or geographic areas and having false negatives. This type of climate data therefore poses a number of interesting machine learning challenges. We present a multichannel spatiotemporal CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. We demonstrate that our approach is able to leverage temporal information and unlabeled data to improve the localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data. We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change. The dataset is available at extremeweatherdataset.github.io and the code is available at https://github.com/eracah/hur-detect.
Deep neural networks, especially deep convolutional neural networks, have enjoyed breakthrough success in recent recent years, achieving state-of-the-art results on many benchmark datasets (Krizhevsky et al., 2012; and also compelling results on many practical tasks such as disease diagnosis (Hosseini-Asl et al., 2016), facial recognition (Parkhi et al., 2015), autonomous driving , and many others. Furthermore, deep neural networks have also been very effective in the context of unsupervised and semi-supervised learning; some recent examples include variational autoencoders (Kingma & Welling, 2013), adversarial networks (Goodfellow et al., 2014;Makhzani et al., 2015;Salimans et al., 2016;Springenberg, 2015), ladder networks (Rasmus et al., 2015) and stacked what-where autoencoders (Zhao et al., 2015).
There is a recent trend towards video datasets aimed at better understanding spatiotemporal relations and multimodal inputs (Kay et al., 2017;Gu et al., 2017;Goyal et al., 2017). The task of finding extreme weather events in climate data is similar to the task of detecting objects and activities in video -a popular application for deep learning techniques. An important difference is that in the case of climate data, the 'video' has 16 or more 'channels' of information (such as water vapour, pressure and temperature), while conventional video only has 3 (RGB). In addition, climate simulations do not share the same statistics as natural images. As a result, unlike many popular techniques for video, we hypothesize that we cannot build off successes from the computer vision community such as using pretrained weights from CNNs (Simonyan & Zisserman, 2014;Krizhevsky et al., 2012) pretrained on ImageNet (Russakovsky et al., 2015).
Climate data thus poses a number of interesting machine learning problems: multi-class classification with unbalanced classes; partial annotation; anomaly detection; distributional shift and bias correction; spatial, temporal, and spatiotemporal relationships at widely varying scales; relationships between variables that are not fully understood; issues of data and computational efficiency; opportunities for semi-supervised and generative models; and more. Here, we address multi-class detection and localization of four extreme weather phenomena: tropical cyclones, extra-tropical cyclones, tropical depressions, and atmospheric rivers. We implement a 3D (height, width, time) convolutional encoderdecoder, with a novel single-pass bounding-box regression loss applied at the bottleneck. To our knowledge, this is the first use of a deep autoencoding architecture for bounding-box regression. This architectural choice allows us to do semi-supervised learning in a very natural way (simply training the autoencoder with reconstruction for unlabelled data), while providing relatively interpretable features at the bottleneck. This is appealing for use in the climate community, as current engineered heuristics do not perform as well as human experts for identifying extreme weather events.
Our main contributions are (1) a baseline bounding-box loss formulation; (2) our architecture, a first step away from engineered heuristics for extreme weather events, towards semi-supervised learned features; (3) the ExtremeWeather dataset, which we make available in three benchmarking splits: one small, for model exploration, one medium, and one comprising the full 27 years of climate simulation output.
2 Related work 2.1 Deep learning for climate and weather data Climate scientists do use basic machine learning techniques, for example PCA analysis for dimensionality reduction (Monahan et al., 2009), and k-means analysis for clusterings Steinhaeuser et al. (2011). However, the climate science community primarily relies on expert engineered systems and ad-hoc rules for characterizing climate and weather patterns. Of particular relevance is the TECA (Toolkit for Extreme Climate Analysis) Prabhat et al. (2012Prabhat et al. ( , 2015, an application of large scale pattern detection on climate data using heuristic methods. A more detailed explanation of how TECA works is described in section 3. Using the output of TECA analysis (centers of storms and bounding boxes around these centers) as ground truth, (Liu et al., 2016) demonstrated for the first time that convolutional architectures could be successfully applied to predict the class label for two extreme weather event types. Their work considered the binary classification task on centered, cropped patches from 2D (single-timestep) multi-channel images. Like (Liu et al., 2016) we use TECA's output (centers and bounding boxes) as ground truth, but we build on the work of Liu et al. (2016) by: 1) using uncropped images, 2) considering the temporal axis of the data 3) doing multi-class bounding box detection and 4) taking a semi-supervised approach with a hybrid predictive and reconstructive model. Some recent work has applied deep learning methods to weather forecasting. Xingjian et al. (2015) have explored a convolutional LSTM architecture (described in 2.2 for predicting future precipitation on a local scale (i.e. the size of a city) using radar echo data. In contrast, we focus on extreme event detection on planetary-scale data. Our aim is to capture patterns which are very local in time (e.g. a hurricane may be present in half a dozen sequential frames), compared to the scale of our underlying climate data, consisting of global simulations over many years. As such, 3D CNNs seemed to make more sense for our detection application, compared to LSTMs whose strength is in capturing long-term dependencies.
This paper presents a new dataset, a model and experimental results on this dataset to address the task of extreme weather events detection and localization. The dataset is 27 year weather simulation sampled 8 times per day for 16 channels only the surface atmospheric level. The proposed model is based on 3D convolutional layers with an autoencoder architecture. The technique is semi-supervised, thus training with a loss that combines reconstruction error of the autoencoder and detection and localization from the middle code layer.
In general the paper is very well written and quite clear on most details. The experimental results only use a small part of the data and are a bit preliminary, but still they do show the potential that this data has for future research.
Some comments/concerns/suggestions are the following.
- Since the paper presents a new dataset it is important to include a link to where the data will be available.
- In line 141 it says that only surface quantities are considered without any explanation why. Is it because the simulation would have been significantly more expensive so only surface computed? Is it a decision of the authors to make the dataset size more manageable? Are there any future plans to make available the full 30 atmospheric levels?
- The caption of table 1 says "Note test statistics are omitted to preserve anonymity of the test data." I assume this means that the ground truth labels are not being made available with the rest of the data. If this is the case, in the paper it should be explained what is the plan for other researchers to evaluate the techniques they develop. Will there be some web server where results are submitted and the results are returned?
- From what I understand the ground truth labels for the four weather events considered were generated fully automatically. If so, why was only half of the data labeled? I agree that exploring semi-supervised methods is important, but this can be studied by ignoring the labels of part of the data and analyze the behavior of the techniques when they have available more or less labeled samples.
- Is there any plan to extend the data with weather events that cannot be labeled by TECA?
- VGG is mentioned in line 51 without explaining what it is. Maybe add a reference for readers that are not familiar with it.
- In figure 3 the the graphs have very tiny numbers. These seem useless so maybe remove them saving a bit of space to make the images slightly larger.
- Line 172 final full stop missing.
- Be consistent on the use of t-SNE, some places you have it with a dash and other places without a dash.