Program on Mathematical and Statistical Methods for Climate and the Earth System Opening Workshop, Deep Learning for Extreme Weather Detection - Prabhat, Aug 23, 2017
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This document discusses using deep learning techniques to detect extreme weather patterns in climate data. It begins by outlining the scientific motivation and successes of deep learning in computer vision. It then describes early successes applying deep learning to climate science tasks like classifying tropical cyclones, atmospheric rivers, and weather fronts. Challenges include dealing with multi-variate climate data and lack of labeled examples. Future work involves creating unified deep learning models that can perform detection, localization, and segmentation of extreme weather across different climate datasets.
Program on Mathematical and Statistical Methods for Climate and the Earth System Opening Workshop, Deep Learning for Extreme Weather Detection - Prabhat, Aug 23, 2017
- 1. Deep Learning for Extreme Weather Detection - 1 - Prabhat Lawrence Berkeley National Laboratory SAMSI Climate Workshop 8/23/2017
- 2. Outline •Scientific Motivation •Deep Learning in Computer Vision – Trends, Successes •Deep Learning in Climate Science – Results, Challenges •Future of AI in Climate Science - 2 -
- 3. Extreme Weather - 3 -
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- 8. How will extreme weather change in the future? - 8 -
- 9. How will extreme weather change in the future? •Look back in time (Paleoclimate records) •Look forward in time (Climate simulations) – Internal climate system variability – External forcings – Anthropogenic influence •Need an objective tool for detecting extremes – Pattern detection task – Can Deep Learning come to the rescue? - 9 -
- 10. Climate Simulations - 10 -
- 11. Outline • •Deep Learning in Computer Vision – Trends, Successes • – • - 11 -
- 12. Computer Vision tasks - 12 -
- 13. Deep Learning for Computer Vision - 13 -
- 14. ImageNet Dataset •1000 object classes •1.2M training examples - 14 -
- 17. Can Deep Learning Work for Climate Science? - 17 - • Similarities – Tasks: • Classification, Localization, Detection, Segmentation • Clustering • Feature Learning • Differences – Unique attributes of Climate Data • Multi-channel / Multi-variate • Different Spatio-temporal scales • Double precision floating point • Underlying statistics are likely different
- 18. Challenge: Multi-Variate Data - 18 -
- 19. Outline • • – •Deep Learning in Climate Science – Results, Challenges • - 19 -
- 20. - 20 - Task: Find Extreme Weather Patterns
- 21. Supervised Binary Classification •Training Input: Cropped, Centered, Multi-variate patches with Labels –Tropical Cyclone (TC) –Atmospheric River (AR) –Weather Front (WF) •Output: Binary (Yes/No) on Test patches – Is there a TC in the patch? – Is there an AR in the patch? – Is there a WF in the patch? - 21 -
- 22. CLASSIFICATION Image Dimension Variables Total Examples (+ve) (-ve) Tropical Cyclone 32x32 PSL,UBOT,VBOT,TMQ, U850,V850,T200,T500 10000 10000 Atmospheric Rivers 148x224 TMQ, Land Sea mask 6500 6800 Weather Fronts 27x60 T2m, Precip, PSL 5600 6500 Training Data
- 23. CLASSIFICATION Conv1 Pool1 Conv2 Pool2 Full Full Tropical Cyclone 5x5-8 2x2 5x5-16 2x2 50 2 Atmospheric River 12x12-8 3x3 12x12-16 2x2 200 2 Weather Fronts 5x5-16 2x2 5x5-16 2x2 400 2 Supervised Convolutional Architecture
- 24. Logistic Regression K-Nearest Neighbor Support Vector Machine Random Forest ConvNet Train Test Train Test Train Test Train Test Train Test Tropical Cyclone 96.8 95.85 98.1 97.85 97.0 95.85 99.2 99.4 99.3 99.1 Atmospheric Rivers 81.97 82.65 79.7 81.7 81.6 83.0 87.9 88.4 90.5 90.0 Weather Fronts 84.9 89.8 72.46 76.45 84.35 90.2 80.97 87.5 88.7 89.4 Hyper-parameter optimization applied with Spearmint for all methods Supervised Classification Accuracy
- 25. Semi-Supervised Detection •Objectives: – Create unified architecture for all weather patterns – Want to predict bounding box location for weather pattern – Might have few/no labels for several weather patterns; want to discover new patterns - 25 -
- 26. Semi-Supervised Convolutional Architecture Encoder Decoder Classification + Bounding Box Regression Contributors: Evan Racah, Chris Beckham, Tegan Maharaj, Chris Pal
- 27. Reconstruction Results - 27 - Original Reconstruction
- 28. Classification + Regression Results - 28 - Ground Truth Prediction
- 30. Weather Front Detection - 30 - Contributors: Jim Biard, Ken Kunkel, Evan Racah
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- 34. Current Status • SC’17 paper on scaling semi-supervised architecture to 600,000 cores, 15PF performance •2 papers under review –BAMS article: “Deep Learning for Detecting Extreme Weather Patterns” – NIPS paper: “ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events” •Upcoming papers – Front detection – Atmospheric river detection - 34 -
- 35. Next Steps • Goal: Create unified architecture for finding extreme weather in all climate datasets • Create schema and reference dataset for climate community; release reference architecture • Hybrid LSTM + CNN architectures for storm tracking • Classification -> Localization -> Detection -> Segmentation - 35 -
- 36. Open Challenges • Hyper-Parameter Optimization – Tuning #layers, #filters, learning rates, schedule is a black art • Performance and Scaling – Current networks take days to train on O(10) GB datasets, we have O(10TB) datasets on hand • Scarcity of Labeled Data – Community needs to self-organize and run labeling campaigns – Need some theory around limits of semi-supervised learning • Interpretability and Visualization – ‘Black Box’ classifier – Need to incorporate domain science principles (physical consistency, etc) - 36 -
- 37. Outline • • – • – •Future of AI in Climate Science - 37 -
- 38. Timeline of 2 communities - 38 - 1970 1980 1990 2000 2010 • Neural Nets • Multi-layer perceptrons, Backpropagation • Eigenface • First principles Physics, Geometry • Random Forests • Hand-tuned features (SIFT, HOG, …) • Edge, Corner detectors • SVMs, Machine Learning • PASCAL VOC • ImageNet • ILSVRC • AlexNet • ResNet • … • Hand-tuned features, first principles physics,… • IMILAST, ARTMIP Pattern Detection in Computer Vision Pattern Detection in Climate Science
- 40. 2018-2020 •Climate Science community will self-organize and conduct labeling campaigns – Datasets and reference architectures will become easily available •Researchers will exploit low-hanging fruit – Classification, Clustering, Regression problems will be (nearly) completely solved •Broad applicability for analyzing observational (satellite, weather station) and model (reanalysis, CMIP-6) output - 40 -
- 41. 2020+ •Broad Deployment of tools at HPC centers, Cloud and universities – Interactive analytics on 100TB-PB datasets •Entire climate archives are segmented and classified – Anomaly detection; Correlation; Causal Analysis •Hard questions are formulated and addressed – Interpretability, incorporating domain science principles • What is the ‘value add’ of the climate data analyst? - 41 -
- 42. 2020+ Climate Analyst Workflow - 42 - CMIP archives Observational Data • Interactive Exploration • Semantic Labels • Patterns • Clusters • Anomalies • Mechanisms • Hypothesis
- 43. Conclusions • Characterizing Extreme Weather in future climate regimes is an important goal • Deep Learning can be applied effectively to solve pattern recognition problem – Supervised and semi-supervised CNNs – Segmentation and LSTM architectures in development • Unique challenges – Performance and Scaling, Interpretability, Hyper-parameter Optimization, Lack of labeled data • Potential for innovation and impact, open to collaboration! - 43 -
- 44. Acknowledgements • Intel Research: Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Amir Khosrowshahi, Pradeep Dubey, Joe Curley • Stanford: Ioannis Mitliagkas, Jian Yang, Yunjie Liu, Chris Re • UC Berkeley: Mayur Mudigonda • MILA: Evan Racah, Chris Beckham, Tegan Maharaj, Chris Pal • LBNL: Thorsten Kurth, Wahid Bhimji, Michael Wehner, William Collins • NOAA: Jim Biard, Ken Kunkel - 44 -