A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns
Year: 2018
Type: preprint
Abstract: Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying and identifying patterns in climate and environmental data. However, because of the inherent complexities of such data, which are often spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be designed/evaluated for each specific dataset and application. Yet to start, CNN, a supervised technique, requires a large labeled dataset. Labeling demands (human) expert time, which combined with the limited number of relevant examples in this area, can discourage using CNNs for new problems. To address these challenges, here we 1) Propose an effective auto-labeling strategy based on using an unsupervised clustering algorithm and evaluating the performance of CNNs in re-identifying these clusters; 2) Use this approach to label thousands of daily large-scale weather patterns over North America in the outputs of a fully-coupled climate model and show the capabilities of CNNs in re-identifying the 4 clustered regimes. The deep CNN trained with 1000 samples or more per cluster has an accuracy of 90% or better. Accuracy scales monotonically but nonlinearly with the size of the training set, e.g. reaching 94% with 3000 training samples per cluster. Effects of architecture and hyperparameters on the performance of CNNs are examined and discussed.
Authors: Ashesh Chattopadhyay, Pedram Hassanzadeh, Saba Pasha
Institutions: Rice University, University of Pennsylvania
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Awards: Atmospheric Blocking: Dynamics and Responses to Climate Change, G4805370682, G5781760129, ATMOSPHERIC BLOCKING EPISODES ARE IMPORTANT CONTRIBUTORS TO HIGH-IMPACT EXTREME WEATHER AND ARE A PRIME TARGET OF SUB-SEASONAL PREDICTION. INTRINSIC PREDICTABILITY OF BLOCKING AND SOURCES OF PREDICTION ERRORS HAVE NOT BEEN WELL CHARACTERIZED WHICH REPRESENT AN OPPORTUNITY FOR IMPROVED MEDIUM RANGE WEATHER FORECASTS. WE PROPOSE A MULTI-PRONGED APPROACH TO BETTER UNDERSTAND PREDICTABILITY AND TO IMPROVE PREDICTION OF ATMOSPHERIC BLOCKING EVENTS AND THE ASSOCIATED EXTREME WEATHER EVENTS. TWO SUITES OF MODELS BASED ON THE GODDARD EARTH OBSERVING SYSTEM MODEL (GEOS) AND THE COMMUNITY ATMOSPHERE MODEL (CAM) WILL BE USED. WE WILL FIRST EVALUATE THE PERFORMANCE OF THE MODELS AGAINST THE MERRA2 AND ERA INTERIM REANALYSES AND ADDITIONAL SATELLITE OBSERVATIONS FROM THE GLOBAL PRECIPITATION ACTIVE PASSIVE (SMAP) GLOBAL PRECIPITATION MISSION (GPM) AND THE TROPICAL RAINFALL MEASURING MISSION (TRMM). WE WILL EVALUATE THE MODELS ABILITY TO SIMULATE BLOCKING AND WEATHER EXTREMES CLIMATOLOGY TO FAITHFULLY CAPTURE BLOCKING DYNAMICS AT A PROCESS LEVEL AND IN THE CASE OF THE GEOSBASED MODELS THE ABILITY TO FORECAST BLOCKING EVENTS AND THE RESULTING WEATHER EXTREMES WITH THE MODELS DATA ASSIMILATION SYSTEM. WE WILL THEN CARRY OUT INTRINSIC PREDICTABILITY STUDIES USING A PERFECT MODEL APPROACH AS WELL AS CROSS-MODEL PREDICTION STUDIES. THE SUITES OF MODELS THAT WE USE HAVE VERSIONS THAT DIFFER ONLY IN THEIR REPRESENTATIONS OF THE STRATOSPHERE MOIST PROCESSES OR SOIL MOISTURE AND THEREFORE ALLOW US TO ISOLATE THE CONTRIBUTION OF THESE MODEL COMPONENTS ON PREDICTABILITY AND PREDICTION ERRORS. THE EFFECT OF NUMERICAL RESOLUTION WILL ALSO BE EXPLORED. THE USE OF BOTH THE GEOS- AND CAM-BASED MODELS WILL ALLOW CROSS COMPARISONS AND ASSESSMENTS OF THE ROBUSTNESS OF THE RESULTS ACROSS MODELS. LASTLY WE WILL GENERALIZE OUR RESULTS TO A BROADER RANGE OF MODELS IN THE SUBSEASONAL-TO-SEASONAL PREDICTION PROJECT DATASET. THE PROPOSED RESEARCH WILL ENHANCE BOTH NASA MODELING CAPABILITY AND THE SCIENCE RETURN ON NASA S INVESTMENT IN MODELING AND SPACE-BASED OBSERVATIONS IN THE CRITICAL AREA OF PREDICTING EXTREME WEATHER.
OpenAlex ID: W2899751676