Spatiotemporal Deep Learning for Climate Anomaly Prediction
Learning climate dynamics with spatiotemporal neural networks.
Skills you will gain:
About Program:
Climate anomalies such as heatwaves, extreme rainfall, droughts, cyclones, and unexpected seasonal shifts are increasing in frequency and intensity. Accurate prediction of these events requires models that understand both spatial relationships (geographic patterns) and temporal dynamics (time evolution) of climate data.
This advanced workshop focuses on Spatiotemporal Deep Learning techniques for predicting climate anomalies using modern AI architectures. Participants will learn how to build models that capture complex climate patterns across space and time using neural networks designed specifically for geospatial and time-series data.
Aim: The aim of this workshop is to equip participants with the theoretical understanding and practical skills required to design, implement, and evaluate spatiotemporal deep learning models for accurate climate anomaly prediction. The program focuses on integrating climate science with advanced AI techniques to enable early detection of extreme events and support data-driven climate risk assessment and decision-making.
Program Objectives:
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To introduce the fundamentals of climate anomalies and spatiotemporal data analysis.
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To develop understanding of deep learning architectures (CNN, LSTM, ConvLSTM, Transformers) for climate prediction.
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To train participants in handling real-world climate datasets (NetCDF, satellite, reanalysis data).
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To build and evaluate spatiotemporal models for forecasting extreme climate events.
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To enable participants to design AI-driven early warning and climate risk assessment systems.
What you will learn?
📅Day 1 –The Setup & Data Preparation
Ingest and structure multi-terabyte NetCDF/HDF5 climate datasets. Learn robust workflows for
spatial slicing, interpolation/regridding, and temporal gap handling
to create model-ready tensors.
Core Topics
- •Efficient I/O, chunking, and lazy loading (Xarray)
- •Spatial subsetting, coordinate systems, regridding basics
- •Temporal alignment, missing values, smoothing strategies
- •Train/validation splits for time-dependent datasets
Hands-on
- •Load ERA5 variables and slice a target region
- •Build a clean spatiotemporal cube (time × lat × lon × channels)
- •Implement interpolation + gap filling for missing timestamps
- •Export training-ready arrays / Zarr pipeline (optional)
📅Day 2 – Core AI Implementation: ConvLSTM Modeling
Train a ConvLSTM model that learns spatial climate patterns and
temporal evolution. Convert reanalysis grids into input-output sequences and build a stable,
research-grade training pipeline.
Core Topics
- •Forecast framing: anomaly prediction objectives
- •Windowing: lookback, horizon, multi-channel inputs
- •ConvLSTM architecture design (depth, kernels, filters)
- •Regularization, tuning, early stopping, validation strategy
Hands-on
- •Create supervised sequences from ERA5 tensors
- •Build and train a ConvLSTM model in TensorFlow/Keras
- •Track learning curves, loss, and overfitting controls
- •Generate baseline forecasts for comparison (simple persistence)
📅Day 3 – Results, Visualization & Paper Readiness
Convert forecasts into publication-quality anomaly maps and extract standard evaluation metrics
(RMSE, spatial correlation). End with outputs formatted for your methods/results sections.
Core Topics
- •Forecast interpretation: anomalies vs absolute values
- •Geospatial visualization workflows with Cartopy
- •RMSE + spatial correlation (grid-wise & aggregated)
- •Reproducibility: seeds, configs, reporting template
Hands-on
- •Generate heatmaps of forecasted anomalies (region-based)
- •Create automated plots + export figures for papers
- •Compute RMSE + spatial correlation and tabulate results
- •Draft methods-ready evaluation text (template provided)
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes
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Analyze and preprocess large-scale spatiotemporal climate datasets.
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Develop and implement deep learning models for climate anomaly prediction.
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Apply architectures such as CNN-LSTM, ConvLSTM, and Transformer-based models to real-world climate problems.
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Evaluate model performance using appropriate forecasting and anomaly detection metrics.
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Design AI-driven frameworks for early warning systems and climate risk analytics.