Learning climate dynamics with spatiotemporal neural networks.
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.
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.
To introduce the fundamentals of climate anomalies and spatiotemporal data analysis.
To develop understanding of deep learning architectures (CNN, LSTM, ConvLSTM, Transformers) for climate prediction.
To train participants in handling real-world climate datasets (NetCDF, satellite, reanalysis data).
To build and evaluate spatiotemporal models for forecasting extreme climate events.
To enable participants to design AI-driven early warning and climate risk assessment systems.
📅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
Hands-on
📅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
Hands-on
📅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
Hands-on
February 27, 2026
IST 4:30 IST
February 27, 2026 – March 1, 2026
IST 5 :30 PM IST
Analyze and preprocess large-scale spatiotemporal climate datasets.
Develop and implement deep learning models for climate anomaly prediction.
Apply architectures such as CNN-LSTM, ConvLSTM, and Transformer-based models to real-world climate problems.
Evaluate model performance using appropriate forecasting and anomaly detection metrics.
Design AI-driven frameworks for early warning systems and climate risk analytics.
₹2,499
$50
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
Live, mentor-led and certified. Dates and times in IST.
