Visualization of Neural Network Predictions for Weather Forecasting
Problem
A single loss value hides variations in recurrent neural network weather forecasts, making it hard to tune hyperparameters and understand model behavior on large datasets.
Why it matters
Accurate weather forecasting depends on well-tuned, trustworthy models; opaque errors undermine both.
What the AI did
An interactive coarse-to-fine visualization system lets users study RNN outputs on training and testing data and compare different hyperparameter settings, applied to weather time series.
Results
not stated in source
Impact
The tool supports hyperparameter adjustment and comparison for neural weather forecasting models.
Record
| Venue | Computer Graphics Forum |
|---|---|
| Published | 2018 |
| Work type | article |
| Authors | Isabelle Roesch, Tobias Günther |
| Citations | 28 (OpenAlex, at import time) |
| References | 27 |
| Model or method | recurrent neural networks |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1111/cgf.13453 |
| Open access | green |
| Keywords | recurrent neural networks, visualization, weather forecasting, hyperparameters, time series |
Importance rating
incremental / niche
Visualization tool for inspecting RNN weather-forecast errors and hyperparameters; useful for model tuning but a niche methodological advance with no stated scale.
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