weather forecasting thesis link fix

Author: RobertoNeglia

_theses/met-no_dataset.md

@@ -8,15 +8,15 @@ contact_person: "Filippo Maria Bianchi, Roberto Neglia"
 
 ## 📝 Description
 The project focuses on applying advanced deep learning techniques to forecast spatiotemporal weather patterns and events.
-The student will work with the data from the [MET Norway](https://www.met.no/en) weather API, which provides high-resolution weather forecasts and historical observations. Once the data is prepared and pre-processed by building a spatiotemporal graph representation, the student will implement and fine-tune Spatiotemporal Graph Neural Networks (STGNNs) models using PyTorch and [Torch Spatiotemporal](https://torch-spatiotemporal.readthedocs.io/en/latest/), experimenting with different architectures and hyperparameters. See for instance [PeakWeather](https://arxiv.org/html/2506.13652v1) ([github](https://github.com/MeteoSwiss/PeakWeather)) on weather forecasting with graph neural networks using data from MeteoSwiss.
+The student will work with the data from the [MET Norway](https://www.met.no/en) weather API, which provides high-resolution weather forecasts and historical observations. Once the data is prepared and pre-processed by building a spatiotemporal graph representation, the student will implement and fine-tune Spatiotemporal Graph Neural Networks (STGNNs) models using PyTorch and [Torch Spatiotemporal](https://torch-spatiotemporal.readthedocs.io/en/latest/), experimenting with different architectures and hyperparameters. See for instance [PeakWeather](https://arxiv.org/abs/2506.13652v1) ([github](https://github.com/MeteoSwiss/PeakWeather)) on weather forecasting with graph neural networks using data from MeteoSwiss.
 
 By the end of the project, the student will have developed a strong understanding of meteorological data pre-processing, graph-based modeling, spatiotemporal deep learning, and its application to real-world weather forecasting tasks, which can be further explored in future research, as well as potential experience in deploying these models in production environments.
 
 **Data:** The student will use raw high-resolution weather data from the MET Norway API, which includes various meteorological variables such as temperature, precipitation, and wind speed.
 
 ## 📚 References:
 - [Torch Spatiotemporal](https://torch-spatiotemporal.readthedocs.io/en/latest/)
-- [PeakWeather](https://arxiv.org/html/2506.13652v1)
+- [PeakWeather](https://arxiv.org/abs/2506.13652v1)
 - [MeteoSwiss/PeakWeather github](https://github.com/MeteoSwiss/PeakWeather)
 
 ## 📨 Contact: