WeatherFusionNet: Predicting Precipitation from Satellite Data (2211.16824v1)

Abstract: The short-term prediction of precipitation is critical in many areas of life. Recently, a large body of work was devoted to forecasting radar reflectivity images. The radar images are available only in areas with ground weather radars. Thus, we aim to predict high-resolution precipitation from lower-resolution satellite radiance images. A neural network called WeatherFusionNet is employed to predict severe rain up to eight hours in advance. WeatherFusionNet is a U-Net architecture that fuses three different ways to process the satellite data; predicting future satellite frames, extracting rain information from the current frames, and using the input sequence directly. Using the presented method, we achieved 1st place in the NeurIPS 2022 Weather4Cast Core challenge. The code and trained parameters are available at \url{https://github.com/Datalab-FIT-CTU/weather4cast-2022}.

• The paper presents WeatherFusionNet, a novel deep learning model that fuses multiple U-Net based modules to predict short-term precipitation from satellite imagery.
• It combines a sat2rad module, a recurrent PhyDNet, and a composite U-Net to achieve higher IoU scores than traditional radar-dependent approaches.
• Empirical evaluations using data from seven European regions in the Weather4Cast challenge demonstrate robust performance in radar-sparse areas.

Essay on "WeatherFusionNet: Predicting Precipitation from Satellite Data"

The paper "WeatherFusionNet: Predicting Precipitation from Satellite Data" introduces an innovative approach to short-term precipitation prediction using satellite radiance images. The authors describe WeatherFusionNet, a tailored neural network model designed for processing satellite input in order to generate high-resolution precipitation forecasts. This contributes significantly to the field of meteorological predictions, particularly in regions where the availability of weather radar data is limited or nonexistent.

In addressing the problem of nowcasting, the methodology capitalizes on recent advancements in deep learning, particularly the U-Net architecture known for its efficacy in image-to-image translation tasks. WeatherFusionNet processes satellite data through three complementary modules: sat2rad, a U-Net model leveraging the immediate time-step information; a recurrent convolutional framework (PhyDNet) designed for extending the input sequence by predicting satellite imagery; and a composite U-Net module that integrates outputs from the previous two with the initial input sequence to deliver the final predictive model. This architectural fusion explicitly aims at overcoming the traditional limitations of radar-dependent approaches, offering an alternative that is based exclusively on satellite information.

The challenge addressed is inherently complex due to the low spatial resolution of the input and the necessity for high fidelity in output. The data used in development come from the Weather4Cast competition, involving satellite and radar images from seven European regions, highlighting the model's applicability to large-scale geographic variability. Training involved the formulation of a binary classification task across numerous sequences, necessitating sophisticated data handling and model tuning strategies.

Empirical evaluations demonstrate robust performance, with WeatherFusionNet achieving the highest scores in the NeurIPS 2022 Weather4cast Core Challenge. Metric comparisons reveal that WeatherFusionNet outperforms baseline models, particularly regarding intersection over union (IoU) measures, underscoring the effectiveness of the fusion strategy applied within its architecture. Specifically, WeatherFusionNet achieved validation IoU gains over standalone U-Net implementations, which underscores its composite design's added value.

Despite the success, the authors recognize room for additional exploration, particularly in upscaling mechanisms—crucial for preserving detail in regression scenarios—and potential end-to-end training to further enhance the model's robustness and capacity. Furthermore, leveraging radar data directly, when available, may offer avenues for streamlining or bypassing certain processes such as sat2rad estimation.

In conclusion, WeatherFusionNet represents a tangible advancement in satellite-based precipitation prediction methodologies. The integration of diverse processing approaches within a unified framework aligns well with the ongoing evolution toward satellite-predominant predictive models, particularly addressing the challenge of severe weather forecasting in radar-void locales. Future developments may build on this groundwork by investigating extended data sources and refining module interoperability, holding promise for further operational enhancements in meteorological predictive analytics.