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Increasing Fine-Scale Temperature Details from Weather Model Forecasts
computer science modeling algorithms meteorology data
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13 months ago
Small pic screen shot 2019 01 02 at 3.05.37 pm

The field of meteorology constantly strives to achieve better weather forecasts by increasing the level of detail it can predict using sophisticated physics algorithms and supercomputers. Unfortunately, even with large supercomputers, there is an upper limit to the amount of physics-based models that can be computed in a timely manner. To improve on those limitations, meteorologists often use statistical techniques. We now seek to leverage more powerful AI techniques in order to achieve even better improvements in the forecasts.  

This challenge is to increase the resolution (the level of detail)of 2D surface temperature forecasts obtained from Environment and Climate Change Canada (ECCC) ’s weather forecast model, using as labelled images 2D temperature analysis at higher resolution. The scale factor between the model and the higher resolution analysis is 4 (from 10 km to 2.5 km). These weather forecast gridded fields are just like images and we believe that Computer Vision algorithms would likely be excellent approaches to use.