|A) During training, a fraction of the weather stations are masked out from the input while kept in the target. B) To evaluate generalization to untrained locations, a set of weather stations represented by squares is never used for training and is only used for evaluation. C) Data from these held out weather stations with sparse coverage is included during evaluation to determine prediction quality in these areas. D) The final forecasts use the full set of training weather stations as input and produce fully dense forecasts aided by spatial parameter sharing.|
|Top: MetNet-3’s forecast of wind speed for each 2 minutes over the future 24 hours with a spatial resolution of 4km. Bottom: ENS’s hourly forecast with a spatial resolution of 18 km.|
The two distinct regimes in spatial structure are primarily driven by the presence of the Colorado mountain ranges. Darker corresponds to higher wind speed. More samples available here: 1, 2, 3, 4.
|Case study for Thu Jan 17 2019 00:00 UTC showing the probability of instantaneous precipitation rate being above 1 mm/h on CONUS. Darker corresponds to a higher probability value. The maps also show the prediction threshold when optimized towards Critical Success Index CSI (dark blue contours). This specific case study shows the formation of a new large precipitation pattern in the central US; it is not just forecasting of existing patterns.|
Top: ENS’s hourly forecast. Center: Ground truth, source NOAA’s MRMS. Bottom: Probability map as predicted by MetNet-3. Native resolution available here.
No the actual canva online app. Looks like there is a chatgpt I don't know about for it! Good looking out!