MetNet: A Neural Weather Model for Precipitation Forecasting

Weather has an enormous impact on renewable energy and markets, which is expected to reach 80% of the world’s electricity production. There are many social and economic benefits of accurate weather forecasting, from improvements in our daily lives to substantial impacts on agriculture, energy and transportation and to the prevention of human and economic losses through better prediction of hazardous conditions such as storms and floods. However, weather forecasting (i.e. prediction of future weather conditions such as precipitation, temperature, pressure, and wind) is a long-standing scientific challenge.

Most of the weather forecasting methods that are used by meteorological agencies are based on physical models of the atmosphere. Although these methods have seen substantial advances over the preceding decades, they are inherently constrained by their computational requirements and are sensitive to approximations of the physical laws used in them. An alternative approach for modeling weather in order to predict the future condition is using deep neural networks, where instead of explicitly encoding physical laws in our model, we can design neural networks that discover patterns in the data and learn complex transformations from inputs to the outputs. Besides, given the infrastructure that is built for serving neural models, like accelerators, neural weather prediction models can be substantially faster than physics-based models.  

Along this direction, we introduce MetNet, a neural weather model for precipitation forecasting. MetNet outperforms HRRR, which is the current state-of-the-art physics-based model in use by HRRR for predicting future weather condition up to 8 hours ahead, and in terms of speed, the latency of the model is a matter of seconds as opposed to an hour.

MetNet Architecture

We quantify the difference in performance between MetNet, HRRR, and the optical flow baseline model evaluated using the F1-score at a precipitation rate threshold of 1.0 mm/h, which corresponds to light rain. The MetNet neural weather model is able to outperform the NOAA HRRR system at timelines less than 8 hours and is consistently better than the flow-based model.MetNet receives as input a four-dimensional tensor of size [t, w, h, c] that corresponds to data from a large patches with dimensions time, height, width and number of channels. The time dimension comprises t slices sampled every 15 minutes over a 90 minutes interval prior to T_x where T_x is the time at which the model makes a prediction into the future. The input data is calculated from a patch covering a geographical area of $1024 \times 1024$ kilometers. The input features comprise the single MRMS [1]Multi-Radar/Multi-Sensor System radar image, the 16 spectral bands of the GOES-16 satellite and additional real-valued features for the longitude, latitude and elevation of each location in the patch as well as for the hour, day and month of the input time T_x

MetNet makes a prediction for a single lead time. To do so, we inform the model about the desired lead time by concatenating this information with the descriptive input features.  Using this conditioning, by changing the target lead time given as input, one can use the same MetNet model to make forecasts for the entire range of target times that MetNet is trained on.

  • Spatial Downsampler: MetNet aims at fully capturing the spatial context in the input patch. A trade-off arises between the fidelity of the representation and the memory and computation required to compute it. To maintain viable memory and computation requirements, the first part of MetNet contracts the input tensor spatially using a series of convolution and pooling layers. The t slices along the time dimension of the input patch are processed separately.
  • Temporal Encoder: The second part of MetNet encodes the input patch along the temporal dimension. The t spatially contracted slices are given to a recurrent neural network following the order of time. The result is a single tensor where each part of the tensor summarizes spatially and temporally one region of the large context in the input patch.
  • Spatial Aggregator: To make MetNet’s receptive field cover the full global spatial context in the input patch, the third part of MetNet uses a series of axial self-attention blocks along the width and height of the input.


We evaluate MetNet on a precipitation rate forecasting benchmark and compare the results with two baselines — the NOAA High Resolution Rapid Refresh (HRRR) system, which is the physical weather forecasting model currently operational in the US, and a baseline model that estimates the motion of the precipitation field (i.e., optical flow), a method known to perform well for prediction times less than 2 hours. We quantify the difference in performance between MetNet, HRRR, and the optical flow baseline model evaluated using the F1-score at a precipitation rate threshold of 1.0 mm/h, which corresponds to light rain. We quantify the difference in performance between MetNet, HRRR, and the optical flow baseline model evaluated using the F1-score at a precipitation rate threshold of 1.0 mm/h, which corresponds to light rain. 

The MetNet neural weather model is able to outperform the NOAA HRRR system at timelines less than 8 hours and is consistently better than the flow-based model.

To know more about MetNet, please check our paper:

Or read this Google AI blog post about MetNet.

References

References
1 Multi-Radar/Multi-Sensor System