DL-FRONT MERRA-2 weather front probability maps over North America, 1980-

DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.

This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.

At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).

The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) Coded Surface Bulletin dataset. The training datasets covered the years 2003-2007.

The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.

The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.