Training and tuning of hyper-parameters

3 classes : no front, warm front and cold front60 epochs, 1340 images of 200x420 pixels splitted in 107 200 images of 48x48 pixelsLoss function : weigthed categorical cross-entropy, Weigths : 1 for "no front" class and 10 for "warm front" and "cold front" classes

Ground truth : surface charts from Météo-France produced 4 times a day

Data : 1 year of analyses from Arpège (French global model) with a resolution of 0.1°

Testing dataset : from 01/02/2020 to 29/02/2020, 116 images

Training and validation dataset :

1340 images (2/3 for training, 1/3 for validation) from 01/07/2019 to 31/01/2020 and from 01/03/2020 to 01/07/2020

Datasets

Ground truth

Results

Surface chart 24/02/2020 00 UTC

extracted from surface chart + θ' w at 850hPa

Prediction CNN + θ' w at 850hPa

Issues Surface charts are drawn using information different from Arpège analyses

(other model analyses, observations, etc...).Data formats changed in 2019. Other preprocessing routines must be developed

to use the previous data.Imbalanced dataset : 80 "no front" pixels for 1 "cold front" or "warm front" pixel Quality of the prediction very sensitive to the activation function.

32 32

64 64

128 128

64 64

32 32

16 161 16 16

48x4

8

24x2

4

12x1

2

6x6

N_cl N_cl

conv 3x3, ReLu, drop out 20%copy and concatenatemax pool 2x2upsample 2x2conv 1x1, ReLusoftmax

U-Net architecture

Adapted from Ronneberger et al, 2015

Automatic fronts detection using a convolutional neural network

Lucie ROTTNER, Laure RAYNAUD, Arnaud MOUNIER, Matthieu PLU

have been used to detect weather fronts (Biard and Kunkel, 2019, Lagerquist et al, 2019, Matsukoa et al, 2019). Here we present a front dectection First attempts at automatic drawing of fronts and other synoptic-scale features were based on physical rules. Recently, convolutional neural networks

method for the French operationnal global model, Arpège. A U-Net is used to detect cold and warm fronts over the western Atlantic.

ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, 5-8 octobre 2020

References

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net : Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 2015

James C Biard and Kenneth E Kunkel. Automated detection of weather fronts using a deep learning neural network. Advances in Statistical Climatology, Meteorology and Oceanography, 5(2) :147–160, 2019.Ryan Lagerquist, Amy McGovern, and David John Gagne II. Deep learning for spatially explicit prediction of synoptic-scale fronts. Weather and Forecasting, 34(4) :1137–1160, 2019.Daisuke Matsuoka, Shiori Sugimoto, Yujin Nakagawa, Shintaro Kawahara, Fumiaki Araki, Yosuke Onoue, Masaaki Iiyama, and Koji Koyamada. Automatic detection of stationary fronts around japan using a deep convolutional neural network. SOLA, 2019.

Centre National de Recherches Météorologiques, UMR 3589 CNRS/Météo-France, Toulouse, Francelucie.rottner@meteo.fr

Data pre-processing

Front extraction from surface charts :

Geographical domain : 32°W-10°E and 40°N-60°N

extraction of front coordinates from json file interpolation on the ground truth grid

Data normalisation : normalisation min-max

Meteorological parameters

Mean sea level pressure

Pseudo-adiabatic potential temperature at 850hPa (θ' w)

10m wind (u and v components)

Absolute vorticity at 850hPa and 700 hPa

Relative humidity at 700 hPa

To be done

Evaluation by forecasters

Improving visualisation :

Adding front classes : occluded fronts,

Using longer datasets

quasi-stationnary fronts etc...

N_cl = 3 classes