A comparative study of algorithms for lossy compression of 2-d meteorological gridded fields

Meteorological observations (e.g. from weather radar) and the output of meteorological models (e.g. from reanalyses or forecasts) are often stored and used in the form of time series of 2-d spatial gridded fields. With increasing spatial and temporal resolution of these products, and with the transition from providing single deterministic fields to providing ensembles, their size has dramatically increased, which makes use, transfer and archiving a challenge. Efficient compression of such fields - lossy or lossless - is required to solve this problem.

The goal of this work was therefore to apply several lossy compression algorithms for 2d spatial gridded meteorological fields, and to compare them in terms of compression rate and information loss compared to the original fields. We used five years of hourly observations of rainfall and 2m air temperature on a 250 x 400 km region over central Germany on a 1x1 km grid for our analysis.

In particular, we applied block averaging as a simple benchmark method, Principal Component Analysis, Autoencoder Neural Network (Hinton and Salakhutdinov, 2006) and the Ramer-Douglas-Peucker algorithm (Ramer, 1972; Douglas and Peucker, 1973) known from image compression. Each method was applied for various compression levels, expressed as the number of objects of the compressed representation, and then the (dis-)similarity of the original field and the fields reconstructed from the compressed fields was measured by Mean Absolute Error, Mean Square Error, and the Image Quality Index (Wang and Bovik, 2002). First results indicate that even for spatially heterogeneous fields like rainfall, very high compression can be achieved with small error.

 

References

Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. In: The Canadian Cartographer. Bd. 10, Nr. 2, 1973, ISSN 0008-3127, S. 112–122, 1973.

Hinton, G. E., & Salakhutdinov, R. R.: Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507, 2006.

Ramer, U.: An iterative procedure for the polygonal approximation of plane curves, Computer Graphics and Image Processing, 1, 244-256, http://dx.doi.org/10.1016/S0146-664X(72)80017-0, 1972.

Zhou Wang, and A. C. Bovik: A universal image quality index, IEEE Signal Processing Letters, 9, 81-84, 10.1109/97.995823, 2002.