1 research outputs found
Towards a Precipitation Bias Corrector against Noise and Maldistribution
With broad applications in various public services like aviation management
and urban disaster warning, numerical precipitation prediction plays a crucial
role in weather forecast. However, constrained by the limitation of observation
and conventional meteorological models, the numerical precipitation predictions
are often highly biased. To correct this bias, classical correction methods
heavily depend on profound experts who have knowledge in aerodynamics,
thermodynamics and meteorology. As precipitation can be influenced by countless
factors, however, the performances of these expert-driven methods can drop
drastically when some un-modeled factors change. To address this issue, this
paper presents a data-driven deep learning model which mainly includes two
blocks, i.e. a Denoising Autoencoder Block and an Ordinal Regression Block. To
the best of our knowledge, it is the first expert-free models for bias
correction. The proposed model can effectively correct the numerical
precipitation prediction based on 37 basic meteorological data from European
Centre for Medium-Range Weather Forecasts (ECMWF). Experiments indicate that
compared with several classical machine learning algorithms and deep learning
models, our method achieves the best correcting performance and meteorological
index, namely the threat scores (TS), obtaining satisfactory visualization
effect