Research and application progress of deep learning in precipitation forecasting
This article summarizes the characteristics of commonly used deep learning algorithms in the meteorological field,as well as the exploration results of pure data-driven deep learning precipitation forecasting technology and deep learning coupled with numerical weather forecasting technology in near,medium-short-term,and extended period precipitation forecasting.At the same time,a brief review is made on the application of deep learning in meteorological operational forecasting.The advantages and disadvantages of traditional deep learning algorithms are different.In practical applications,it is necessary to choose appropriate models and methods based on specific meteorological data characteristics and business needs.In terms of proximity prediction,deep learning algorithms are often used to establish precipitation prediction models for extracting spatial features and analyzing temporal evolution of heavy precipitation cloud clusters;Long time series(medium to short term,extended period)precipitation forecasting mainly optimizes the numerical model precipitation forecasting effect through initial field data assimilation,model improvement,and model prediction post-processing.At present,meteorological departments in various regions have applied deep learning algorithms in business forecasting.In the future,specific problems still need to be addressed,a large amount of related work needs to be carried out to further promote the development of precipitation forecasting.
deep learningprecipitation forecastnumerical weather forecastingmodes coupling
万方数据
维普
