Weather forecast apparatus and method based on recognition of echo patterns of radar images

A weather forecast method for providing a weather radar image to an FNN model, which is a neural network model for learning and forecasting, so that the model learns weather dynamics and forecasts weather by using the FNN model after the learning, the method performing pattern recognition of the radar image by using a TNN model that is a neural network model for transforming feature quantities which were input into the TNN model, comprising the steps of:

• calculating and extracting one or more feature quantities from a radar image;
• providing the feature quantities extracted in the calculating and extracting step for the TNN model as input and teacher data, and learning identical transformation from the feature quantities to the feature quantities;
• calculating an estimated value based on a criterion for recognition from the output feature quantities that were identically transformed and output from the TNN model;
• adding perturbation to the output feature quantities;
• performing re-learning of the TNN model such that the estimated value is maximized or minimized;
• pattern-recognizing a pre-learned image by providing feature quantities calculated and extracted from the pre-learned image for the TNN model after the re-learning;
• and
• forecasting and outputting a future radar image based on result of the pattern recognition of the pre-learned image.

A weather forecast method for providing a weather radar image to an FNN model, which is a neural network model for learning and forecasting, so that the model learns weather dynamics and forecasts weather by using the FNN model after the learning, the method performing systematic classification of the radar image by using a PNN model, which is a neural network model for pattern classification, and selects reference vectors based on input feature quantities, comprising the steps of:

• calculating and extracting plural feature quantities from the radar image;
• providing the feature quantities extracted in the calculating and extracting step for the PNN model, and performing learning of the PNN model by finding a minimum value of an object function such that the quantization error of each reference vector which belongs to each output unit of the PNN model is minimized;
• classifying a pre-learned image by providing the feature quantities calculated and extracted from the pre-learned image for the PNN model after the learning;
• and
• forecasting and outputting a future radar image based on results of the classification of the pre-learned image.

A weather forecast apparatus for providing a weather radar image to an FNN model, which is a neural network model for learning and forecasting, so that the model learns weather dynamics and forecasts weather by using the FNN model after the learning, comprising:

• feature quantity calculating means for calculating and extracting one or more feature quantities from a radar image;
• a TNN model, which is a neural network model, for transforming and outputting the feature quantities that are input from the feature quantity calculating means;
• perturbation generating means for generating an amount of perturbation;
• means for calculating an estimated value from the feature quantities output from the TNN model, based on a criterion for recognition;
• learning means for providing the feature quantities extracted by the feature quantity calculating means for the TNN model as input and teacher data, for performing identical transformation from the feature quantities to the feature quantities, for adding the amount of perturbation to the output feature quantities after the transformation, and for performing re-learning of the TNN model such that the estimated value is maximized or minimized;
• recognizing means for pattern-recognizing a pre-learned image according to the estimated value that is obtained when feature quantities calculated and extracted from the pre-learned image are input into the TNN model after the re-learning;
• and
• forecast means for forecasting and outputting a future radar image based on result of the pattern recognition of the pre-learned image.

A weather forecast apparatus for providing a weather radar image to an FNN model, which is a network model for learning and forecasting, so that the model learns weather dynamics and forecasts weather by using the FNN model after the learning, comprising:

• feature quantity calculating means for calculating and extracting plural feature quantities from a radar image;
• a PNN model, which is a neural network model for pattern classification, for selecting reference vectors based on the feature quantities that are input from the feature quantity calculating means;
• learning means for providing the feature quantities extracted by the calculating and extracting means for the PNN model, and for performing learning of the PNN model by finding a minimum value of an object function such that the quantization error of each reference vector which belong to each output unit of the PNN model is minimized;
• classifying means for classifying a class of the image according to the reference vectors that were selected by the PNN model, wherein systematic classification of a pre-learned image is performed by inputting the feature quantities calculated and extracted;
• from the pre-learned image into the PNN model after the learning and
• forecast means for forecasting and outputting a future radar image based on result of the classification of the pre-learned image.