{'fraction_non_alphanumeric': 0.01606425702811245, 'fraction_numerical': 0.0, 'mean_word_length': 5.592592592592593, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Interpolative picture fuzzy rules: A novel forecast method for weather nowcasting

Weather nowcasting is a short-range forecasting that maps current weather, then uses an estimation of its speed and direction of movement to forecast weather in a short period ahead - assuming the weather will move without significant changes. It operates through latest radar, satellite or observational data. However, flawed characterization of transitions between different meteorological structures is its main challenges. In this paper, an innovative method for weather nowcasting from satellite image sequences using the combination of picture fuzzy clustering and interpolative fuzzy rules is proposed. Firstly, picture fuzzy clustering algorithm, a fuzzy clustering method based on the theory of picture fuzzy set, is used to partition the satellite image pixels into clusters. Secondly, the interpolative trapezoidal picture fuzzy rules are created from the clusters. Finally, particle swarm optimization is employed to train the defuzzified parameter from the rules to enhance the accuracy of the predicted satellite images in sequence. The experimental results indicate that the proposed method is better than the relevant ones for weather nowcasting.