Method for identifying potential supercooled water zone through satellite-borne multi-parameter ensemble

A method for identifying a potential supercooled water zone through a satellite-borne multi-parameter ensemble, comprising the following steps:

• acquiring reference data on potential supercooled water zone identification from an active remote sensing satellite, collecting parameter data from a geostationary meteorological satellite, and pre-processing the reference data and the parameter data;
• grouping based on distribution features of the parameter data to obtain interval data, and performing feature selection of the parameter data based on the reference data and the interval data according to a cumulative frequency crossover method to obtain an influencing parameter;
• performing pixel-wise voting based on the influencing parameter to identify a potential supercooled water zone, and calculating a probability of supercooled water to obtain identification data;
• when a value of cloud mask (CLM) in a cloud product value is 0 and data on a cloud top temperature (CTT) is available, this indicates presence of a cloudy zone, otherwise it is identified as a cloud-free zone;
• for the influencing parameter, when the CTT is below 0° C. and a cloud top is identified to have a liquid phase according to a parameter threshold, this indicates that supercooled water exists at the cloud top or supercooled water exists in the cloud, and in this case, a voting value of 1 is assigned, otherwise a voting value of −1 is assigned;
• voting results are accumulated to obtain a voting value, and when the voting value is greater than 0, it is identified as a potential supercooled water zone, otherwise it is identified as a non-potential supercooled water zone;
• a probability of supercooled water is calculated based on the voting value:
• P
• gl
• =
• V
• +
• n
• ′
• 2
• ⁢
• n
• ′
• ×
• 100
• ⁢
• %
• in the formula, V is a voting value, and n′ is the number of influencing parameters;
• and
• the identification data comprise results of identifying the potential supercooled water zone and the probability of supercooled water;
• establishing a potential supercooled water zone identification model based on the identification data, and optimizing the potential supercooled water zone identification model using the reference data;
• and
• inputting data to be identified into the potential supercooled water zone identification model to obtain identification results.