Weather forecast data compression method based on video compression technology

Abstract

The invention discloses a weather forecast data compression method based on a video compression technology, and belongs to the technical field of data compression. The method comprises the following steps: S1, acquiring original weather forecast data, and extracting time slices and weather element data; s2, preprocessing the weather forecast data: mapping the same meteorological element data of each station at the same time to an interval [0-1023 * n], wherein n is the number of mapped channels; s3, reconstructing three-dimensional grid points of the meteorological data: storing the meteorological forecast data according to three-dimensional grid point data, and forming a meteorological data graph corresponding to three color channel data formats of the picture; s4, using a video compression tool to carry out lossless compression coding of 10bit depth and chrominance-brightness ratio of 4: 4: 4 on the weather forecast data; and S5, carrying out meteorological data decoding on the coding file generated in the step S4 by using a video compression tool, and carrying out meteorological prediction data inverse transformation. According to the method, the storage space of weather forecast data is reduced, and the final compression ratio can reach 10: 1.

Description

technical field [0001] The invention belongs to the technical field of data compression, in particular to a method for compressing weather forecast data based on video compression technology. Background technique [0002] Meteorological forecast data has the characteristics of large data volume, various types of meteorological elements, and high timeliness. Because a large amount of data in weather forecast data is spatiotemporal data, which records the observations of various physical quantities within time and space, these massive meteorological data consume a lot of storage resources and network transmission resources of people. Therefore, choosing a good weather forecast data compression algorithm can greatly reduce the disk space occupation, and at the same time, the network load will be smaller, improving user experience satisfaction. Weather forecast data includes 6 meteorological elements: [0003] storage channel Meteorological elements unit PRE ...

AI Technical Summary

Problems solved by technology

Lossless compression can retain all the information in the data, but the compression ratio is generally not high, generally 2:1 to 5:1; for a large amount of weather forecast data, it is necessary to have a large enough compression ratio, but data loss is critical to the accuracy of data prediction and quality will have immeasurable impact

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