7 results about "Wind profiler" patented technology

This invention provides a method for inverting horizontal divergence profiles based on a network of wind profiler radars. The method includes: employing simulation analysis to evaluate the sensitivity of the horizontal divergence inversion results of each wind profiler radar to its horizontal wind, as well as the rules for determining the distance to the wind profiler radars and the rules for determining the maximum interior angle and area of ​​the triangle; based on the determined results, selecting three target wind profiler radars, and obtaining the horizontal divergence at a certain vertical height layer within the triangle formed by the three target wind profiler radars using near-real-time wind vertical profile product data files from the mesoscale network of wind profiler radars. This invention provides a method for inverting horizontal divergence profiles based on a network of wind profiler radars, which is an optimal method for inverting horizontal divergence vertical profiles suitable for monitoring and warning of signals in the early stages of convection. It can reduce horizontal divergence inversion errors, improve the precision and accuracy of strong convection monitoring and short-term warnings, and thus provide support for optimizing the layout of the mesoscale network of wind profiler radars.

This invention relates to an intelligent processing method for wind profiler data, which solves the problems of existing wind profiler data processing methods, such as reliance on manual intervention for identifying missing height layers and the lack of structural consistency guarantees in interpolation methods. This method achieves automatic structural analysis and intelligent completion of missing height layers in wind profiler data through multi-step collaborative processes. The steps sequentially execute data access and format standardization, height layer structure parsing and index construction, intelligent identification of missing height layers, interpolation condition judgment, adaptive interpolation calculation, data structure reconstruction, and database storage. It automatically identifies missing height layers and adaptively selects interpolation algorithms to generate interpolation results based on data change trends, ensuring the integrity and continuity of the data structure and providing a reliable data foundation for wind field analysis models. This invention can be widely applied to wind profiler radar data platforms, meteorological data operational processing systems, and other scenarios.

This invention discloses a method for inverting atmospheric precipitation particle spectra, belonging to the field of inversion technology. It addresses the limitations of existing technologies in accurately obtaining inversion parameters for ground and atmospheric microphysical parameters, thus hindering understanding of the formation, development, and variation mechanisms of atmospheric cloud precipitation and consequently preventing accurate prediction and interpretation of atmospheric processes and meteorological disasters. The method includes: acquiring the observed power spectrum using C-band frequency-modulated continuous wave radar and employing denoising methods to remove noise levels; constructing the atmospheric power spectrum based on horizontal wind data acquired from wind profiler radar; constructing the raindrop spectrum using ground raindrop data acquired from a ground raindrop spectrometer and a standardized gamma distribution; and performing ground and atmospheric microphysical parameter inversion based on the above results. This invention is used for inverting and obtaining ground and atmospheric microphysical parameters.

This invention provides a deep learning-based method and system for boundary layer height inversion from wind profiler radar. The method includes: first, proposing a sensitivity analysis theory for boundary layer height inversion to analyze the impact of the number of local peaks on boundary layer inversion, laying a theoretical foundation for algorithm improvement; then, selecting numerous atmospheric parameter profiles from wind profiler radar products based on boundary layer physical characteristics and their importance, filtering out the input parameter profiles for the inversion model; inputting the filtered profiles and the actual boundary layer height obtained from sounding inversion into a convolutional neural network model for training; and finally, the fully trained model can directly output high-precision boundary layer height inversion results. This invention combines the advantages of deep learning in discovering complex structures in high-dimensional data with the inclusion of multiple atmospheric parameter profiles, significantly improving the accuracy and stability of boundary layer height inversion, providing strong support for boundary layer research and global climate change studies.

This application provides a method and apparatus for inverting typhoon boundary layer wind profiles based on multi-source data fusion. The method according to this application includes: based on the measurement height H of a ground-based automatic measuring station... start The starting height H of the wind-measuring lidar low Low-altitude wind profiles are obtained by combining wind-measuring lidar with ground-based automatic measurement stations; the maximum detectable height H of the wind-measuring lidar is based on... high With preset height H end The comparison results were obtained by measuring H using a combination of wind profiler radar and wind lidar. low To H end The wind speed within the specified range is used to obtain the upper-level wind profile; the lower-level and upper-level wind profiles are then combined to obtain a wind profile covering the entire boundary layer. The technical solution provided in this application can achieve wind profile inversion and wind field distribution characteristic analysis at the entire boundary layer height during different evolution stages of a typhoon.

This application relates to the field of meteorological monitoring equipment technology, and in particular to a low-altitude wind shear detection device based on wind profiler radar, which includes a base, a rotating detection mechanism, a height adjustment mechanism, and a data recording mechanism. The base has adjustable horizontal support legs to ensure stable installation. The wind cup assembly in the rotating detection mechanism senses wind force and converts it into rotation. The height adjustment mechanism uses components such as an inflatable airbag and a return spring to flexibly adjust the height of the detection mechanism. A transmission conversion component converts the rotation of the wind cup assembly into the up-and-down movement of a recording pen, which, in conjunction with recording paper wound around the outside of the active and driven rollers, completes the recording of wind shear data. This device has a reasonable structural design, effectively solving the problems of complex structure and poor environmental adaptability of existing equipment. It can achieve accurate detection and convenient data recording of low-altitude wind shear, providing reliable data support for fields such as aviation.

This application provides a networking method for wind profiler radar and wind-measuring lidar, as well as a dynamic parameter fusion method, belonging to the field of digital signal processing. The networking method of this application analyzes and evaluates the influence characteristics of the geometric parameters of a triangle on the retrieved key dynamic parameters, the influence characteristics of wind field element observation errors on the retrieved key dynamic parameters, and the influence characteristics of different weather and geographical conditions on the retrieved key dynamic parameters. It employs an area-weighted averaging method to form an optimal triangle networking scheme and calculates the key dynamic parameters, fully utilizing the advantages of both radars to improve the accuracy of retrieved atmospheric dynamic parameters. The dynamic parameter fusion method, based on the retrieved key dynamic parameters, further fuses reanalysis data or numerical model initial fields to obtain multi-source fused data, thereby further improving the accuracy of the data.