6 results about "Radiosonde" patented technology

This invention relates to the field of AI weather forecasting technology and discloses a weather forecasting method based on an AI weather forecasting model and a WRF model. The method includes: inputting ERA5 global atmospheric reanalysis gridded data into the AI ​​weather forecasting model to obtain an assimilated background field; constructing an effective multi-source observation vector based on observation data from ground automatic weather stations, radiosonde observation data, geostationary meteorological satellite radiance data, and Doppler radar VAD wind profile data; solving for the optimal atmospheric analysis field; extracting atmospheric state variables from the optimal atmospheric analysis field and writing them into the WRF model's side boundary driving file, driving the WRF model to complete the integration operation, and obtaining a gridded forecast field of meteorological elements. This method enables the WRF boundary condition update frequency to be higher than the traditional 6-hour static scheme, effectively characterizing the rapid evolution of the atmospheric state in the boundary region at the sub-hourly scale, thereby improving the WRF model's forecasting capability for high-impact weather events such as severe convection.

This invention provides a method and apparatus for reconstructing the three-dimensional temperature and humidity field of a tropical cyclone based on deep learning and sparse observations. The method includes: acquiring radiosonde observation data and satellite remote sensing data for a target tropical cyclone region; mapping the radiosonde observation data onto a two-dimensional grid on multiple preset pressure layers to generate an initial temperature and humidity field containing missing data regions, and generating a mask to identify the missing data locations; interpolating and aligning the satellite remote sensing data onto the two-dimensional grid to form an auxiliary remote sensing field; combining the initial temperature and humidity field, the mask, and the auxiliary remote sensing field at each pressure layer to form a multi-channel input tensor, which is then input into a pre-trained deep neural network model. The model infers and completes the missing data regions, outputting the complete two-dimensional temperature and humidity field for the current layer; and vertically synthesizing the complete two-dimensional temperature and humidity fields corresponding to all pressure layers to obtain the three-dimensional temperature and humidity field of the target tropical cyclone.

This utility model discloses an automatic power-on device for a radiosonde, comprising the following structure: a bracket, a positioning pin, a power-on module, and a limit sensor. The bracket is a fixed structure, the positioning pin is mounted on the bracket, and the power-on module is located at the front end of the bracket, equipped with a cable and a controllable power supply connection. The limit sensor is located inside the bracket. This utility model is suitable for unattended operations in deserts, mountains, extremely cold, and extremely hot uninhabited areas. It utilizes a positioning pin, a power-on module, and a limit sensor, all integrated on a single bracket to form an automatic power-on device. The positioning pin ensures precise positioning and installation of the radiosonde carrier. The limit sensor detects whether the radiosonde carrier is properly installed. A control computer powers the automatic power-on device, thus automatically powering on the radiosonde. This utility model features a scientifically designed structure, convenient operation, strong practicality, and broad application prospects.

This invention relates to the field of meteorological monitoring technology, specifically to a sea front identification method based on machine learning, comprising the following steps: S1, acquiring meteorological station, radiosonde station, and radar echo data of the target area, extracting meteorological element gradients, abrupt change intensity, and land-sea difference, and extracting radar echo features to form multi-source feature data; S2, collecting sea front and non-sea front data to construct samples and dividing them into training, validation, and test sets; S3, jointly analyzing meteorological features to identify the land-sea interaction boundary and constructing a sea front probability field; S4, constructing a machine learning model to fuse multi-modal features and training and evaluating it; S5, inputting multi-source data in real time for sea front identification and outputting early warnings. This invention, by fusing multi-source meteorological data and combining it with a machine learning model, achieves accurate identification and real-time early warning of sea fronts, thereby improving the accuracy and automation level of sea front monitoring.

This invention relates to a method and system for individually calibrating channel coefficients used in a radiosonde. The method includes: S1, sending a command to the radiosonde; S2, if the radiosonde does not respond, returning to S1; if the radiosonde responds, proceeding to S3; S3, outputting a known standard resistance value to the radiosonde, calibrating the actual ADC sampling value according to the known standard resistance value, obtaining calibration result parameters, calculating a target resistance value based on the calibration result parameters, and returning to S1 to store the calibration result parameters; S4, establishing a resistance-ADC mapping model based on the calibrated actual ADC sampling value and the target resistance value, and obtaining the actual resistance value corresponding to the ADC value based on the resistance-ADC mapping model. This invention can effectively improve calibration accuracy.

This invention discloses a method for extracting oceanic low-altitude waveguide features based on meteorological gradients, relating to the fields of marine atmospheric environment detection and radio wave propagation technology. This method constructs a standardized multi-scale meteorological gradient dataset through data preprocessing, and then uses a gradient fusion algorithm to achieve global optimization of the physical constraint fusion factor of the mesoscale meteorological gradient and local modeling of small-scale non-uniform features, forming a high-resolution feature field. Based on this, combined with radiosonde data, a waveguide comprehensive inversion algorithm is used to invert and obtain a full-space waveguide feature parameter set. Finally, the coefficients are adaptively optimized through closed-loop verification using independent measured data. This invention achieves high-resolution integration of mesoscale global constraints and small-scale local features, improving the accuracy of oceanic low-altitude atmospheric environment characterization. Relying on the waveguide comprehensive inversion and closed-loop optimization mechanism, it integrates measured data and multi-dimensional verification, adaptively optimizing core coefficients, enhancing the reliability of waveguide feature extraction and its adaptability to complex ocean-atmosphere environments.