6 results about "Weather system" patented technology

The application discloses a radar echo space-time extrapolation method based on asymmetric space modeling, belongs to the cross field of meteorological radar data processing and artificial intelligence technology, and first constructs an asymmetric input space sample containing a target region and a surrounding extended region by preprocessing original meteorological radar observation data, builds an asymmetric space modeling prediction network based on a 3D U-Net architecture, adopts a regional mask mean square error loss function to complete model training, and finally realizes accurate space-time extrapolation of the target region radar echo. The application solves the technical problems of information fragmentation and significant long-time prediction error accumulation of the traditional symmetric modeling method, improves the precision and stability of radar echo extrapolation under complex weather systems, and is suitable for 0-1 hour nowcasting and meteorological disaster accurate early warning scenes.

This invention discloses a simulation method and system for three-dimensional cloud precipitation detection based on a spaceborne conical scanning radar. The method simulates a three-dimensional cloud field and acquires water condensate information by inputting typical cloud precipitation scene data into a numerical model; it establishes a water condensate particle spectrum and microphysical model, and calculates its scattering and attenuation characteristics using a T-matrix; it simulates a wide-swath three-dimensional scanning trajectory based on satellite orbital parameters and conical scanning geometry; it matches the trajectory with the cloud field using the nearest neighbor method and calculates the radar reflectivity factor before and after attenuation; finally, it reconstructs the three-dimensional gridded radar echo data using a range-weighted method. This invention overcomes the shortcomings of existing simulation technologies in completely reconstructing wide-swath three-dimensional cloud and rain structure and wind field information, and can simulate three-dimensional radar observation scenarios of weather systems such as typhoons with high fidelity, providing an effective simulation verification platform for the system design and parameter optimization of next-generation spaceborne conical scanning radar.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for emulating the evolution of meteorological phenomena within a weather system. In one aspect, a system comprises receiving observation data characterizing an initial state of a weather system at a first time step, encoding the initial state as an observation representation, updating the observation representation for each of a sequence of time steps, the updating comprising: calculating one or more dynamical tendencies for the weather system using a numerical solver, processing an input comprising the observation representation using a physical tendency neural network to generate one or more physical tendencies for the weather system, combining the observation representation with the one or more dynamical and physical tendencies to update the observation, and decoding the observation representation to generate a predicted observation of a future weather state at the final time step of the sequence of time steps.

This invention relates to the field of power system automation technology, specifically to a cross-time period load collaborative forecasting method based on deep meteorological learning, comprising the following steps: S100: acquiring multidimensional meteorological data and historical load data of the target power grid area and surrounding meteorological stations; constructing a meteorological spatiotemporal topology map, and mapping the multidimensional meteorological data to the same time step as the historical load data through a cross-frequency feature alignment mechanism; S200: inputting the meteorological spatiotemporal topology map into a pre-constructed spatiotemporal feature extraction network to extract the dynamic evolution features of the meteorological system in terms of spatial distribution and time lag, and outputting a comprehensive meteorological spatiotemporal feature vector. This invention pioneers a cross-scale dual-branch network and a cross-attention gating mechanism, which perfectly simulates the physical modulation effect of "macro-climate trends" on "micro-local meteorological abrupt changes" at the feature decoupling level, significantly improving the model's prediction robustness under complex extreme weather superposition states.

This invention provides a modeling method for objective short-term heavy precipitation forecasting that integrates multi-scale meteorological features, belonging to the field of precipitation forecasting technology. The method includes the following steps: collecting data at several scales and preprocessing the collected data; constructing a progressive multi-scale feature pyramid network; designing a multi-scale spatiotemporal attention fusion module; constructing a task learning framework, including a precipitation probability head, a precipitation intensity head, and an optical flow head; constructing a dynamic weight loss function; model training and optimization; actual precipitation prediction; and finally, fine-tuning the model to complete its construction. The progressive multi-scale feature pyramid network can more effectively extract and fuse multi-scale features, capturing information at different scales from local convection to weather systems. The multi-scale spatiotemporal attention fusion module adaptively fuses multi-source data, fully utilizing the complementarity of radar, satellite, and numerical model data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for simulating evolution of meteorological phenomena within a weather system. In one aspect, a system includes receiving observation data characterizing an initial state of a weather system at a first time step; encoding the initial state into an observation representation; updating the observation representation for each time step in a sequence of time steps, the updating including computing one or more dynamical tendencies of the weather system using a numerical solver; processing an input including the observation representation using a physical tendency neural network to generate one or more physical tendencies of the weather system; combining the observation representation with the one or more dynamical tendencies and physical tendencies to update an observation; and decoding the observation representation to generate a predicted observation of a future weather state at a last time step in the sequence of time steps.