11 results about "Meteorological models" patented technology

A method, device, equipment and medium for independent modeling of troposphere anisotropy of dry and wet convection layers, the method comprising: constructing a troposphere anisotropy model, which adopts a tilted plane horizontal gradient model to respectively describe dry delay anisotropy and wet delay anisotropy; deriving oblique dry delay through a numerical meteorological model, calculating a dry horizontal gradient parameter through the oblique dry delay, and correcting the dry delay anisotropy by using the dry horizontal gradient parameter; taking a wet horizontal gradient parameter as an unknown parameter to be estimated into an observation equation and performing dynamic estimation, and correcting the wet delay anisotropy by using the estimated wet horizontal gradient parameter. The present application adopts an asymmetric processing strategy of dry delay anisotropy model correction and wet delay anisotropy parameter estimation, which not only avoids the ill-conditioned problem of observation equation caused by simultaneous estimation of dry and wet gradients, but also utilizes the high accuracy of the numerical meteorological model in describing the dry delay anisotropy, so that the positioning stability and accuracy are improved at the same time.

This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and discloses a mesoscale-microscale atmospheric coupling simulation method and related apparatus. The mesoscale-microscale atmospheric coupling simulation method includes: acquiring initial field, lateral boundary conditions, and terrain data; based on the initial field, lateral boundary conditions, and terrain data, performing time integration of the microscale meteorological model within a preset time period to obtain the microscale simulation results for all main time steps; and performing spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results. The technical solution disclosed in this invention solves the problem of computational collapse in existing atmospheric coupling simulations due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

This invention relates to a method for correcting temperature forecasts using a large meteorological model based on short-term real-time information calibration. The method includes: S1, data collection; S2, data preprocessing and sample set creation; S3, temperature correction forecast model construction; S4, sample set partitioning and model training and testing; and S5, outputting the temperature forecast result after short-term real-time calibration. This invention, by fully utilizing the prior information of the large meteorological model forecast, introduces residual information between short-term real-time data and the forecast as a constraint, revealing the spatiotemporal correlation structure of the modeling error and its evolution with forecast lead time. Furthermore, it enables the correction process to adaptively adjust the calibration strategy according to the error state, thereby improving the accuracy, stability, and consistency of temperature forecasts and enhancing the operational application level of large meteorological model temperature forecast calibration technology.

The present application relates to the technical field of weather model optimization and polar ocean data processing, and particularly relates to a method and system for optimizing COARE model based on sea ice density, comprising: obtaining polar ocean historical measured data, calculating real roughness and model roughness output by COARE model and determining roughness difference value; dividing wind speed interval, fitting and establishing correction function of sea ice density and roughness difference value; introducing sea ice density to COARE model, determining corresponding interval according to wind speed and calling corresponding function to correct model roughness, and outputting ocean-atmosphere flux meeting polar sea ice condition to complete model optimization. The present application realizes dynamic correction of roughness of COARE model under polar sea ice covering condition by introducing sea ice density and establishing wind speed partition correction relationship, breaks through the limitation of original model in wind speed applicability and polar adaptability, and significantly improves the accuracy and practicability of polar ocean-atmosphere flux calculation.

The present application relates to a kind of sea wind advection benefit evaluation method and system, wherein sea wind advection benefit evaluation method includes the following steps: S1. Construct high-precision gridded multi-parameter coupled urban meteorological model, output includes air temperature, humidity, wind speed and high-frequency meteorological parameters of surface total solar radiation;S2. Based on high-frequency meteorological parameters, reconstruct the high-resolution spatial and temporal field of outdoor heat stress index;S3. Based on the heat balance mechanism of urban three-dimensional space, total heat and water vapor budget are decoupled into three physical driving processes of advection transport, turbulent diffusion and artificial heat emission, extract the net advection sensible heat flux and net advection latent heat flux of sea wind advection;S4. Based on numerical differentiation method, nonlinear extraction of outdoor heat stress index on sensible heat and latent heat sensitivity weight coefficient;The present application solves the problem that traditional evaluation result and health net benefit are disconnected from two dimensions of bottom flux decoupling and human sensitivity weighting, realizes the accurate quantification of sea wind advection health net benefit.

This invention relates to a coupling method for mesoscale and microscale wind field forecasting models, comprising the following steps: S1, in a mesoscale numerical weather prediction model, outputting the coordinate information of grid points with a horizontal spatial resolution covering the area to be finely predicted at 3km; S2, in a microscale meteorological model, acquiring the three-dimensional coordinate information of all grid points on all boundaries of the computational domain in the horizontal direction; S3, acquiring multi-layer wind speed forecast data at a horizontal spatial resolution of 3km in the mesoscale numerical weather prediction model in real time and calculating the average wind direction; S4, selecting all horizontal boundaries S in the microscale meteorological model with an angle less than or equal to 90 degrees from the average wind direction; S5, using a three-dimensional linear interpolation method to interpolate the multi-layer wind speed forecast data in the mesoscale numerical weather prediction model to obtain the wind speed values ​​of all grid points on the boundary S of the microscale meteorological model; S6, in the microscale meteorological model, assigning values ​​to the grid points on the boundary S of the computational domain to obtain the weather background information in the mesoscale model; S7, starting the microscale meteorological model to simulate and analyze the area to be simulated to obtain wind field information with higher resolution.

This invention relates to the field of power system operation and planning technology, and proposes a scenario generation method and system based on retrieval-enhanced probabilistic downscaling. It constructs wind power output conversion models and photovoltaic power output conversion models that consider the coupling of multiple meteorological variables, utilizes the TCVAE model, and performs probabilistic sampling of meteorological residuals. A multi-source condition vector is constructed, consisting of daily-scale meteorological statistics, hourly-level meteorological prior profiles of similar days, and meteorological states at the end of the previous day, serving as macroscopic constraints for intraday temporal evolution. During the extrapolation phase, the generated residuals are superimposed with the prior profiles of similar days to reconstruct the meteorological scenario, and a wedge correction algorithm is used to eliminate numerical jumps at the inter-day junctions. This invention effectively improves the physical consistency and temporal continuity of downscaled scenarios. By performing hourly-level temporal downscaling on coarse-time-resolution meteorological model data, it outputs a high-fidelity meteorological-wind-solar scenario set that meets the requirements of long-cycle simulation of new power systems.

A dynamic prediction method for strong wind disaster risks of power grid facilities in the Loess Plateau region is proposed, comprising: acquiring geographical and power grid information of the target area; inputting this information into a pre-constructed dynamic prediction model to obtain high-resolution dynamic wind field and turbulence parameter forecast products; dynamically calculating load effects and structural resistance using the high-resolution dynamic wind field and turbulence parameter forecast products combined with transmission line foundation data and environmental data; calculating the safety margin of each failure mode based on the load effects and structural resistance; and determining the risk level of the target area based on the safety margin. The dynamic prediction model is constructed by coupling local thermal disturbances with a computational fluid dynamics model or a physically constrained machine learning downscaling model. This method addresses the challenge of implementing macro-meteorological models by considering the complex mountainous geography and climate characteristics of the Loess Plateau and the wind-induced disaster mechanism of power grid facilities. By coupling micro-topographic dynamic effects with local thermodynamic processes, the simulation bias of complex mountain wind fields is reduced.

The application provides a field adaptation weather forecast method based on multi-intelligent large model fusion, generates a forecast result by using multiple AI weather models based on the same GFS initial field, solves an optimal fusion weight by constructing a fusion model and an optimization objective function containing a data fitting term and a physical constraint term, adopts a differentiated fusion strategy according to different forecast time periods, dynamically adjusts the weight in combination with historical forecast performance and real-time observation data, and outputs a forecast product after quality control. The application has the beneficial effects that the advantages of each model are fully utilized, the forecast precision is improved, the field adaptability and physical consistency are enhanced, the forecast stability is improved, full-time coverage is realized, the calculation efficiency is high, and the business demand is met.

This invention discloses a method for spatiotemporal redistribution of precipitation based on coordinated meteorological and hydrological regulation, comprising: multi-source radar data assimilation and short-term precipitation forecasting; CFD wind field dynamic simulation and cloud tracking; construction of a computational fluid dynamics non-hydrostatic equilibrium equation set based on the background wind field output by a mesoscale meteorological model; dynamic coupling of runoff generation and confluence and water demand regulation modeling; artificial rain enhancement catalytic decision-making and dynamic feedback; and evaluation and iterative optimization of the spatiotemporal redistribution effect. Through multi-source data fusion and a dynamic feedback mechanism, this invention achieves high-precision prediction of precipitation processes, proactive suppression of flood risk, and optimization of resource utilization efficiency.