8 results about "Empirical orthogonal functions" patented technology

The application discloses a kind of air temperature change driving factor attribution method, device, medium and equipment.Therein, method includes: the historical data obtained is re-sampled according to preset grid scale, and pretreatment data is obtained, wherein pretreatment data includes pretreatment air temperature data and pretreatment driving factor data;Respectively on pretreatment air temperature data and pretreatment driving factor data are carried out empirical orthogonal function decomposition, and air temperature decomposition data and driving factor decomposition data are obtained;XGBoot regression fitting is carried out to air temperature sequence of air temperature decomposition data and driving factor sequence of driving factor decomposition data, and XGBoost model is obtained;Based on air temperature spatial pattern of air temperature decomposition data, XGBoost model and the SHAP explainability decomposition of each driving factor to air temperature change for sample data for explanation, and the attribution result of each driving factor affecting air temperature change is obtained.

This invention discloses a method for predicting the spatial distribution of algal blooms in lakes based on near-surface wind vectors and a Bi-GRU-Attention model. The invention employs Empirical Orthogonal Function (EOF) analysis to extract the dominant mode of algal bloom spatial distribution and time coefficients that represent the temporal variation characteristics of algal bloom spatial patterns. Then, a bidirectional gated cyclic unit model based on an attention mechanism is constructed to extract key dynamic features from the wind vector time series and identify wind periods that significantly contribute to the formation of algal bloom spatial distribution patterns. Finally, the time coefficients of the dominant mode of algal bloom spatial distribution are predicted using the wind vector time series state, and the spatial distribution of algal blooms is reconstructed by combining the corresponding spatial modes. This invention provides a new approach for the study of wind-driven algal bloom spatial distribution.

The application discloses a three-dimensional modeling and data assimilation method for ionospheric electron density, belongs to the field of space environment monitoring and information technology, and comprises the following steps: constructing a hybrid basis function system based on spherical harmonics and empirical orthogonal functions and establishing a state space model, independently perturbing through key geophysical driving parameters, combining a physical empirical model to construct a physical-statistical dynamic model, fusing multi-source heterogeneous observation data, obtaining an analysis state vector set through a set Kalman filtering algorithm and an asynchronous assimilation window, and reconstructing a three-dimensional electron density field and uncertainty products through the hybrid basis function system based on the analysis state vector set.

The application discloses a kind of black carbon emission inversion method, system, equipment and medium, it is related to carbon emission inventory inversion technical field, comprising: using empirical orthogonal function model to black carbon emission inventory is carried out spatial and temporal decomposition dimension reduction, obtain the multiple emission source regions of global scale indicating black carbon emission source;In each emission source region, consider the linear and nonlinear interaction between multiple influence factors, establish the nonlinear regression model between black carbon emission under the interaction of multiple influence factors;Satellite remote sensing observation true value and reanalysis meteorological field data of multiple influence factors are input nonlinear regression model to obtain the black carbon emission inventory in emission source region.The application can obtain accurate black carbon emission, and then obtain accurate black carbon emission inventory data.

A kind of multi-modal quantitative inversion method of paleoatmospheric circulation based on spatiotemporal deep learning network, using the powerful non-euclidean space feature extraction capability of spatiotemporal graph neural network (ST-GNN), captures the nonlinear spatiotemporal lag effect of vegetation response to climate; At the same time, coupling empirical orthogonal function decomposition (EOF) technology, orthogonal mode is extracted in potential feature space to capture the macroscopic atmospheric teleconnection pattern in geological record. The present application provides a quantitative inversion paradigm that can integrate micro-ecological mechanism and macro-dynamic mode, solves the problem of mixed signals in paleoclimate records of Qinghai-Tibet Plateau and surrounding transition zone, and realizes high-precision and high-resolution restoration of the evolution process of paleoatmospheric circulation intensity. In the multi-source stratigraphic record verification, not only can the mixed signals be effectively separated, but also the superior robustness and generalization ability are shown when dealing with geological age error and data loss.

This invention belongs to the technical field of seawater sound velocity profile inversion, and particularly relates to a method and apparatus for seawater sound velocity profile inversion based on the HMC algorithm. The method includes the following steps: interpolating and preprocessing historical sound velocity profile data and decomposing it using empirical orthogonal functions to obtain empirical orthogonal function expressions; calculating the time required for sound velocity propagation at different depths using the empirical orthogonal function expressions; using time as observation data, modeling the inversion algorithm based on the HMC sampling algorithm principle to obtain a result dataset; calculating the mean and standard deviation of the result dataset to obtain empirical orthogonal function values; and using the HMC algorithm to invert the empirical orthogonal function values ​​to reconstruct the seawater sound velocity profile, thus completing the inversion of the seawater sound velocity profile. This invention can accurately calculate the relationship between parameters using the constructed target distribution function and efficiently complete the inversion of seawater sound velocity profiles using the HMC sampling algorithm, providing a new method for seawater sound velocity profile inversion.

This invention discloses a method and system for enhancing the resolution of water vapor products using multimodal features and deep learning. The invention first acquires a first atmospheric precipitable water vapor fusion field generated using a fusion method, along with multimodal feature data related to atmospheric water vapor. Then, it uses an empirical orthogonal function method to extract common modes from both, obtaining common variation patterns. Finally, it inputs the common variation patterns into a pre-trained AutoResNet model to generate a second atmospheric precipitable water vapor product with a higher spatiotemporal resolution than the first fusion field. The AutoResNet model is a neural network model based on an autoencoder and a residual network, which has established a nonlinear mapping relationship between multimodal features and atmospheric precipitable water vapor. This invention effectively breaks through the resolution limit of the original water vapor data source, significantly improving the temporal and spatial resolution of the fusion product, and can be applied to severe weather monitoring, regional water cycle research, and high-resolution water vapor inversion operations.