70 results about "Metocean" patented technology

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

The invention provides an HY-2A ocean water vapor inversion method based on machine learning, and the method comprises the steps: constructing a high-quality HY-2A scanning microwave radiometer data set through the data preprocessing, matching, feature extraction and normalization processing of HY-2A satellite scanning microwave radiometer data and ERA5 reanalysis data. A plurality of machine learning models are used for training and testing, Bayesian optimization is used for realizing hyper-parameter automatic adjustment, an optimal solution in finite time is obtained, an SHAP method is used for explaining the models, contribution of each characteristic variable is clear, interpretability of the used machine learning models is improved, and finally high-precision inversion of the water vapor over the sea is realized. The method has efficient calculation performance, can realize rapid processing of large-scale marine meteorological data, and improves the interpretability of a machine learning model. The method has important application value in numerical weather forecast, ocean extreme weather forecast, climate monitoring and other geophysical and meteorological fields.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

The invention discloses a dynamic feature and physical gating fused wave height prediction system and method, belongs to the technical field of artificial intelligence and machine learning, is used for marine environment monitoring, and comprises a data acquisition and preprocessing module, a feature screening module and a model prediction module. The method comprises the steps of obtaining and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient elevator regression model to screen a core variable set, constructing a physical guidance gating feature perception Transform prediction model, constructing a mixed loss function constraint prediction model, and outputting a sea wave significant wave height prediction value after prediction model training is completed. According to the method, through a feature screening and dynamic weight distribution mechanism, the utilization efficiency and scene adaptability of multi-source marine meteorological features are remarkably improved, the method can be flexibly expanded to sea wave prediction tasks of different sea areas and spatial-temporal scales, and high-precision and high-reliability technical support is provided for ocean engineering, shipping safety and disaster early warning.

The invention relates to the technical field of marine survey and big data processing, and provides a rolling sea wave forecast correction method and device, electronic equipment and a storage medium. Comprising the steps of training a sea wave rolling forecast correction model based on artificial intelligence and real-time observation; obtaining to-be-corrected numerical mode sea wave factor forecast data and real-time observation data; the numerical mode sea wave element forecast data comprises sea wave significant wave height data, and the real-time observation data comprises sea surface wind vectors and sea wave significant wave height real-time observation data; correcting the numerical mode sea wave element forecast data according to a sea wave rolling forecast correction model obtained through training, obtaining a group of new sea wave forecast data after new observation data are input, and obtaining numerical mode sea wave significant wave height data capable of being updated and corrected in a rolling manner; the method can effectively reduce the negative influence of the error on the subsequent application in the fields of marine meteorology and the like, and effectively improves the application effect of the sea wave forecast data.

The invention provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

The invention discloses a sea temperature prediction method and system based on long-time modeling and cross-branch fusion, and belongs to the technical field of marine meteorological intelligent prediction and deep learning modeling. The method comprises the following steps: carrying out spatial feature coding on a standardized sea temperature time sequence image sequence by using a deep convolution encoder to obtain multi-level spatial features; performing two-dimensional discrete wavelet decomposition on the multilevel spatial features to obtain low-frequency approximate representation, horizontal singular features, vertical singular features and diagonal edge features, and inputting the features into a wavelet attention module to obtain short-term time sequence enhancement features; inputting the multi-level spatial features and the low-frequency approximate representation into a ConvLSTM module to obtain high-frequency dynamic enhancement features; and inputting the short-term time sequence enhancement feature and the high-frequency dynamic enhancement feature into a cross-branch attention fusion module to obtain a fusion feature, and sequentially inputting the fusion feature into an Informer time sequence encoder and a decoder to obtain a prediction result.

The invention discloses a sea surface wind speed inversion method and system based on a CYGNSS and Willoughby model, and belongs to the technical field of ocean remote sensing. The method comprises the following steps: firstly, extracting a cyclone parameter from IBTrACS data through a Willoughby model, and encrypting the cyclone parameter to 1 second for sampling by utilizing segmented three times of Hermite interpolation; screening CYGNSS mirror reflection points and constructing a data set in combination with a space matching algorithm; processing CYGNSS L1-level data and extracting observation parameters; establishing a basic GMF (Gaussian Mixture Function) according to incidence angle grouping, and optimizing the weight through nonlinear fitting; the Willoughby model wind speed and the basic GMF are fused to generate an enhanced GMF, the DDM signal-to-noise ratio is dynamically adjusted in combination with minimum variance estimation, and the precision is improved. According to the method, the GNSS-R technology is utilized to overcome the defects of high cost, limited coverage and the like of a traditional method, the sea surface wind speed monitoring reliability under the extreme weather is enhanced through collaborative optimization of a physical model and actually measured data, and the method has the advantages of low cost, wide coverage and interference resistance and is suitable for refined marine meteorological monitoring.

This invention provides a method, apparatus, and device for generating marine meteorological data, relating to the field of data processing technology. The method for generating marine meteorological data includes: acquiring identification observation data and numerical simulation data of marine meteorological observation data; interpolating the numerical simulation data onto a preset target grid to generate interpolated wind field data; generating corrected wind field data based on the interpolated wind field data and the identification observation data; generating simulated ocean wave data based on the corrected wind field data; generating corrected ocean wave data based on the simulated ocean wave data and the identification observation data; and encoding the corrected wind field data and the corrected ocean wave data into target marine meteorological data. This invention achieves the fusion of autonomous observation data and numerical simulation data, and efficiently generates high-precision auxiliary meteorological data that meets the input requirements of marine satellites.

The invention belongs to the technical field of ship tracking and positioning, and discloses a ship dynamic tracking method and system based on Beidou positioning, and the method comprises the steps: employing a ship-borne Beidou satellite receiver to determine the position in real time, judging whether to trigger a ship dangerous case alarm, marking a ship in distress, and transmitting the dangerous case alarm information to a Beidou comprehensive application service platform; positioning a ship in distress on an electronic chart, marking a dangerous case range, querying surrounding ships or a global marine meteorological model to generate a vector marine field of the dangerous case range, predicting a drifting high-probability area of the ship in distress, searching a prepared rescue ship for the ship in distress, generating a minimum-cost path, and calculating predicted arrival time; the method comprises the following steps: acquiring the position and state of a ship in real time, generating a pre-rescue ship list, sending a rescue request to the pre-rescue ship, marking an actual rescue ship according to a reply, and carrying out parallel obstruction detection and obstruction coordination on the actual rescue ship. And full-process tracking rescue from dangerous case alarm to rescue execution is realized.

The present invention belongs to the technical field of marine meteorology and relates to a method for forecasting low-altitude atmospheric ducts at sea based on artificial intelligence ensemble error correction. This method establishes a set of forecast error correction algorithms and dynamically adjusts the weights of the ensemble members to achieve ensemble error correction of the forecast results, overcoming the high spatiotemporal uncertainty and unstable correction accuracy of a single correction algorithm. The method of the present invention incorporates numerical forecasting methods using empirical statistical post-processing techniques. Compared with purely data-driven low-altitude atmospheric duct forecasting methods, its results are more reasonable and stable, and its effective forecast time is longer.

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.

A method of designing a floatable foundation 100 for a wind turbine generator, the method comprising generating a three-dimensional (3D) computer model of the foundation 100; providing a set of metocean data representative of environmental conditions at a proposed operational site for the foundation 100; simulating an operation of the foundation 100 at the proposed operational site; and determining a parameter indicative of stress and / or fatigue loads on the floatable foundation 100 under the environmental conditions at the proposed operational site. There is also provided a floatable foundation 100 for a wind turbine generator, the floatable foundation 100 comprising three outer column members 20,21,22 disposed about a tubular central column 10, three horizontally extending pontoon members 30,31,32, three horizontally extending beam members 40,41,42, and a plurality of reinforcement members (50, Fig.26) each extending between at least two pontoon members 30,31,32.

The invention relates to the technical field of deep learning and ocean engineering meteorology, and particularly provides an industrial ocean meteorological disaster forecasting method based on deep learning. The method comprises the following steps: acquiring real-time meteorological data, and constructing a dynamic text prompt set; performing semantic-physical feature joint modeling on the dynamic text prompt set by using a multi-modal fusion expert module to obtain a real disaster sample; constructing a dream learning framework, entering a dream stage, and obtaining a virtual disaster dream sample; in an awakening stage, high-dimensional potential representation is obtained through a real disaster sample and a virtual disaster dream sample; according to the high-dimensional potential representation, a forecast result is output, a full-closed-loop industrial meteorological disaster prediction system is formed, and the method improves the accuracy, robustness and interpretability of meteorological disaster prediction in a marine industrial scene.

The invention discloses a numerical weather forecast cold start error suppression method and device, and relates to the technical field of offshore wind power, and the suppression method comprises the steps: S1, collecting initial data through a marine meteorological sensor array, including sea wave height, period, wave direction and corresponding wind field initial observation data, and constructing a multi-dimensional data set after preprocessing; s2, on the basis of the data set and historical sea wave-wind field time series data, a physical mechanism and data driving combination method is adopted to construct a sea wave-wind field coupling model, and the nonlinear coupling relation between sea waves and the wind speed and the wind direction is quantified. According to the method, a physical mechanism and data driving fusion modeling is adopted, so that the meteorological rule conformity is guaranteed, the nonlinear coupling relationship is accurately captured, the limitation of a pure physical and pure data driving model is avoided, and the prediction core support is tamped; based on neural network mining time sequence and mode characteristics, a lattice point relaxation algorithm parameter strategy is dynamically adjusted, and the problem that traditional fixed parameters are poor in adaptability is solved.

This application provides a method, device, and medium for assisting decision-making in the search of distressed targets at sea, relating to the field of maritime technology. The method includes: acquiring marine meteorological and environmental forecast information for the distressed area and surrounding waters to determine the search area; calculating the sea area risk level and the urgency of the search operation in the search area; acquiring search resources; establishing a search resource decision-making model, combining the urgency of the search operation, the sea area risk level, and the search resources to obtain the optimal search resource planning scheme; dividing the search area into sub-regions according to the sea area risk level, and combining the optimal search resource planning scheme to obtain the working sea area of ​​the search forces and determine the optimal global path planning for the search forces. This scheme achieves a balance between search speed, search cost, and search safety in search operations, improving the efficiency of maritime search operations.

This invention discloses a wave height prediction system and method integrating dynamic features and physical gating, belonging to the field of artificial intelligence and machine learning technology, for marine environmental monitoring. The system includes a data acquisition and preprocessing module, a feature selection module, and a model prediction module. The method includes acquiring and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient booster regression model to select the core variable set, constructing a physically guided gating feature-aware Transformer prediction model, constructing a hybrid loss function-constrained prediction model, and outputting significant wave height prediction values ​​after the prediction model is trained. This invention significantly improves the utilization efficiency and scenario adaptability of multi-source marine meteorological features through feature selection and dynamic weight allocation mechanisms. It can be flexibly extended to wave prediction tasks at different sea areas and spatiotemporal scales, providing high-precision and highly reliable technical support for marine engineering, shipping safety, and disaster early warning.

The application discloses a kind of long time dependence modeling and cross-branch fusion sea temperature prediction method and system in the technical field of marine weather intelligent prediction and deep learning modeling.The method comprises the following steps: using deep convolutional encoder to encode the spatial features of standardized sea temperature time series image sequence, obtaining multi-level spatial features; the multi-level spatial features are two-dimensionally discrete wavelet decomposed, to obtain low-frequency approximation representation, horizontal singular feature, vertical singular feature and diagonal edge feature, then input into wavelet attention module, to obtain short-term time series enhanced features;Multi-level spatial features and low-frequency approximation representation are input into ConvLSTM module, to obtain high-frequency dynamic enhancement features;Short-term time series enhanced features and high-frequency dynamic enhancement features are input into cross-branch attention fusion module, to obtain fusion features, then input into Informer time series encoder and decoder in turn, to obtain prediction results.

The application relates to the technical field of marine meteorological data assimilation, in particular to a marine meteorological data assimilation method based on self-adaptive scale decomposition. An original data set is acquired and preprocessed to obtain a two-dimensional matrix. Based on an empirical orthogonal function analysis method EOF, dimension reduction is carried out on the initial matrix to obtain a spatial mode which does not change with time. A spatial self-adaptive scale reduction model is constructed by using the correlation of points on the spatial mode. Grid setting is carried out on the spatial self-adaptive scale reduction model to obtain an optimized multiple network. A target functional is determined and scale-by-scale assimilation is carried out to obtain an analysis field. An accuracy calculation formula and an accuracy threshold value are determined. Sample observation values and sample analysis values of a sample to be analyzed are acquired, and the accuracy is calculated. The two-dimensional matrix is extracted and processed by the EOF, the spatial self-adaptive scale reduction model is obtained, and scale-by-scale assimilation of adaptive matching of spatial characteristics is realized.

The invention belongs to the technical field of downscaling, and relates to an effective sea wave height downscaling method based on a generative adversarial network, which comprises the following steps: acquiring input data, and preprocessing the input data, the input data comprising sea wave height data and wind speed data; introducing an attention coordinating mechanism, and capturing characteristics of wind speed data in longitude and latitude directions; a multi-resolution feature fusion generator is constructed, and sea wave height data is up-sampled to a high-resolution grid; a spectral normalization Markov discriminator SN-PatchGAN is adopted, and local features of the image are captured; constructing a loss function according to the training target of the discriminator; and carrying out training and parameter updating on the model by utilizing an optimizer, and carrying out iterative training on the model in combination with an early stop strategy to generate high-resolution sea wave height data. According to the method, more real and higher-quality high-resolution sea wave height data can be generated, and the method is suitable for the fields of marine weather forecast, climate research and the like.

The disclosure relates to a computer-implemented method for optimizing a layout of a subsea oil & gas field on a portion of the seabed, comprising providing input specifying locations unsuitable for well location and injection or production targets, determining optimal locations each of an injection well or of a production well by optimizing a well location and trajectory, determining optimal locations by performing an optimization that rewards every well is connected to exactly one drill center and the overall drilled length and number of drill centers is minimized, determining a location and orientation for the surface facility or processing facility based on metocean data, marine regulation constraints and the determined locations each of a drill center. The method also comprises determining a layout of flowlines by performing an optimization process including iterations of computing a solution to the optimization and defining mechanical constraints on the flowlines to find another solution.

The invention discloses water bottom sampling equipment for marine meteorology service, relates to the field of water bottom sampling for marine meteorology service, and solves the problems that the existing water bottom sampling equipment for marine meteorology service is difficult to continuously sample during use and different samples are easy to interfere with each other in the sampling process. Comprising a device box, a sampling mechanism, a first fixing ring and a second fixing ring, and the sampling mechanism comprises a rotating disc, a sampling cylinder, a suction disc, a sampling piece, a control piece and a cleaning piece. The negative pressure is generated in the sampling cylinder, the cleaning piece is used for cleaning the lower structure after the sampling cylinder reaches the position needing sampling, the sampling cylinder is controlled to rotate to the bottom position, lower underwater substances are pumped into the sampling cylinder in the negative pressure state to be collected, the continuous sampling function is achieved by controlling the position of the sampling cylinder, and multiple sets of samples do not interfere with one another.

The invention belongs to the technical field of marine weather forecast, and particularly relates to an atmosphere structure stability correction method, system and device and a medium, and the method comprises the steps: analyzing the incidence relation between the atmosphere structure stability and the wind wave growth rate, and determining a core evaluation parameter; collecting a plurality of marine meteorological observation data, and preprocessing the plurality of marine meteorological observation data to construct a standardized training data set; constructing a wind wave growth rate initial correction model based on physical constraints; integrating the initial correction model into a wind energy input source item of a sea wave numerical mode so as to correct the stability of the atmosphere structure in real time and obtain correction stability; verifying the correction effect through long-term numerical simulation, and performing model performance verification on the initial correction model by adopting multi-index statistical evaluation to obtain a model verification result; and performing parameter sensitivity analysis and optimization on the initial correction model based on a model verification result to obtain an optimized correction model. According to the invention, the accuracy of wave numerical forecasting is improved.

This invention relates to the field of marine meteorological observation equipment technology, specifically a shipborne high-precision rainfall measurement device and method based on video image and attitude synchronization. The device includes a rain collection head, a main body, a multi-tube array rain gauge, an optical observation module, a main control module, and a temperature and humidity sensor. The main body, rain collection head, and bottom plate form a Faraday cage. The rain gauge is equipped with a damping and liquid-sealing structure to suppress liquid surface oscillations, and a variable diameter design amplifies liquid level changes. The measurement method synchronizes ship data with the attitude sensor, triggers image acquisition at the alignment moment, extracts the water level line using an algorithm, and calculates the rainfall using a variable diameter formula. The temperature and humidity sensor is linked to achieve temperature compensation and snow melting. This invention integrates attitude calibration and video image technology to form a closed-loop measurement process, possesses electromagnetic shielding and full-scene adaptability, and can effectively improve the accuracy and stability of rainfall measurement in shipborne environments. It is suitable for various ship observation scenarios in nearshore and ocean-going environments.

The utility model provides a marine meteorological monitor, which relates to the technical field of marine monitoring and comprises a monitoring body, a positioning sleeve is fixed on the top surface of the monitoring body, and a group of photovoltaic panels fixed on the top surface of the monitoring body are arranged outside the positioning sleeve. And a cleaning assembly for cleaning the surface of the photovoltaic panel is arranged above the monitoring body. According to the marine meteorological monitor, transmission wings are blown by sea wind, so that the transmission wings drive a moving frame to move on the surface of a positioning sleeve through a rotating rod and drive a cleaning rod to clean the surface of a photovoltaic panel, sea birds can be repelled, the sea birds or other sundries are prevented from staying on the surface of the monitoring device, and the use efficiency of the photovoltaic panel is improved; the transmission plate is driven by the transmission rod to rotate around the rotating shaft, the transmission wing is driven by the rotating rod to rotate, the use angle of the transmission wing can be adjusted according to the sea surface wind speed or wind direction, the rotating speed of the transmission wing is kept within a certain range, and the service life of the detector is prolonged.

The invention is suitable for the field of marine meteorology, and provides an error correction method, device and equipment for El Nino prediction, and the method comprises the steps: obtaining sea temperature field post-report data in a target time period; extracting surface layer sea temperature post-report data from the sea temperature field post-report data, and performing sample averaging operation on the surface layer sea temperature post-report data to obtain surface layer sea temperature feature data; acquiring surface layer sea temperature label data in a target time period; training a random forest model according to the surface layer sea temperature feature data in the target time period, the surface layer sea temperature label data in the target time period and a preset model training algorithm to obtain a trained random forest error correction model; and receiving an El Nino real-time prediction result, and inputting the El Nino real-time prediction result into the random forest error correction model to obtain an El Nino final prediction result. The method can adapt to different climate conditions and marine environments, and systematic deviation in real-time prediction can be corrected.

The invention provides a rain and fog simulation cabin for acquiring rain and fog reference data and an acquisition method thereof. The rain and fog simulation cabin comprises a closed cabin body, a water supply system, a rain and fog system, a controllable light source system, a target trolley, a six-degree-of-freedom mechanical arm camera platform deck, a data acquisition system, a controller and an upper computer. According to the invention, rainfall, fog making, illumination control, ship motion simulation and rainwater closed-loop recovery are integrated, and a complex marine meteorological scene can be reproduced truly and repeatedly in a controlled indoor environment. Motion data of a real unmanned ship under different sea conditions are input into a mechanical arm, and simulation shooting of a camera under heaving, pitching and rolling is achieved; meanwhile, key parameters such as the rainfall range, the number and arrangement of nozzles and the color temperature range of a light source are scientifically demonstrated in the design of the simulation cabin, and it is ensured that experimental conditions are consistent with statistical characteristics of a real natural environment; a whole set of scientific sampling process is provided, and multi-source synchronization of the camera, the mechanical arm, the light source and the sensor is achieved.