47 results about "Climate model" patented technology

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention discloses a U-Net-based multi-mode wind energy resource monthly scale prediction correction method and system, belongs to the technical field of wind energy resource evaluation and climate numerical value prediction crossing, and is used for correcting monthly scale wind speed output by a climate mode. According to the method, a climate state wind speed field is constructed by utilizing ERA5 reanalysis, and a monthly-scale 10m wind speed anomaly is calculated to serve as a correction reference; monthly-scale historical return data of a plurality of dynamic climate modes are obtained, 10m wind speed and multilayer meteorological elements are extracted, unified interpolation and standardization are carried out, and a sample set is formed; a U-Net correction model with a coding and decoding structure is constructed based on a sample set, feature combination and hyper-parameters are optimized through cross validation, and nonlinear mapping from a multi-mode forecast field to an ERA5 distance flat field is learned. And correcting future monthly scale forecast by using the optimal model, generating a wind speed product of which space structure and amplitude distribution are closer to observation, and providing high-credibility wind energy climate information for wind power planning, power generation planning and power grid dispatching.

Embodiments relate to computer systems and methods for computer models and scenario generation. The system involves generating integrated climate risk data using a Climate Risk Classification Standard hierarchy that maps climate data and multiple risk factors to geographic space and time. A computer model involves risk factors modeled as graphs of nodes, each node corresponding to a risk factor and connected by edges or links. The nodes of the graph create scenario paths for the model. The system automatically generates multifactor scenario sets using the scenario paths for the climate model to compute the likelihood of different scenario paths for the computer model. The scenario sets include transition scenarios.

The invention discloses a regional extreme drought and flood event prediction system based on a climate model, and relates to the technical field of climate model prediction and disaster early warning, and the system comprises a data input module which is used for receiving regional multivariable prediction data processed by downscaling of the climate model, and the multivariable prediction data comprises rainfall, soil humidity and evapotranspiration. According to the regional extreme drought and flood event prediction system based on the climate model, equation strong constraint and variational optimization are carried out on key variables such as rainfall, soil humidity and evapotranspiration through the physical constraint dynamic coupler, the physical law of water circulation mass conservation and surface energy balance is forcibly met, the distortion phenomenon is eliminated, and the prediction accuracy is improved. According to the method, physical correction residual errors are converted into weight factors, Copula function parameters are dynamically adjusted, it is ensured that joint probability distribution of extreme drought and flood events strictly follows a physical mechanism, the path of misinformation physical impossible events is blocked from the source, and the problem of prediction distortion caused by multivariate physical inconsistency in downscaling output of a climate model is solved.

The invention discloses a method for inverting a cloud condensation nucleus number concentration profile based on a laser radar and a neural network. The method comprises the following steps: collecting multi-wavelength laser radar data including a backscattering coefficient vertical profile, an extinction coefficient vertical profile, a laser radar ratio and a depolarization ratio; the method comprises the following steps: acquiring an atmospheric temperature profile and a water vapor mixing ratio profile which are in time-space synchronization with multi-wavelength laser radar data, performing grid alignment on a vertical height, and constructing a multi-channel input feature set for each preset target supersaturation ratio; and constructing a convolutional neural network profile inversion model, taking the multi-channel input feature set as input and the cloud condensation nucleus number concentration profile data as output, training the model, and obtaining the trained convolutional neural network profile inversion model. According to the method, the limitation of continuous, high-precision and high-temporal-spatial-resolution detection of CCN number concentration vertical distribution is overcome, and key data support is provided for the fields of weather forecast, climate models, artificial influence weather and the like.

The application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

The application belongs to the technical field of data analysis and processing, and specifically discloses a typhoon track trend change confirmation method, system, device and medium. The method obtains the sea surface pressure disturbance field, the sea surface wind field and the diagnostic field in the sea basin scale. Based on the multi-field joint determination, the typhoon track and the influence area in the climate model data are determined. Further iteration is performed to determine the typhoon track and the influence area data set. Based on the modal decomposition and by using the influence area data set and the TDF method, the typhoon trend change is determined. The method can comprehensively utilize the forecast model data wind pressure field data to jointly research and judge, effectively determine the typhoon center position and the typhoon track, and objectively and reliably determine the typhoon influence range by the sea surface pressure disturbance field, so that the influence of the artificial factor introduced by the artificial given area weight influence factor in the TDF method can be reduced. The typhoon change trend is determined by using the principal component analysis method on the objective typhoon influence range, and the operability is high.

The present application belongs to the technical field of aerosol measurement, and particularly relates to a method and system for measuring the surface tension of cloud droplets in the process of aerosol activation into clouds. The method comprises: building a system for measuring the surface tension of cloud droplets, including a cloud condensation nucleus counter, electrode plates, a high-voltage power supply, a condensation nucleus particle counter, and a differential mobility analyzer; applying a high-voltage electric field to cause positive and negative ions in the droplets to split under the traction of the electric field; aerosol samples are converted into droplets by absorbing water vapor in a cloud chamber, and the droplets split under the traction of the high-voltage electric field; the number concentration of the droplets in the outflow sample is measured by the condensation nucleus particle counter; the critical voltage at which the droplets split is obtained by fitting the graph of the number concentration of the droplets versus the voltage intensity; and the surface tension of the droplets is calculated according to the quantitative relationship between the critical voltage and the surface tension of the droplets. The present application can be used in atmospheric chemical transport simulation and climate models to evaluate the influence of the surface tension of cloud droplets on the number concentration and particle size of the cloud droplets.

The application discloses a kind of marine observation data gridding multi-level parallel acceleration calculation method and system.The application is based on the coarse-grained parallel implementation of climate model between MPI parallel architecture and parallel computing acceleration of ocean depth data, mainly includes the IO parallel reading of data, the calculation and distribution of climate model data, the synchronization of gridding data etc.;Based on the fine-grained parallel computing of OpenMP parallel architecture, the parallel computing of algorithm level between CPU core in node is realized, mainly includes the data gridding of each grid point in grid;For the gridding calculation of data in grid point, the distribution of data structure in memory is adjusted, and the specific calculation operation is accelerated using SIMD.The application can flexibly configure parallel scale according to specific hardware resources, realize the maximization of computing power utilization, and can provide computing power support for the construction of long-time sequence complete and reliable ocean grid data.

This invention discloses an inversion method for complex spatially distributed eddy viscosity coefficients in marine internal tide models, belonging to the field of marine numerical simulation technology. Key improvements of this invention include: the introduction of "independent point interpolation parameterization," a decisive solution to the computational efficiency bottleneck, transforming the problem from an "extremely difficult to compute" high-dimensional state to a "computable" low-to-medium-dimensional state, providing a feasible basis for subsequent operations; and the adoption of a "spectral projection gradient algorithm" as the optimization engine, further improving efficiency on the basis of dimensionality reduction, ensuring that the optimal solution is approximated with as few iterations as possible in each iteration step, thereby translating the advantages of dimensionality reduction into significant savings in actual computation time. This invention achieves a synergistic leap in accuracy, efficiency, and applicability, not only providing a powerful new tool for internal tide research but also potentially promoting the development of high-precision marine environmental forecasting and climate models, possessing significant scientific value and application potential.

The application discloses a satellite remote sensing data collection and preprocessing method and system, and relates to the technical field of satellite navigation. The method specifically comprises the following steps: collecting remote sensing images of a semiarid region through a satellite, analyzing pixel radiation and brightness gradient to identify bare land water features, constructing a leading atlas, extracting a boundary and registering to generate a standard structure, combining a climate model to correct to generate a reflectivity layer, transferring instructions when parameters are insufficient, reconstructing a queue by using a shortest algorithm to complete radiation calibration, atmospheric correction and geometric registration, and forming a preprocessing layer set. In the application, multi-band division is combined with radiation gradient calculation to enhance the separation degree of ground objects, boundary feature extraction and geometric registration are used to eliminate imaging distortion, a dynamic radiation model is fused with climate parameters to correct reflectivity, a double-threshold value is monitored to optimize a transmission strategy and avoid interruption, a priority algorithm is used to reconstruct a queue to reduce data delay, multi-level quality control is used to cooperatively process to form standard data, and the temporal and spatial reliability and application value are improved.

The invention discloses a sub-season meteorological disaster risk early warning effect improvement method and system, and the method comprises the steps: building a disaster process library based on climate observation data and a climate mode forecasting product; based on the disaster process library, calculating a monthly-scale disaster risk space level by using a disaster risk assessment model; based on the monthly-scale disaster risk space level, establishing a five-dimensional inspection index of the monthly disaster risk early warning capability of the multi-climate mode, and calculating an average score of an early warning product of each climate mode; and carrying out weighted fusion on the average score of each climate mode early warning product to obtain a final risk early warning result, and completing improvement on a traditional early warning method. According to the invention, by comprehensively evaluating the performance of the climate mode and the disaster model, a scientific basis is provided for optimizing the forecast mode and the report starting time in the business, and the precision and foresight of the disaster early warning service are powerfully supported.

This invention discloses a method, medium, and program product for assessing the impact of high-temperature and / or drought events on both the supply and demand sides of the power system. It belongs to the interdisciplinary field of meteorological disaster risk assessment and energy system security. First, multi-source data from the target area is collected and preprocessed. Then, high-temperature or drought events are identified based on temperature percentile thresholds and the SPEI drought index. A bivariate distribution model is constructed using a joint probability density function to identify combined high-temperature and drought events. Next, response models for the power supply side and load side are constructed separately to quantitatively assess the output changes of different power sources and the load response characteristics of various users under high-temperature and / or drought scenarios. Furthermore, key indicators such as power shortages and load risk exposure intensity are calculated through supply-demand coupling offset analysis. Finally, the risk model is driven by climate model prediction data to output the risk evolution trend under future scenarios. This invention can provide support for improving the climate resilience of power systems and for dispatching decisions.

The invention relates to the technical field of climate prediction, in particular to a high-resolution refined underlying surface sub-seasonal climate prediction method and a related device, and the method comprises the steps: respectively carrying out the correlation analysis according to monthly average analysis data, first monthly average reanalysis data, a weather reanalysis data set and a weather forecast center reanalysis data set, obtaining a plurality of key regions and a plurality of buffer regions, screening to obtain a target simulation region, a target time step size and a target spatial resolution, and obtaining a first region climate mode; processing according to an output result of the third-generation climate prediction system and the first region climate mode to obtain a target initial boundary field; obtaining a target initial field, an initial configuration file and a target region climate mode according to the first region ground data and an output result of a preset third-generation climate prediction system; and performing simulation analysis processing according to the target instruction data to obtain target prediction data. According to the method, the problem of regional high-precision and high-accuracy sub-seasonal climate prediction is solved.

This invention belongs to the field of medium- and long-term power generation in new energy, and discloses a medium- and long-term power forecasting method and related equipment based on ERA5 reanalysis data. This method uses multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, obtains the climatological average irradiance, and constructs an error correction model by combining it with historical measured data from the power plant. This allows for medium- and long-term power forecasting using historical data from the same period of the power plant's climatological output. By using multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, this method can accurately capture the climatological average irradiance of the area, effectively solving the forecasting problem caused by the lack of historical data for newly built or short-term operating photovoltaic power plants. Using this method is of crucial practical significance for power plants to reasonably declare medium- and long-term trading volumes in electricity transactions and reduce revenue losses caused by positive and negative volume deviations, significantly enhancing the competitiveness of power plants in the electricity market.

The present application relates to an interpretable and machine learning-based climate model preferably combined with a collection estimation method, belonging to the technical field of climate models, the method comprising: obtaining area-weighted average ozone column total of reanalysis data and CMIP6 model data, and constructing a data set; based on the data set, respectively adopting a traditional method and a decision tree SHAP method to screen the CMIP6 model; based on the CMIP6 model data screened by the traditional method and the decision tree SHAP method, a machine learning model is constructed; based on the constructed machine learning model, the optimal screening method and the corresponding optimal machine learning model are determined; based on the optimal screening method and the corresponding optimal machine learning model, the Antarctic ozone under different greenhouse gas emission scenarios is estimated. Through the present application, more accurate Antarctic ozone recovery estimation results can be obtained.

This invention provides a method for climate-adaptive reconfiguration of power distribution networks, comprising: obtaining typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; constructing power distribution network optimization models based on the typical, extreme, and expected climate datasets respectively; performing a risk quantification assessment of the power distribution network based on the optimization models to form candidate reconfiguration schemes; and selecting the candidate reconfiguration scheme with the smallest normalized integration assessment value as the climate-adaptive reconfiguration scheme through normalized integration evaluation of the expected net present value and expected integration level of the power distribution network under each candidate reconfiguration scheme. This invention enables power distribution network reconfiguration to adapt to various climate scenarios.

The invention provides a monthly-scale meteorological drought risk quantitative estimation method based on climate mode forecasting, and the method comprises the steps: determining an evaluation time period according to the occurrence condition of a drought process in an early-stage rainfall time period of an evaluation region, and carrying out the evaluation of the early-stage rainfall time period through the historical day-by-day rainfall observation data of a meteorological station in the evaluation time period for many years. Counting the number of duration days without effective precipitation and the average percentage of the accumulated precipitation in the evaluation period of each year, calculating a meteorological drought risk index of each year, and determining a grading threshold value of a meteorological drought risk grade by adopting a percentile method; and calculating a meteorological drought risk index of an estimated month and determining a risk level through the estimated data of the evaluation time period. According to the method, the non-effective rainfall duration day index considering the characteristic of less rainfall duration and the accumulated rainfall average percentage index reflecting the characteristic of less rainfall total amount are selected as estimation indexes, the complexity of an existing monthly-scale meteorological drought estimation method is overcome, and the estimation operability and reliability are improved.

The invention relates to the technical field of data processing, and discloses a horizontal grid registration and parallel computing method and system in climate mode coupling. The method comprises the following steps: performing registration processing on an atmospheric mode component through a component identity verification algorithm to obtain a mode component identifier; analyzing the horizontal grid geometric parameters according to the mode component identifier to obtain a grid basic information set; constructing masked and maskless horizontal grid configurations based on the grid basic information set; performing parallel domain decomposition processing on the dual grid configuration to obtain a parallel decomposition mapping table; establishing a global and local grid index bidirectional mapping relationship; and performing time step synchronization registration based on the bidirectional mapping relation to obtain a coupling validity verification result. The technical problem that a self-adaptive grid registration and parallel decomposition processing mechanism for air-sea coupling special requirements is lacked in an existing climate mode coupling technology is solved.

The invention discloses an inversion method of a complex space distribution vortex dynamic viscosity coefficient in an ocean internal tide mode, and belongs to the technical field of ocean numerical simulation. The key improvement point of the invention is as follows: 'independent point interpolation parameterization 'is introduced, which is a decisive scheme for solving the computational efficiency bottleneck, and the problem is converted from a'difficult-to-compute' high-dimensional state to a'computable 'middle-and-low-dimensional state, thereby providing a feasible basis for subsequent operation; a spectrum projection gradient algorithm is adopted as an optimization engine, on the basis of dimension reduction, the efficiency is further improved, it is ensured that in each iteration step, the optimal solution can be approached as few as possible, and therefore the advantage brought by dimension reduction is converted into great saving of actual calculation time. According to the method, the collaborative leap of precision, efficiency and applicability is realized, a powerful new tool is provided for internal tide research, the development of high-precision marine environment forecasting and climate models is more expected to be promoted, and the method has great scientific value and application potential.

The present application belongs to the technical field of new energy detection, and relates to a method, system and device for testing albedo of a photovoltaic power station and a storage medium. The method comprises: obtaining a total plan layout of a target photovoltaic power station and high-altitude image data; dividing a photovoltaic power station region by using a classification algorithm based on the total plan layout and the high-altitude image data to obtain a plurality of surface feature regions; determining a plurality of typical points of each surface feature region to obtain a single-point albedo of each typical point; determining the areas of the plurality of surface feature regions by using the high-altitude image data; and calculating a total albedo of the target photovoltaic power station by using a weighted average algorithm based on the single-point albedo of the typical points and the areas of the surface feature regions. The present application significantly improves the accurate evaluation of albedo under complex surface environment through multi-dimensional quantitative analysis of albedo, and provides key data support for ecological effect evaluation and climate model construction of photovoltaic power stations.

The application discloses a method and system for monitoring atmospheric physical parameters based on aurora optical images, which comprises the following steps: firstly, converting multi-band aurora original optical images collected from a polar observation space into Rayleigh brightness images; then, constructing a brightness cube of a unified geographical grid according to the Rayleigh brightness images, and dividing the brightness cube into a plurality of spatial units, and meanwhile, obtaining a local two-dimensional particle deposition parameter field of the corresponding polar observation space according to Rayleigh brightness values of the multi-band auroras in each spatial unit, and performing screening; further, applying a spatial physics reconstruction model to perform a magnetic coordinate system conversion and a vertical ionization rate layering calculation on the reliable spatial units obtained after the screening, so as to generate a four-dimensional ionization rate driving file; finally, importing the four-dimensional ionization rate driving file into a whole-atmosphere community climate model to obtain a global two-dimensional particle deposition parameter field. Since the particle deposition parameter field of the polar observation space is derived through the collection of aurora images of the polar observation space, the physical parameter monitoring of the polar atmosphere is realized.

The invention provides a combined temperature forecast correction method and system, and relates to the technical field of weather forecast. According to the method, three kinds of errors are systematically corrected, and the climate mode temperature forecast deviation is corrected. The method comprises the following steps: firstly, considering an error source of temperature forecast, and respectively disassembling observation temperature data and original forecast temperature data in a training period into three independent components, namely a mean term, a trend term and a residual term; and using conditional Gaussian correction to optimize the residual term error, and keeping the rank correlation structure of the original ensemble forecast. And according to requirements, performing mean value correction, trend correction and residual error correction on the original forecast temperature data, combining a mean value correction result, a trend correction result and a residual error correction result of the original forecast temperature data, and outputting a combined temperature forecast correction result. The method is used for temperature forecast correction and has the advantages of being good in correction effect, flexible to use, efficient in calculation and the like.

A method, a computer system, and a computer program product for regionalized climate models is provided. Embodiments of the present invention may include selecting a class of a reduced order model. Embodiments of the present invention may include building a neural network in a reduced order space. Embodiments of the present invention may include recovering full state dynamics. Embodiments of the present invention may include training a model. Embodiments of the present invention may include providing an output.

The application discloses a kind of ocean observation data gridding method and system based on climate model.The application provides more optimal initial field and flow-dependent ocean parameter space by introducing coupled model comparison plan model set as dynamic set sample, and is more in line with real ocean physical variability, and then more reasonably reconstructs irregular ocean subsurface observation data into data set with complete global ocean coverage.In addition, multi-scale cyclic reconstruction technology is used, while gradually reconstructing multiple typical scale variability, the problem that traditional grid assimilation method can introduce deviation to climate state system is avoided.This method promotes the construction of more optimal long-time complete reliable ocean subsurface grid data set, and provides more complete and accurate data set for climate change and ocean science research.The application is mainly used for but not limited to ocean subsurface observation data gridding and construction of global complete grid data set.

A plurality of climate models for a variety of hazards and / or initial conditions to produce a collection of Hazard Exposure (Risk) Maps. The Hazard Exposure Maps are transformed to high resolution (and may be filtered) and convolved with auxiliary data related to one or more hazards. The now super resolution maps are input to a learning engine along with spatiotemporally harmonized historic events and active near real-time events to produce a calibrated model (Asset Level Exposure Risk Estimation Model) that utilizes the super resolution maps and asset location(s) to project risk for each asset. The projected risk may be provided to asset operators / owners, initiate signal alerts and other messages, invoke automated responses to protect / preserve assets. The risk results may also be grouped in risk valuation datasets.

The application discloses a kind of coupling regional climate model and water vapor tracking algorithm's analysis method of climate effect of water diversion project, including S1, the water diversion quantity of water diversion project of estimation of each time period and the water delivery flow-water surface area relationship of water delivery channel;S2, the land-air flux caused by water diversion project is calculated, the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport equation in the water vapor transport

The present application relates to the field of photovoltaic power generation, and particularly relates to a local climate regulation and power generation gain method and system for a desert photovoltaic power station; environmental data and equipment state data in an internal area and an upwind area of the photovoltaic power station are collected in real time to generate standardized data; the standardized data is input into a coupled climate model for prediction to generate a prediction result; based on the prediction result, a global optimization target of maximizing a net present value of power generation gain is considered, power generation loss avoided by intervention, energy cost consumed by intervention, and loss cost of intervention actions on equipment life are comprehensively considered to generate an optimal control scheme; according to the optimal control scheme, a distributed array of environmental regulation equipment is dispatched to perform physical intervention; the present application realizes a change from passive operation and maintenance to active regulation and control of the desert photovoltaic power station, and significantly improves power generation efficiency and reduces operation and maintenance cost.

The invention discloses an all-sky imager aerosol optical parameter inversion algorithm constructed based on machine learning, and belongs to the field of meteorological observation. According to the method, an iterative threshold segmentation method and threshold self-optimization are used, a clear sky extraction quantitative standard is established for the first time, and the problems of manual dependence and poor consistency are solved; according to the method, an equal zenith angle scanning mode is simulated, multi-dimensional feature extraction is carried out from a sky area picture, aerosol scattering information reflecting multi-angle and multi-position information related to an aerosol scattering phase function and multi-angle information of total brightness is obtained, and key support is provided for SSA inversion; according to the method, an integrated regression model is constructed by taking the multi-dimensional features as input and AOD and SSA as output, a spatial difference and time difference matching sample is set, synchronous output is realized, a sample standard is defined, and efficiency and precision are greatly improved. The method is applied to the fields of atmosphere remote sensing, climate mode research, air quality evaluation and the like.

The invention provides a power distribution network climate adaptability reconstruction method. The method comprises the following steps: acquiring a typical climate data set, an extreme climate data set and an expected climate data set according to a global climate model and a regional climate model; constructing a power distribution network optimization model according to the typical climate data set, the extreme climate data set and the expected climate data set; performing risk quantitative evaluation on the power distribution network according to the power distribution network optimization model to form a power distribution network reconstruction candidate scheme; and through normalized integration evaluation of an expected net present value and an expected integration level of the power distribution network under each power distribution network reconstruction candidate scheme, taking the power distribution network reconstruction candidate scheme with the minimum normalized integration evaluation value as a power distribution network climate adaptability reconstruction scheme. Through power distribution network reconstruction, the method can adapt to various climate scenes.