188 results about "Climate variation" 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 discloses a thermal power generating unit decommissioning path planning method and device based on meteorological and hydrological risks, equipment, a medium and a product, and relates to the field of power system decision optimization. The method comprises the steps of obtaining meteorological and hydrological risk data and a decommissioning index of a thermal power generating unit; according to the decommissioning index and the meteorological and hydrological risk data under the climate change, a progressive hierarchical screening logic method is adopted for screening and constraint progressive correction processing; performing mapping and nonlinear correlation interaction analysis by adopting Monte Carlo simulation and a gradient boosting decision tree and combining multiple groups of parameter combination data, such as water temperature and runoff volume in meteorological and hydrological variables; and performing collaborative optimization processing based on the adaptive strategy model and a set analysis function to obtain an optimal strategy, and then performing mapping and analysis processing in combination with an analysis result to obtain targeted strategy list information for planning the decommissioning path of the thermal power generating unit. The invention aims to realize the planning of the decommissioning path of the thermal power generating unit.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention discloses a glacier material balance simulation method and system based on feature interaction, and belongs to the technical field of glacier hydrology and climate change monitoring, and the method comprises the steps: sequentially carrying out the uniform format processing and slope feature enhancement of a multi-source data set of a glacier, and dividing the multi-source data set into a simulation data set and an observation data set; training a model based on a trans-attention Transform encoder framework by using the simulation data set to obtain a pre-training model; when one-glacier-leaving cross validation is carried out on the pre-trained model, multi-stage fine tuning training is carried out by using the observation data set to obtain a plurality of fine-tuned models; performing model integration based on performance index screening on the fine-tuned models, and constructing an integrated simulation model; and preprocessing the meteorological dynamic characteristics and the static topographic characteristics of the target glacier, inputting the preprocessed meteorological dynamic characteristics and static topographic characteristics into the integrated simulation model, and carrying out glacier material balance simulation to obtain a glacier material balance simulation result. According to the method, the simulation uncertainty is reduced, and the high-precision simulation of the glacier material balance is realized.

The invention discloses a method for predicting reservoir inflow and under-dam water resource change trend in a future change environment. The method comprises the steps of obtaining future monthly rainfall data, future monthly temperature data, future monthly evapotranspiration data and future land utilization data based on a GCM climate mode; the future runoff process of the reservoir control section is obtained through simulation in combination with an RCCC-WBM model; based on a data mining technology and an LSTM deep learning coupling model, mining relation characteristics of future reservoir runoff and the under-dam water resource quantity, and obtaining the under-dam water resource quantity by inputting the reservoir runoff; and a nonlinear trend analysis method and the like are adopted to analyze change trend characteristics of water resources under the dam in the future. The method can fully solve the problem of insufficient consideration of climate change and land utilization in the existing method, and can significantly improve the prediction precision of the water resource under the dam in the future.

The invention discloses a sudden snow disaster risk degree probability prediction method oriented to insufficient historical time sequences. The method comprises the following steps: step 1), constructing a multi-index risk index based on related data such as historical snow disasters; 2) performing time sequence modeling on the violent snow disaster risk index by adopting an autoregressive integral moving average model, and capturing trend and periodic characteristics of the violent snow disaster risk index; 3) discretizing a continuous risk index of modeling production into a plurality of risk levels, and constructing a state transition probability matrix; step 4), introducing space neighborhood influence and carrier exposure constraint, and constructing a Markov space weighted state transition model; 5) coupling CMIP6 climate scene data, and dynamically adjusting future state transition probability, and 6) fusing ARIMA trend prediction and Markov state transition probability, and outputting future multi-period snowstorm disaster risk level probability distribution.According to the method, the accuracy of snowstorm disaster risk prediction and the modeling ability and prediction adaptability of future climate change scenes are improved.

The invention provides a drought and flood sudden change prediction method and system based on artificial intelligence, and the method comprises the steps: collecting the in-out reservoir runoff observation data of a reservoir in a target region, obtaining a restored natural reservoir runoff series based on a water balance method, calibrating a long-short-term memory model through a minimum batch gradient descent method, and carrying out the prediction of the drought and flood sudden change. Based on the natural reservoir runoff, the actual reservoir runoff and meteorological data, constructing a long-short-term memory model to simulate the influence of water conservancy project regulation and storage on the runoff; based on an earth system mode set and a multivariable deviation correction method, performing spatial downscaling on output data of earth system modes to obtain corrected meteorological variables; driving a multi-member set of an earth system mode, and separating by adopting a detection attribution technology to obtain a contribution rate of man-made climate compulsion to drought and flood sudden turning event evolution; and predicting a daily runoff process under future climate change, and predicting a future drought and flood sudden turning event by adopting deep learning and superposition of regulation and storage influence of a water conservancy project.

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

The invention discloses a corn planting risk prediction method and system based on a climate change trend, and relates to the technical field of environment prediction. The method comprises the following steps: acquiring climate-related data of corn planting; constructing a risk prediction model; the risk prediction model adopts an LSTM-Transform hybrid neural network to process time sequence meteorological data, introduces an SHAP value interpretation model to quantify the contribution degree of each climate factor to the output reduction risk, and couples a DSSAT crop model and a risk probability prediction module; inputting the climate-related data into the risk prediction model, and determining a final corn planting risk result through operation of the risk prediction model; the corn planting risk result comprises a regional risk grade map, planting window period optimization suggestions and extreme weather event probability early warning. According to the invention, the accuracy of corn planting risk prediction can be improved.

The invention of system of observation and monitoring the earth warming and changes in polar ice behavior using LoRa to satellite technology is a system for measuring, observing and monitoring weather and environmental conditions in order to determine the effects of environmental conditions and climate changes on the way ice masses change in the polar regions of the earth. This invention is related to the rocket equipped with several modules for measuring the conditions of the surrounding environment as well as data transmission through the LoraWan protocol to a fixed or deployable station. Also, in this invention, if there is a need to calibrate any of the modules in the meteorological rocket, the central server structure, which is equipped with an artificial intelligence analyzer, can calibrate the device by comparing the arrays received from the surrounding rockets and analyzing the data received from these systems.

The invention relates to the technical field of meteorological disaster assessment, in particular to a rainstorm flood-based risk assessment system, which comprises an acquisition terminal, a law analysis terminal, a prediction terminal and an assessment terminal which are in progressive signal connection, the prediction terminal is used for predicting future extreme rainfall and flood influence through climate change conditions and in combination with historical rainstorm flood evolution rules analyzed by the rule analysis terminal, the prediction terminal comprises a rainfall prediction module and a flood influence module, and the evaluation terminal is used for comprehensive prediction through the rainfall prediction module and the flood influence module. And setting an evaluation framework for the flood risk, and estimating a future flood risk change trend. According to the method, multiple factors are comprehensively considered, the prediction precision is improved, more comprehensive and accurate support is provided for prediction, evaluation and response of rainstorm flood disasters, and therefore comprehensive and multi-level evaluation of disaster loss is effectively reduced.

The invention relates to the technical field of environmental science, and discloses an SWAT-based BMP optimal configuration method adaptive to climate change, and the method comprises the steps: collecting pollution data of a target agricultural region, and constructing an NPS simulation model of the target agricultural region based on an SWAT model; collecting basic data of a target agricultural area, determining implementation data of a plurality of preset optimal management measures, and constructing an optimal management measure database; based on the optimal management measure database, performing parameter optimization on the NPS simulation model to form an NPS simulation optimization model; simulating climate change in future time according to the climate observation data and the historical meteorological data, and constructing a meteorological database; and based on the meteorological database, operating the NPS simulation optimization model, and performing optimal management measure optimization configuration analysis through a genetic algorithm to obtain an optimal management measure optimization result. According to the method, resources can be reasonably configured, and the flexibility, adaptability and actual effect of optimal management measures are improved.

The invention provides an urban extreme rainfall risk assessment and drainage optimization method fusing a composite threshold and a Chicago method, and relates to the field of power grid disaster prevention and reduction planning and meteorological and hydrological analysis, and the method comprises the following steps: employing a quartile method and a composite threshold method to recognize and classify extreme rainfall events; constructing an extreme rainfall pattern model, and determining a minute-level rainfall intensity curve by using the extreme rainfall pattern model; generating an extreme rainfall spatial-temporal distribution characteristic spectrum according to the extreme rainfall event set in combination with a spatial interpolation technology fused with a deep learning model; the drainage capacity of the urban drainage system is evaluated, and the urban drainage system is optimized according to the evaluation result. The method has high adaptability, can be popularized to urban areas with different terrains and climate conditions, provides an efficient technical tool for coping with extreme weather events under the climate change background and improving the disaster prevention and reduction capacity of a power system, and has important social and economic values.

A system and method for architecturally designing and manufacturing building structures customized for environments vulnerable to extreme weather events or other natural phenomena. The method includes determining an environmental susceptibility of a proposed building location, designing building components factoring in geographical and climate-related features, manufacturing the building components, and assembling the building structure in a manner better configured to resist extreme weather or natural events.

The invention discloses an alpine grassland livestock capacity monitoring and predicting method and system based on climate change. The alpine grassland livestock capacity monitoring and predicting method based on climate change comprises the following steps: 1) modeling data preprocessing: acquiring grassland type data and meteorological factor data, and carrying out space-time mask fusion, year / month scale space-time alignment and dynamic standardization to obtain grassland type standardized data; 2) feature screening and XGBoost modeling: performing feature importance screening and multi-parameter grid search optimization on grassland type standardized data by using a random forest model, and establishing and verifying a model; 3) preprocessing future CMIP6 climate factor data; and (4) predicting the livestock carrying capacity by using the results of the steps (2) and (3). According to the method, the potential influence of the climate change on the alpine grassland ecosystem can be evaluated on the regional scale, the variation trend of the future livestock capacity is accurately predicted, and a scientific basis is provided for formulating pasturing area management, ecological restoration and climate adaptability strategies.

The invention relates to the field of climate change influence assessment, and discloses a method for quantizing contribution of natural internal variability to drought intensity based on a circulation similarity method, and the method comprises the steps: data collection; evaluating the drought strength based on the difference between the rainfall distance and the potential evapotranspiration distance, and quantifying the relative contribution of rainfall deficit and evapotranspiration demand to the drought strength; based on a circulation similarity method, a regional large-scale atmospheric circulation background is obtained, and contribution of a natural internal variation rate to drought intensity, rainfall deficit and evapotranspiration requirements is evaluated; estimating long-term evolution trends of drought intensity, rainfall deficit and evapotranspiration demands under different carbon emission scenes in the future; the method has the advantages that the natural internal influence mechanism of drought intensity change is defined, the relative contribution of historical and future rainfall deficit and evapotranspiration requirements to the drought intensity is quantified, the reliability of future drought trend estimation is enhanced, theoretical support is provided for making disaster prevention and reduction policies, and the accuracy of drought risk prediction is improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a shared unmanned aerial vehicle distribution method and system based on route planning, and the method comprises the following steps: collecting data related to an unmanned aerial vehicle distribution task, including geographic information and climate condition data, associating a place coordinate with a time label through geocoding, and carrying out the data arrangement according to a time sequence, and generating a task serialization list. According to the method, the accuracy of task planning is remarkably improved by utilizing geographic information and climate condition data, so that the unmanned aerial vehicle distribution can more effectively cope with the problems of different geographic environments and climate changes, and accurate sorting of task priorities is realized by dynamically analyzing the emergency degree, the customer level and the expected delivery time of the tasks; the service pertinence and the task response speed are improved, the flight time and the energy consumption prediction are combined, the resource allocation is optimized, the cost effectiveness of the flight plan is improved, and the flight route and the task allocation are dynamically adjusted by monitoring the environment change and the state of the unmanned aerial vehicle in real time.

The invention, which belongs to the technical field of atmospheric monitoring, discloses an atmospheric monitoring big data analysis system comprising a demand identification module and a demand analysis module. The demand identification module is used for carrying out real-time demand analysis on the atmospheric monitoring data to obtain a corresponding demand detail list; the demand analysis module is used for carrying out calibration analysis on the demand detail list to obtain application calibration data of a corresponding application demand, supplementing the application calibration data into the demand detail list, and marking the current demand detail list as a demand analysis list; displaying the demand analysis table to a corresponding manager; through mutual cooperation between the demand identification module and the demand analysis module, intelligent analysis of atmospheric monitoring data is realized. The challenges of climate change and continuous change of meteorological monitoring requirements in the urbanization process can be effectively handled; with the continuous evolution of different commercial technologies on atmospheric monitoring data application requirements, the limitation problem of an existing atmospheric monitoring system is broken through.

The invention provides a method and system for predicting drought and flood sudden turning disasters under climate change, and the method comprises the steps: calculating a drought and flood sudden turning index of a target drainage basin in a first time period, and carrying out the fitting of a hook-shaped Hook response function of a relation between a drought and flood sudden turning index prediction value and a near-earth temperature; based on the air temperature data of the target watershed output by the plurality of global climate modes, determining a preliminary prediction value of a drought and flood sudden turning index in a second time period; constructing a three-variable emergence constraint model according to the temperature change trend of each global climate mode in the third time period and the corresponding drought and flood sudden turning index preliminary prediction value; substituting the temperature change trend of the fourth time period into the emergence constraint model to obtain a drought and flood sudden turning index prediction value of the target drainage basin in the second time period; and based on the corrected prediction result of the drought and flood sudden turning index in the future period, identifying the drought and flood sudden turning risk of the target drainage basin in the future period, and evaluating possible social and economic risks.

The invention discloses a hydrological analysis method and system based on SWAT model runoff reconstruction and a storage medium. The hydrological analysis method comprises the following steps: 1, obtaining research basin runoff data, meteorological data, digital elevation model data, land utilization data and soil data; 2, based on the data in the step 1, analyzing the evolution trend and potential mutation points in the runoff time sequence period by adopting a mutability test method; 3, dividing a natural period and a variation period based on the mutation points, and obtaining a natural runoff sequence by using an SWAT model; and 4, based on a SWAT model simulation result, extracting multiple hydrological indexes representing hydrological situation changes by adopting an IHA method, extracting ecologically-related hydrological indexes ERHIs from the hydrological indexes by adopting a PCA method, and calculating contribution rates of climate changes and human activities to the ecologically-related hydrological indexes by adopting a quantitative attribution method. The runoff driving mechanism of climate factors and human activities is disclosed, and the physical interpretability and decision support capability of the SWAT model are enhanced.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

This invention discloses a cluster analysis method for diagnosing the composite trend characteristics and intensity of climate change based on multi-source data. The method includes: selecting a study area, identifying the study objects, and acquiring multi-source data using big data technology; analyzing the changing trends of the study objects from each data source using the Sen slope estimation method; evaluating the reliability of the changing trends of the study objects from each data source using the Mann-Kendall test; selecting composite changing trend evaluation indicators for the study objects based on whether they pass the reliability test; distinguishing the changing trend characteristics of the study objects from each data source under different conditions, classifying and summarizing the composite changing trend characteristics of the study objects, calculating the ratio of composite changing trend characteristics of the study objects, and diagnosing the composite changing trend characteristics and intensity of the study objects. This method is based on cluster analysis theory and uses multiple data sources to determine the composite changing trends of hydrological and meteorological elements under specific conditions. It can be directly applied to the composite diagnosis of climate change in the hydrological and meteorological field.

The invention provides a surface water-underground water integrated simulation method and system coupled with a glacier module, the method is based on an extended SWAT-GL model, rainfall, accumulated snow melt water and glacier melt water are jointly used as daily effective rainfall input, infiltration supply and river water level are transmitted to MODFLOW through a daily interface, and the water level of the MODFLOW is effectively simulated. And simulating underground water head change and surface water-underground water exchange capacity by utilizing a three-dimensional underground water flow equation, and feeding back a result to SWAT-GL river water balance, thereby realizing bidirectional dynamic coupling of the glacier-accumulated snow-surface-underground water whole process. The system breaks through the limitation that glacier mass balance and groundwater dynamics are difficult to depict synchronously in cold region simulation, can reveal a supply and regulation mechanism of glacier melt water to a groundwater system under the driving of climate change under a fine time scale, and remarkably improves the simulation capability of a cold region water circulation key process.

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 invention discloses a method for estimating the photosynthetic efficiency of wetland vegetation under the influence of climate change, relates to the technical field of agricultural remote sensing, and aims to solve the problems that the photosynthetic efficiency of the wetland vegetation is inaccurately estimated and the future cannot be predicted in the conventional method. The method comprises the steps of 1, obtaining and preprocessing a data set; 2, collecting the month-by-month data into a growth season data set; 3, calculating to obtain a multi-year average LAI value in the research time period; 4, obtaining wetland vegetation distribution as a research area range; 5, obtaining a pixel-by-pixel growth season SIF value, a pixel-by-pixel growth season LAI value, a pixel-by-pixel growth season historical meteorological element value and a pixel-by-pixel growth season future meteorological element value in the research area range; 6, calculating the photosynthetic efficiency value of the wetland vegetation pixel by pixel in the range of the research area; 7, constructing a pixel-by-pixel wetland vegetation photosynthetic efficiency estimation model under the influence of climate change; and 8, predicting the photosynthetic efficiency of the future wetland vegetation in the range of the research area. According to the method, the future wetland vegetation photosynthetic efficiency can be quickly and accurately estimated and predicted on the regional scale.

The invention relates to the field of hydrological disaster risk assessment, and discloses a man-made signal identification and future estimation method for hydrological drought risks, and the method comprises the steps: collecting data; hydrological drought identification; hydrological drought occurrence probability trend quantification; quantification of contribution of human climate change and human water taking and land management change; human activity signal detection of hydrological drought occurrence probability; hydrological drought attribution risk score quantification; identifying human activity signals in hydrological drought based on a signal-to-noise ratio criterion; the uncertainty source of the future hydrological drought occurrence probability trend is quantified; according to the method, the global hydrological drought occurrence risk change in historical and future periods under the influence of human activities is evaluated, and the human activity signals in the global hydrological drought occurrence risk change are identified, so that the understanding of the global hydrological drought risk change and influence mechanism is enhanced, and a scientific basis is provided for making a hydrological drought disaster prevention and reduction decision.

The present invention discloses a method for tracking four-dimensional soil moisture drought events and identifying warming signals, the method comprising the following steps: data acquisition; four-dimensional soil moisture drought event identification; four-dimensional soil moisture drought event feature quantification; human-induced warming signal detection of the temporal evolution of four-dimensional soil moisture drought features in historical periods; four-dimensional soil moisture drought future evolution prediction; and four-dimensional soil moisture drought event driving factors. The present invention has the beneficial effects of exploring the future spatiotemporal evolution characteristics of four-dimensional soil moisture drought, analyzing the driving factors and physical mechanisms of four-dimensional soil moisture drought, and providing a strong scientific basis for responding to global climate change, ensuring water resource security, and formulating disaster prevention and mitigation strategies.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

A technological improvement to a system for vehicle navigation is provided. The improvement diminishes a contribution to global climate change, e.g., global warming, by generating a flight path (generated flight path) or modifying the flight path (modified flight path) of a vehicle to avoid an atmospheric region in which the vehicle would create a contrail if the vehicle travelled through the atmospheric region.