50 results about "Precipitable water" patented technology

The present invention is a rainfall prediction method, system, device and medium based on machine learning, which relates to the field of meteorological prediction technology. It uses atmospheric precipitable water volume (PWV) data, rainfall data and related meteorological parameters to input into a trained rainfall prediction network, and realizes accurate prediction of rainfall through an improved Transformer model. The model includes an encoder, a decoder and a final output layer, wherein feature extraction is performed inside the encoder through a multi-head probabilistic sparse self-attention module and a distillation module, and the encoder output containing feature information is used as the input of the decoder. The decoder passes through the decoder mask multi-head probabilistic sparse self-attention layer, and performs a multi-head self-attention operation with the intermediate result output by the encoder, and finally adjusts the data output dimension through a fully connected layer to generate a prediction result.

The invention provides an atmospheric precipitable water quantity inversion method and system based on multi-source satellite remote sensing data, and relates to the technical field of remote sensing data. After clear sky pixels are screened, radiation brightness temperature data, digital elevation model data and spatio-temporal feature coding data are processed through three feature extraction branches, obtained feature representations are spliced and input into a feature fusion network, a spatial attention module is introduced in the feature fusion stage, finally, a composite loss function is adopted for training, and the feature fusion network is constructed. And high-precision atmospheric precipitable water quantity inversion is realized. According to the method, the multi-source data characteristics are fully utilized, and the inversion precision and the space continuity are improved.

The invention discloses an assimilation method based on a GNSS multi-dimensional water vapor cooperation WRF meteorological mode. The assimilation method comprises the steps of 1, GNSS water vapor inversion; 2, assimilating the precipitable water quantity inversed by the GNSS for the first time; 3, carrying out GNSS three-dimensional chromatography inversion; 4, interpolating the water vapor density value of the three-dimensional chromatography grid into the forecast three-dimensional grid to obtain a water vapor density value of a three-dimensional interpolation grid which is the same as the forecast three-dimensional grid; 5, carrying out inversion on the water vapor density value of the three-dimensional interpolation grid and the temperature data of the forecast three-dimensional grid to obtain a relative humidity profile; and 6, assimilating the relative humidity profile again. The method is reasonable in design, first assimilation is carried out by using the precipitable water volume inverted by the GNSS, the relative humidity profile is obtained by using the three-dimensional water vapor density and the temperature data of the first assimilation, the relative humidity profile is assimilated again based on the WRF meteorological mode, assimilation is effectively carried out by using the three-dimensional water vapor density data, and the meteorological parameter forecasting precision is improved.

The invention discloses a rainstorm ground waterlogging prediction method, device, equipment and product, and relates to the technical field of urban waterlogging prediction.The rainstorm ground waterlogging prediction method comprises the steps that ephemeris and ground observation station data are obtained, the atmospheric moisture delay amount and the high-altitude water vapor conversion factor are calculated respectively, the historical water vapor content is calculated, and the future atmospheric precipitable water amount is predicted; rainfall time distribution data are obtained through rainstorm intensity decomposition; calculating the rainwater overflow amount of the catchment area in combination with the water discharge amount time distribution data of the urban drainage pipe network; and iterative calculation is carried out by using a digital elevation model grid to obtain the waterlogging depth, and waterlogging early warning information is sent out. By constraining the total rainfall amount, the rainfall amount can be effectively constrained by predicting the precipitable water amount based on the satellite ephemeris; the underground pipe network drainage is restrained, and a water flow condition in a pipe channel is simulated by adopting an SWMM model; and finally, the waterlogging accumulated water volume is calculated, constraint verification is carried out on each link of urban waterlogging, and relatively high precision and relatively long timeliness are ensured.

The embodiment of the invention provides a Beidou water vapor inversion method and system based on a buoy platform. The method is applied to the technical field of data processing, and comprises the following steps: receiving a Beidou dual-frequency signal through a direct antenna to obtain original observation data; the multi-path effect and the platform shake are inhibited by combining buoy motion characteristics obtained by the MEMS sensor to obtain pre-processed data; calculating a troposphere delay estimated value; establishing a delay-water vapor conversion model; suppressing short message interference to obtain effective observation data; and inputting the data into a model to calculate the amount of precipitable water and the relative humidity. According to the method, the key technical problems of sea surface multi-path effect suppression, platform shake compensation, marine atmosphere modeling, short message interference elimination and the like are solved, so that reliable water vapor parameter inversion on the buoy platform is realized.

The invention discloses a time-space intelligent area rainfall classification weighting mixing quantitative forecasting method based on Beidou water vapor. The method comprises the following steps: 1, acquiring atmospheric precipitable water and meteorological historical data inverted by Beidou; 2, constructing and training a BPNN rain-free model and a BPNN rain model; 3, training based on the univariate meteorological parameters in combination with CNN and LSTM network models; 4, on the basis of CNN and LSTM network model input, constructing a BPNN rain-free hybrid model and a BPNN rain hybrid model, and training; and 5, subsequent area rainfall classification weighting mixing quantitative forecasting is carried out. The method is simple in steps and reasonable in design, weighting is carried out on the basis of predicted values of the BPNN rain-free model and the BPNN rain model and predicted values of the BPNN rain-free mixed model and the BPNN rain mixed model, subsequent quantitative rainfall forecasting of the to-be-studied area is achieved, and short-term rainfall forecasting precision is improved.

The invention discloses a Beidou navigation system short-time rainfall prediction method based on an LSTM network, and the method comprises the following steps: firstly, carrying out the seasonal division of atmospheric rainfall data according to a rainy season and a non-rainy season; constructing and training an LSTM network prediction model; acquiring real-time atmospheric rainfall data of an observation point, and inputting the real-time atmospheric rainfall data into a pre-trained LSTM network prediction model so as to output a rainfall probability prediction value of the station after one hour; converting the rainfall probability prediction values of the plurality of single sites into a graph structure according to the spatial information, converting the graph structure into an adjacent matrix, and re-inputting the adjacent matrix into a pre-trained LSTM network prediction model to obtain a networked prediction result presented in a graph form; extracting a predicted value of any position from the networked prediction result by using a multi-linear fitting algorithm; and weighting the predicted value of any position by adopting a distance weighting strategy to obtain a final prediction result and outputting the final prediction result.

The invention discloses a foundation space ionosphere and troposphere integrated joint detection method and device, and belongs to the technical field of satellite navigation, and the method comprises the steps: detecting the vertical total electron content of an ionosphere: extracting the inclined total electron content of the ionosphere at an ionosphere puncture point, and converting the inclined total electron content into the vertical total electron content of the ionosphere; detecting a foundation Beidou ionosphere disturbance index; and detecting an ionosphere phase scintillation index, a troposphere zenith total delay, a troposphere precipitable amount and an ionosphere amplitude scintillation index, and outputting ground-based Beidou space ionosphere and troposphere integrated joint detection parameters. The method effectively improves the reliability, robustness and meteorological perception capability of a navigation positioning system in a complex atmospheric environment, and is especially suitable for special environmental conditions such as severe convection weather, typhoon landing, violent disturbance of an ionized layer and the like.

The invention discloses a refined remote sensing atmospheric water vapor inversion method based on a GNSS and a Transform. The method comprises the following steps: acquiring OLCI remote sensing data, ERA5 meteorological data and GNSS-PWV data of a target area; performing quality control on the OLCI remote sensing data, and screening effective observation data; performing space-time registration on the OLCI remote sensing data, the ERA5 meteorological data and the GNSS-PWV data, and constructing a multi-dimensional feature vector; inputting the multi-dimensional feature vector into a Transform model for training, and learning a coupling relationship between features through a self-attention mechanism; and outputting an atmospheric precipitable water quantity inversion result based on the trained Transform model. According to the invention, the precision and spatial resolution of atmospheric water vapor detection are improved.

The invention discloses a rainfall short-term and imminent forecasting method and device based on GNSS-PWV and numerical weather forecast fusion, and the method comprises the steps: calculating the real-time atmospheric precipitable water amount measured through GNSS signal delay, and obtaining the meteorological observation data and numerical weather forecast data of a base station; respectively constructing a physical forecasting model and a machine learning forecasting model through the atmospheric precipitable water amount, the variable quantity of the atmospheric precipitable water amount, the base station meteorological observation data and the numerical weather forecasting data, and respectively forecasting the precipitation probability and the precipitation amount by utilizing the physical forecasting model and the machine learning forecasting model; and a hybrid forecasting model is constructed, the model fuses the precipitation probability and precipitation forecast by the physical forecasting model and the machine learning forecasting model, and the final precipitation probability and final precipitation are obtained. According to the method, on the basis of traditional numerical weather forecast, time-varying information of GNSS-PWV is intelligently fused, and adaptive optimization is realized by dynamically adjusting the weights of a physical model and a data driving model, so that the precision and reliability of short and temporary rainfall forecast are remarkably improved.

The invention discloses a GNSS (Global Navigation Satellite System) and wind cloud satellite assisted meteorological and hydrological coupled runoff forecasting method. The method comprises the following steps: 1, GNSS two-dimensional water vapor inversion; 2, performing water vapor inversion on the Fengyun No.4; 3, performing three-dimensional variation assimilation on the precipitable water volume inverted by the GNSS and the precipitable water volume inverted by the FY-4 star A based on a WRF meteorological mode; and 4, runoff forecasting is carried out based on the WRF-Hydro hydrological model. The method is simple in step and reasonable in design, three-dimensional variational assimilation is performed on the precipitable water volume inversed by the GNSS and the precipitable water volume inversed by the FY-4 star A based on the WRF meteorological mode, accurate meteorological driving data is provided for a WRF-Hydro hydrological model, the dependence of WRF meteorological mode data assimilation on conventional meteorological observation data in traditional meteorological and hydrological coupling forecast is avoided, and the method is suitable for popularization and application. And the applicability of the WRF and WRF-Hydro model coupling mode in the meso-and small-scale watershed in the conventional data scarcity region is further improved.

The invention relates to the technical field of meteorological remote sensing, in particular to a three-dimensional wind speed and wind direction detection method and system based on GNSS tomography inversion, and the method comprises the steps: building a three-dimensional water vapor density tomography inversion model through obtaining observation data and meteorological observation data of a plurality of GNSS observation stations, obtaining water vapor density distribution of different height layers, and obtaining a three-dimensional wind speed and wind direction detection model; further obtaining the PWV (atmospheric precipitable water) of each height layer; a three-dimensional wind field inversion algorithm is established by utilizing a coupling relation between water vapor transportation and a wind field and combining a spatial position of a GNSS observation station and spatial and temporal variation characteristics of PWV of different height layers, so that quantitative estimation of wind speeds and wind directions of the different height layers is realized, and three-dimensional wind speed and wind direction quantitative detection based on GNSS tomography inversion can be realized. And important observation data support is provided for meteorological monitoring and numerical weather forecast.

The invention relates to the technical field of satellite navigation meteorological remote sensing, and discloses a water vapor ionized layer snow depth integrated detection method and system. The method comprises the steps that direct and reflected signals of a Beidou satellite are received through fusion antenna array differentiation, carrier phase and pseudo-range observation values are obtained through carrier tracking loop synchronous demodulation, the total atmospheric rainfall amount and the total ionosphere electron content are extracted through a troposphere and ionosphere combined calculation algorithm, the snow depth is inverted through reflection path geometric difference, and the snow depth is calculated. And carrying out space-time synchronization constraint processing on the extended Kalman filter to realize multi-parameter integrated detection. The problems of low efficiency, insufficient precision and lack of cooperative processing caused by independent observation of water vapor, ionized layer and accumulated snow parameters in the prior art are solved, and the observation efficiency and the measurement precision of multi-parameter integrated detection are improved.

The embodiment of the invention discloses a prediction method of atmospheric precipitable water and related equipment, geographic coordinates in the prediction method of atmospheric precipitable water provide accurate geographic position information, time parameters can capture daytime and seasonal change rules, and land cover type codes reflect water vapor contents in different land surfaces. A ZTD value is decomposed into a trend item, a season item and a residual item, high-frequency characteristics in the residual item, namely an intrinsic mode function, are analyzed through mode decomposition, and the intrinsic mode function can capture complex changes of meteorological conditions more meticulously, so that the weather condition can be accurately detected. The geographic coordinates, the time parameters, the land cover type codes, the ZTD trend term, the season term and the intrinsic mode function are input into the trained bidirectional long-short-term memory neural network and the multilayer perceptron to predict the PWV value, so that more accurate and reliable prediction of the water vapor content is realized, and the accuracy and timeliness of meteorological monitoring and weather forecast are greatly improved; and the requirements of precise monitoring and early warning of sudden weather are met.

The present application relates to the technical field of precipitation forecast, solves the problem that the prior art is prone to underestimate the peak value of precipitation or produce false alarm under strong convective weather, and particularly relates to a short-impending precipitation forecast method fusing GNSS and water conservancy rain measurement radar, GNSS observation data and water conservancy rain measurement radar base data in a monitoring area are acquired, pretreated, corresponding atmospheric precipitable water distribution field and radar reflectivity factor distribution field are generated, based on a preset time sliding window, time sequence characteristics of the atmospheric precipitable water distribution field and the radar reflectivity factor distribution field are extracted, and the two are superimposed in channel dimension to construct a multi-channel spatio-temporal fusion input tensor. The present application overcomes the defect that the traditional extrapolation method rapidly decays with the prolongation of the forecast time, significantly improves the prediction accuracy of the rainstorm center intensity and the falling area, reduces the missed report and underestimation of the precipitation event, and solves the problem of low prediction accuracy of the traditional linear extrapolation algorithm under strong convective weather.

The application provides a water conservancy multi-parameter monitoring method based on GNSS, comprising the following steps: 1) receiving GNSS satellite signals based on a GNSS receiving antenna and processing: extracting direct signals; extracting reflected signals; 2) monitoring water conservancy engineering displacement deformation based on the extracted direct signals; 3) monitoring water level based on the extracted reflected signals; and 4) inversing atmospheric precipitable water in real time based on GNSS troposphere delay information. The same receiving device is used for different signal processing, water conservancy engineering deformation displacement, water vapor, water level parameter information are quickly acquired and high-precision monitoring is realized, and the water conservancy monitoring sensing capability is comprehensively improved, so that scientific data support can be provided for flood control, drainage and other projects.

The invention discloses a rainfall prediction method, system and equipment based on machine learning and a medium, relates to the technical field of meteorological prediction, and aims to realize accurate prediction of rainfall through an improved Transform model by inputting PWV (atmospheric precipitable water) data, rainfall data and related meteorological parameters into a trained rainfall prediction network. The model comprises an encoder, a decoder and a final output layer, feature extraction is carried out in the encoder through a multi-head probability sparse self-attention module and a distillation module, and encoder output containing feature information serves as input of the decoder. And the decoder masks the multi-head probability sparse self-attention layer through the decoder, performs multi-head self-attention operation on the multi-head probability sparse self-attention layer and an intermediate result output by the encoder, and finally adjusts a data output dimension through a full connection layer to generate a prediction result.

The application provides a kind of heat wave influence on global navigation satellite system positioning accuracy evaluation method and system, determine the research area and research year of target heat wave event, according to the corresponding climate state of research year;Obtain the temperature data of each meteorological station in the research area, interpolate and supplement the missing data among them;According to the data after interpolation and supplement, determine the research period of heat wave;Analyze the abnormal value spatial distribution of two-meter temperature above ground and daily average soil moisture in the research period of research area;Analyze the change of abnormal value of air temperature and atmospheric precipitable water per hour, and explain the reason of atmospheric precipitable water anomaly through the corresponding relationship of factors;Analyze the abnormal value change of ionospheric total electron content and air temperature per hour;Compare the difference of global navigation satellite system positioning accuracy in heat wave period and non-heat wave period in research period.The application provides a reference for users to carry out high-precision GNSS positioning during heat wave.

The application discloses a prediction method for radiation refrigeration capacity by using aerosol optical thickness and precipitable water amount, comprising the following steps: S10, obtaining corresponding hourly weather data according to weather data; S20, judging whether the local latitude is greater than 33°, if yes, entering step S30, S30, calculating the local precipitable water amount (PWV H ) by using a high-latitude calculation method; if no, entering S40, S40, calculating the local precipitable water amount (PWV L ) by using a low-latitude calculation method; S50, calculating atmospheric radiation according to the calculated precipitable water amount (PWV H or PWV L ); S60, correcting the atmospheric radiation calculated in S50 according to the aerosol optical thickness in the weather data; and S70, calculating the radiation refrigeration capacity according to the atmospheric radiation corrected in S60.

The invention relates to the technical field of rainfall forecasting, and provides an extreme rainfall forecasting method and device. The method comprises the following steps: acquiring satellite observation data and meteorological radar data in a target area; inverting the precipitable water amount of the target area based on the satellite observation data; estimating the rainfall intensity of the target area based on the meteorological radar data; assimilating the precipitable water amount and the rainfall intensity to obtain rainfall assimilation data; inputting the rainfall assimilation data into an extreme rainfall forecast model to obtain an extreme rainfall forecast of the target area output by the extreme rainfall forecast model; the extreme rainfall forecasting model is obtained by training the historical rainfall assimilation data of the target area and the corresponding extreme rainfall forecasting label on the basis of a bidirectional long short-term memory network. According to the invention, the extreme rainfall forecast which covers a large target area and has high spatial-temporal accuracy can be output.

The embodiment of the specification discloses a satellite product verification method and system based on temporal and spatial variability of atmospheric precipitable water (TPW), and the satellite product verification method comprises the following steps: acquiring TPW observation data of a to-be-observed area; determining a temporal and spatial variability matrix of each station in the to-be-observed area based on the TPW observation data of the to-be-observed area; determining monthly temporal and spatial variability characteristics of the to-be-observed area based on the temporal and spatial variability matrix of each station in the to-be-observed area; determining a spatial matching window and a time matching window based on the monthly temporal and spatial variability characteristics of the to-be-observed area; and generating a matching data set based on the spatial matching window and the time matching window, wherein the matching data set is used for satellite TPW product verification.

The invention provides a target area potential evapotranspiration estimation method and device, and relates to the technical field of hydro meteorology, and the method comprises the steps: estimating zenith total delay based on the pressure intensity of a target area, the atmospheric precipitable water amount and the atmospheric weighted average temperature; respectively utilizing the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model to calculate a first estimated value and a second estimated value of the target area; establishing a mixed potential evapotranspiration fusion model according to the function relationship between the zenith total delay and the first estimated value and the second estimated value; resolving the mixed potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; and estimating the potential evapotranspiration of the target area by using the multi-source data potential evapotranspiration estimation model. The method not only can effectively improve the estimation precision of the potential evapotranspiration of the target region, but also can improve the applicability and universality in different target regions.

Provided is a precipitable water amount estimation system and method, a learning system and method, and a storage medium, which can observe water vapor in a local range without using liquid nitrogen for calibration. A learning system (4) of a precipitable water amount estimation model according to the present application includes: a radio wave intensity acquisition unit (40) that acquires radio wave intensities of a plurality of frequencies in radio waves received by a microwave radiometer (3); a precipitable water amount acquisition unit (41) that acquires a precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver (2); and a learning unit (43) that causes a learning model (43a) to perform machine learning in a manner in which input data based on the radio wave intensities of the plurality of frequencies is input and the precipitable water amount is output, based on the radio wave intensities of the plurality of frequencies and the precipitable water amount at a plurality of time points in a prescribed period.

The embodiment of the invention provides an atmospheric precipitable water (PWV) time sequence generation method and device, and is applied to the technical field of meteorological data processing. The method comprises the following steps: acquiring an initial PWV data set; according to a preset quality inspection rule, data which does not conform to the quality inspection rule in the initial PWV data set is removed to obtain a first PWV data set, and according to a pre-constructed homogenization processing model, homogenization processing is performed on the first PWV data set to obtain a second PWV data set; and according to a timestamp in the second PWV data set, constructing a PWV time sequence of the second PWV data set. In this way, a high-quality PWV time sequence can be generated, and more scientific data support is provided for extreme rainfall forecast and early warning, Chinese region reanalysis data construction, climate change monitoring, artificial influence weather, satellite water vapor observation product verification and the like.

The invention discloses a time-space intelligent area rainfall qualitative forecasting method based on Beidou water vapor. The method comprises the following steps: 1, acquiring atmospheric precipitable water and meteorological historical data inverted by Beidou; 2, constructing and training a BPNN rainfall model; 3, obtaining a rainfall-free threshold optimization value based on the trained BPNN rainfall model; 4, obtaining a rainfall grade threshold optimization value based on the trained BPNN rainfall model; and 5, qualitatively forecasting subsequent rainfall and rainfall grades. The method is simple in steps and reasonable in design, firstly, the BPNN rainfall model is trained through the atmosphere precipitable water amount and meteorological historical data inverted by Beidou, the rainfall-free threshold value optimization value and the rainfall grade threshold value optimization value are obtained through the trained BPNN rainfall model, intelligentization of subsequent area rainfall qualitative forecasting is achieved, rainfall qualitative forecasting in future hours is achieved, and the method is suitable for large-scale popularization and application. And important data support is provided for water conservancy disaster monitoring and water resource management.

The application relates to an atmospheric precipitable water estimation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring observation data of a plurality of global navigation satellite system stations, first atmospheric precipitable water of a plurality of European Centre for Medium-Range Weather Forecasts grid positions, sounding atmospheric precipitable water and position information of a to-be-measured position in a to-be-measured area; according to the observation data of the plurality of stations, second atmospheric precipitable water and precipitation uncertainty of the corresponding stations are solved; based on the first atmospheric precipitable water, the sounding atmospheric precipitable water, the second atmospheric precipitable water, the precipitation uncertainty and fitting parameters, a target function is determined, and the fitting parameters corresponding to the function value convergence of the target function are taken as target fitting parameters; and based on the target fitting parameters and the position information of the to-be-measured position, the atmospheric precipitable water at the to-be-measured position is determined. The method can improve the atmospheric precipitable water estimation result acquisition efficiency.

The present invention provides a method for estimating the precipitable water in cloud and sky atmosphere based on meteorological satellites, belonging to the technical field of meteorological satellite detection. Cloud detection is carried out using the relative humidity threshold method based on the atmospheric sounding profile, and the radiative simulation calculations of the near-infrared and thermal-infrared water vapor channels of meteorological satellites in the case of cloud and sky are carried out using the atmospheric radiative transfer model MODTRAN. The near-infrared and thermal-infrared water vapor channels can detect the precipitable water in the atmosphere above the cloud, and the numerical prediction model can provide the temperature, humidity profile distribution of the whole atmosphere and the total precipitable water in the atmosphere. It is assumed that the form of the atmospheric profile in the numerical prediction is correct, but bias correction is required. Taking the radiation of the near-infrared and thermal-infrared water vapor channels and the temperature and humidity profiles predicted by the model as inputs, a convolutional neural network is used to construct an estimation model for the precipitable water in cloud and sky atmosphere. The present invention uses multi-band observations and numerical prediction model information, takes into account the atmospheric radiative transfer process, and obtains a relatively high accuracy of the precipitable water in cloud and sky atmosphere.

The invention relates to the technical field of precipitation analysis, and discloses a basin precipitation space-time evolution characteristic and development trend analysis method, which comprises the following steps of: selecting precipitation data and foundation laser radar water vapor data of a plurality of ground meteorological stations in a target basin, and performing monthly correlation analysis on the precipitation data and precipitation data parameters respectively; and analyzing the variation trend of the four-season precipitable water in the recent N years along with the years, analyzing the evolution rule of the four-season precipitable water in the recent N years along with the years by utilizing a Mangkendall test method, and analyzing the evolution trend of the precipitable water in the recent N years by utilizing an empirical orthogonal function analysis method. According to the invention, through analyzing the annual, latitude and longitude and seasonal spatial-temporal distribution of the precipitable water amount of the target drainage basin in recent 20 years, the spatial-temporal distribution evolution characteristics of the precipitable water amount of the target drainage basin in recent 20 years and the long-time sequence development change trend of the target drainage basin can be obtained; therefore, important reference can be provided for the climate and natural disaster early warning of the target drainage basin.

The present invention provides a method and device for calculating potential evapotranspiration parameters of a target area, belonging to the technical field of data processing. In order to address the bottlenecks such as low areal accuracy and spatial resolution existing in the existing measured method and model method, it includes: calculating a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; calculating the difference between the first estimated value and the second estimated value; establishing a multi-source potential evapotranspiration initial model according to the functional relationship between the difference and temperature and atmospheric precipitable water; performing model solution on the multi-source potential evapotranspiration initial model to obtain a multi-source potential evapotranspiration calculation model; obtaining an areal potential evapotranspiration estimation model according to the multi-source potential evapotranspiration calculation model and the first potential evapotranspiration calculation model; and estimating the potential evapotranspiration parameters of the target area by using the areal potential evapotranspiration estimation model. The present invention can more accurately evaluate the evapotranspiration situation of water in the target area.

The embodiment of the invention discloses a training method, a use method and a related device of a high-resolution weather forecast model. The training method comprises the following steps: acquiring a pre-collected training sample data set; inputting the first forecast data of the low-resolution rainfall forecast in the training sample data set, the atmospheric precipitable water amount data obtained by the Beidou data inversion and the surface meteorological observation data into a preset deep learning model for high-resolution weather forecast model training to obtain second forecast data of the high-resolution weather forecast; determining whether the deep learning model converges or not by using the second forecast data, a first label in the training sample data set and a preset loss function; and if so, taking the converged deep learning model as a high-resolution weather forecast model. Through the above mode, high-resolution weather forecast based on deep learning and fusing Beidou and meteorological data is realized, the forecast precision is improved, and the accuracy of short temporary rainfall forecast is improved.