CN116822185A - Daily precipitation data space simulation method and system based on HASM - Google Patents
Daily precipitation data space simulation method and system based on HASM Download PDFInfo
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Abstract
The application relates to the technical field of computer-aided simulation, and provides a daily precipitation data space simulation method and system based on HASM. According to the method, daily precipitation observation data corresponding to a plurality of observation stations in a target area are firstly obtained, precipitation probability corresponding to each observation station in the target area is generated according to the daily precipitation observation data, precipitation probability data are obtained, then space simulation is carried out on the daily precipitation observation data and the precipitation probability data respectively by using a HASM method, a precipitation value curved surface and a precipitation probability curved surface of the target area are correspondingly obtained, and multiplication processing is carried out on the precipitation value curved surface and the precipitation probability curved surface, so that a daily precipitation data space simulation result is obtained. According to the method, the space simulation is carried out on the precipitation amount and the precipitation probability, then the curved surface synthesis is carried out, the influence of the precipitation probability on daily scale precipitation simulation is fully considered, meanwhile, the high-precision advantage of HASM is fully utilized, and the high-precision and high-accuracy simulation of daily precipitation data is realized.
Description
Technical Field
The application relates to the technical field of computer-aided simulation, in particular to a daily precipitation data space simulation method and system based on HASM.
Background
In order to fully recognize the occurrence rule of extreme climate events and establish an effective countermeasure, the change rule of the historical climate must be clarified, and the first scientific problem is how to accurately simulate the historical climate, in particular to the time-space change rule of precipitation.
The simulation of the law of the spatio-temporal variation of precipitation comprises various time scales, such as the annual, lunar and daily scales. Unlike the curved surface modeling process of annual-scale and monthly-scale precipitation data, the time resolution of daily-scale precipitation is higher, and curved surface modeling is more complex and difficult, and not only needs to consider the simulation of precipitation amount, but also the precipitation possibility (i.e., precipitation probability) of a target area in a simulation time period has a great influence on the accuracy of a simulation result. A high-precision curved surface modeling (High Accuracy Surface Modeling, hereinafter abbreviated as HASM) method is an ecological environment element space simulation method originally created by scholars in China, and the technology solves the error problem and the multi-scale problem which plague the curved surface modeling process for half a century. Currently, the HASM is widely applied to curved surface simulation processes of ecological environment elements, such as digital ground model construction, climate change analysis, population space distribution, soil attribute space distribution, ecological system space distribution, forest carbon reserve simulation and the like, and has become an important mathematical method for surface element simulation.
At present, although the HASM method is applied to simulation of annual-scale and monthly-scale precipitation data, the conventional method does not relate to daily-scale simulation, and as the conventional method generally only simulates annual-scale and monthly-scale precipitation, complexity of daily-scale precipitation is not considered, influence of precipitation probability on daily-scale precipitation is not considered, and certain error still exists when the HASM method is applied to daily-scale precipitation, and the requirement of practical application cannot be met.
Accordingly, there is a need to provide an improved solution to the above-mentioned deficiencies of the prior art.
Disclosure of Invention
The application aims to provide a daily precipitation data space simulation method and system based on HASM, which are used for solving or relieving the problems in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a daily precipitation data space simulation method based on HASM, which comprises the following steps:
acquiring daily precipitation observation data corresponding to a plurality of observation sites in a target area; the daily precipitation observed data corresponding to each observation station point represents a daily precipitation observed value of the observation station;
generating precipitation probability data of the target area according to the daily precipitation observation data;
respectively carrying out space simulation on the daily precipitation observation data and the precipitation probability data by using a HASM (transient phase change memory), and correspondingly obtaining a precipitation value curved surface of a target area and a precipitation probability curved surface of the target area;
and multiplying the rainfall value curved surface of the target area and the rainfall probability curved surface of the target area to obtain a daily rainfall data space simulation result.
Preferably, the spatial simulation is performed on the daily precipitation observation data by using a HASM to obtain a precipitation value curved surface of a target area, including:
calculating the average precipitation value of the target area according to the daily precipitation observation data;
generating a first driving field of the HASM based on the average precipitation value of the target area, the range of the target area and the preset HASM target resolution;
and taking the daily precipitation observation data as an optimized control field of the HASM, and combining the first driving field to perform space simulation by using a HASM method so as to obtain a precipitation value curved surface of the target area.
Preferably, the spatial simulation is performed on the precipitation probability data by using a HASM to obtain a precipitation probability curved surface of the target area, including:
calculating the average precipitation probability of the target area according to the precipitation probability data;
generating a second driving field of the HASM based on the average precipitation probability of the target area, the range of the target area and the preset HASM target resolution;
and taking the precipitation probability data as an optimized control field of the HASM, and combining the second driving field to perform space simulation by using a HASM method so as to obtain a precipitation probability curved surface of the target area.
Preferably, generating precipitation probability data of the target area according to the daily precipitation observation data includes:
performing binarization processing on the daily precipitation observation data to obtain a binarization processing result of the daily precipitation observation data;
and converting the binarization processing result of the daily precipitation observation data into precipitation probability data of the target area.
Preferably, the binarizing processing is performed on the daily precipitation observed data to obtain a binarizing processing result of the daily precipitation observed data, including:
and judging whether the precipitation observed data corresponding to each observation station is larger than a preset precipitation threshold, if so, assigning 1, otherwise, assigning 0, and obtaining a binarization processing result of the daily precipitation observed data.
Preferably, the binarization processing result of the daily precipitation observation data is converted into precipitation probability data of the target area, specifically:
and judging each value of the binarization processing result of the daily precipitation observation data, converting into 100% if the value is 1, and converting into 0% if the value is 0, so as to obtain precipitation probability data of the target area.
Preferably, before multiplying the precipitation value curved surface of the target area and the precipitation probability curved surface of the target area to obtain the daily precipitation data space simulation result, the method further comprises:
extracting simulation values of precipitation probability curved surfaces at the observation station points according to the positions of a plurality of observation stations in the target area;
comparing the simulation values of the precipitation probability curved surfaces at the observation station points with the corresponding daily precipitation observation data to determine an optimal prediction probability threshold;
and determining a rainfall probability curved surface of the target area according to the optimal prediction probability threshold.
Preferably, comparing the simulated value of the precipitation probability curved surface at each observation station point with the corresponding daily precipitation observation data to determine an optimal prediction probability threshold value includes:
taking a preset precipitation probability interval as an iteration step length, and iteratively executing the following steps:
determining a precipitation probability threshold value in the current step according to the initial precipitation probability threshold value and the precipitation probability interval;
performing binarization processing on the simulation values of the rainfall probability curved surfaces at the observation station points according to the rainfall probability threshold value in the current step to obtain binary sequences corresponding to the simulation values of the rainfall probability curved surfaces at the observation station points;
comparing the binary sequence corresponding to the simulation value of the rainfall probability curved surface at each observation station point with the daily rainfall observation data corresponding to each observation station point, and calculating the rainfall probability prediction accuracy;
and taking the corresponding precipitation probability threshold value when the precipitation probability prediction accuracy rate is highest as the optimal prediction probability threshold value.
Preferably, before generating precipitation probability data of the target area according to the daily precipitation observation data, the method further comprises:
and checking the daily precipitation observation data from the observation sites one by one, taking the observation site as an invalid observation site if the daily precipitation observation data of the observation site is lack of measurement, and deleting the daily precipitation observation data of the observation site from the daily precipitation observation data.
The embodiment of the application provides a daily precipitation data space simulation system based on HASM, which comprises the following steps:
the acquisition unit is configured to acquire daily precipitation observation data corresponding to a plurality of observation stations in the target area; the daily precipitation observation data represents a daily precipitation observation value of the observation site;
a generation unit configured to generate precipitation probability data of the target area according to the daily precipitation observation data;
the simulation unit is configured to input the daily precipitation observation data and the precipitation probability data into the HASM respectively for space simulation, and correspondingly obtain a precipitation value curved surface of the target area and a precipitation probability curved surface of the target area;
and the curved surface comprehensive unit is configured to multiply the rainfall value curved surface of the target area and the rainfall probability curved surface of the target area to obtain a daily rainfall data space simulation result.
The beneficial effects are that:
according to the technical scheme, daily precipitation observation data corresponding to a plurality of observation stations in a target area are firstly obtained, precipitation probability corresponding to each observation station in the target area is generated according to the daily precipitation observation data, precipitation probability data are obtained, then space simulation is carried out on the daily precipitation observation data and the precipitation probability data respectively by using a HASM method, a precipitation value curved surface and a precipitation probability curved surface of the target area are correspondingly obtained, and multiplication processing is carried out on the precipitation value curved surface and the precipitation probability curved surface, so that a daily precipitation data space simulation result is obtained. According to the method, the space simulation is carried out on the precipitation amount and the precipitation probability, then the curved surface synthesis is carried out, the influence of the precipitation probability on daily scale precipitation simulation is fully considered, meanwhile, the high-precision advantage of HASM is fully utilized, and the high-precision and high-accuracy simulation of daily precipitation data is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. Wherein:
FIG. 1 is a technical logic diagram of a method for spatial simulation of solar precipitation data based on HASM, according to some embodiments of the present application;
FIG. 2 is a flow chart of a method for spatial simulation of solar precipitation data based on HASM according to some embodiments of the present application;
fig. 3 is a schematic structural diagram of a HASM-based solar precipitation data space simulation system according to some embodiments of the present application.
Detailed Description
The application will be described in detail below with reference to the drawings in connection with embodiments. The examples are provided by way of explanation of the application and not limitation of the application. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment. Accordingly, it is intended that the present application encompass such modifications and variations as fall within the scope of the appended claims and their equivalents.
In the following description, the terms "first/second/third" are used merely to distinguish between similar objects and do not represent a particular ordering of the objects, it being understood that the "first/second/third" may be interchanged with a particular order or precedence where allowed, to enable embodiments of the application described herein to be implemented in other than those illustrated or described herein.
Exemplary method
The embodiment of the application provides a daily precipitation data space simulation method based on HASM, as shown in figures 1 and 2, comprising the following steps:
step S101, daily precipitation observation data corresponding to a plurality of observation sites in a target area are obtained.
The daily scale refers to a scale or a period in which observation, analysis, and representation of precipitation are performed in units of one day in time.
Space simulation (Spatial simulation) refers to the process of modeling and predicting geospatial phenomena and variables by building simulation models and using computer technology, which is an important component of Geographic Information Systems (GIS), geographic modeling, and spatial analysis, provides a way to understand geographic phenomena, and helps decision makers make decisions and plans based on spatial analysis.
In this embodiment, the spatial simulation of the target area is based on HASM, where the target area may be a study area of any shape, size, or geographic extent.
An observation site, also referred to as a weather observation site, refers to a location for collecting and recording weather data. These sites are typically established and operated by the weather department, research institution or other related organization. Types of weather observation sites include: an meteorological station, a meteorological balloon observation station, a meteorological radar station, a meteorological satellite receiving station and an meteorological marine observation station. Among them, weather stands are specialized weather observation and forecast facilities established by the weather bureau or the weather department, and are usually located in the central location of a city or region. Weather balloon observation stations are stations for launching weather balloons to acquire observations of the high-altitude atmosphere, and are typically located in relatively open areas, such as plain or rural areas. Weather radar stations are stations for detecting precipitation, measuring the intensity of precipitation, and tracking weather systems, and radar stations are typically located in high altitudes or mountainous areas to obtain a wider coverage area. A meteorological satellite receiving station is a station for receiving, processing and analysing data transmitted by meteorological satellites, which stations are typically located in areas remote from urban light pollution to ensure that clear satellite images are received. A meteorological marine observation station is a buoy or platform located on the ocean for collecting marine meteorological data such as ocean surface temperature, ocean wind speed, wave height, and the like.
Meteorological site observation is the primary way to obtain certain earth precipitation data currently. Taking an weather station as an example, the weather station can measure and record the precipitation amount of the day every day, and usually the daily precipitation data acquired by the weather station is the precipitation amount data recorded every day at the observation station by taking millimeter (mm) as a unit. That is, daily precipitation observations corresponding to each observation site characterize daily precipitation observations of the observation site. The daily rainfall observation data has the advantages that the observation precision is high, the time continuous observation data can be obtained, but the disadvantage that the meteorological site observation mode belongs to sparse observation, is often discontinuous in space, and cannot meet the requirements of the fields of agriculture, hydrology, emergency management and the like on the space continuous data, so scientists often generate the space continuous rainfall data from the discrete point observation data by using a curved surface modeling method.
Step S102, generating precipitation probability data of a target area according to daily precipitation observation data.
The precipitation probability data are used for representing precipitation probability of each observation site in the target area of the designated time.
In this embodiment, there are various ways to generate precipitation probability data of the target area according to daily precipitation observation data, for example, by statistical analysis, or by probability distribution function fitting, or by numerical weather forecast model.
In particular, statistical methods calculate the frequency of precipitation events occurring within each time period (e.g., daily, monthly, or yearly) by performing statistical analysis using historical precipitation data. According to past observations, the number of precipitation events can be divided by the total number of observations to obtain the precipitation probability for that time period. The probability distribution function fitting is to fit existing precipitation data to a proper probability distribution function (such as normal distribution, exponential distribution or gamma distribution), and then estimate parameters of the fitting function to calculate precipitation thresholds corresponding to different probability levels, for example, calculate the probability that precipitation exceeds a certain threshold, so as to obtain precipitation probability data. The numerical weather forecast model is output by utilizing the numerical weather forecast model, wherein the numerical weather forecast model comprises rainfall forecast in a future time period, the model can simulate and forecast the occurrence probability of rainfall based on an atmospheric physical equation and a statistical method, and rainfall probability data can be obtained by analyzing the output of the model.
And step S103, respectively performing space simulation on the daily precipitation observation data and the precipitation probability data by using the HASM, and correspondingly obtaining a precipitation value curved surface of the target area and a precipitation probability curved surface of the target area.
As described in the background art, the HASM method is a method for modeling an arbitrary geographic area of the earth surface, which builds a high-precision curved surface modeling model with global approximation data (including remote sensing data and global model coarse resolution simulation data) as a driving field and local high-precision data (including monitoring network data and survey sampling data) as an optimization control condition based on a differential geometry principle and an optimization control theory, and refines and forms an earth surface modeling basic law based on a large number of application researches, so as to obtain a high-precision spatial simulation result.
According to the embodiment of the application, the HASM is utilized to perform space simulation on daily precipitation observation data and precipitation probability data respectively, so that the obtained precipitation value curved surface and precipitation probability curved surface have the characteristics of high precision and space continuity, and a foundation is laid for obtaining high-precision daily scale precipitation data.
And step S104, multiplying the precipitation value curved surface of the target area and the precipitation probability curved surface of the target area to obtain a daily precipitation data space simulation result.
The traditional daily scale precipitation data simulation generally only considers the simulation of precipitation amount, ignores the influence of precipitation probability on precipitation distribution, causes the simulation result to possibly appear negative values, null values and the like, and seriously influences the simulation precision and the actual use effect of the simulation result. In the embodiment, on the basis of daily precipitation record of a meteorological site, a precipitation value curved surface of a target area and a precipitation probability curved surface of the target area are respectively generated based on a HASM method, then the precipitation value curved surface of the target area and the precipitation probability curved surface of the target area are synthesized to obtain a daily precipitation data space simulation result, namely, daily scale obtains space continuous daily precipitation grid data, so that the accuracy of the daily scale precipitation data simulation result is greatly improved, the simulation result is more fit with real precipitation space distribution, and a foundation is laid for effective application of ecological environment informatics.
The acquisition of solar precipitation raster data is a key point and difficulty in ecological environment informatics, and no effective and high-precision simulation method exists in the current industry. Unlike the surface modeling process of annual and monthly scale precipitation data, the surface modeling of daily scale precipitation is more complex and difficult, because the surface modeling of daily scale precipitation is essentially a comprehensive task, not only to simulate the magnitude of precipitation values, but also whether precipitation exists. In order to obtain solar precipitation raster data with high precision and high accuracy, the embodiment is based on a high-precision curved surface modeling method (HASM), and the precipitation probability and the precipitation value are respectively simulated, so that a precipitation probability curved surface and a precipitation value curved surface are correspondingly obtained, the high-precision advantage of the HASM is fully utilized, and the high-precision and high-accuracy simulation of the solar precipitation data is realized.
In some embodiments, the spatial simulation of daily precipitation observation data by using the HASM is performed to obtain a precipitation value curved surface of the target area, including: according to the daily precipitation observation data, calculating an average precipitation value of the target area; generating a first driving field of the HASM based on the average precipitation value of the target area, the range of the target area and the preset HASM target resolution; and taking the daily precipitation observation data as an optimized control field of the HASM, combining the first driving field, and performing space simulation by using the HASM method to obtain a precipitation value curved surface of the target area.
Referring to fig. 1, in an embodiment of the present application, input parameters of the HASM method include: the driving field (also called as HASM initial field) and the optimized control field are usually generated by other methods in the traditional simulation process, so that on one hand, the workload of data processing is increased, and on the other hand, the scale difference can be caused by different data generation principles of different methods. In this embodiment, the average precipitation value of the target area is calculated by using daily precipitation observation data, so that on one hand, the calculation amount is reduced, and on the other hand, the generated driving field can be ensured to be consistent with other input data of the model, thereby further improving the simulation precision.
Specifically, dividing daily precipitation observation data by data of an observation site to obtain an average precipitation value of a target area, and then generating a first driving field of the HASM according to the current area range and the target resolution, wherein the first driving field is denoted by P1. It should be noted that P1 is a curved surface formed by constant grids, the size of each grid is determined by the target resolution, and the value of each grid is an average precipitation value. Then, P1 is used as a driving field of the HASM, daily rainfall observation data is used as an optimal control field, and the HASM method is used for simulation to obtain a rainfall value curved surface of the target area, wherein the rainfall value curved surface is represented by R1.
In some embodiments, the spatial simulation of the precipitation probability data using the HASM to obtain a precipitation probability surface for the target area includes: according to the precipitation probability data, calculating the average precipitation probability of the target area; generating a second driving field of the HASM based on the average precipitation probability of the target area, the range of the target area and the preset HASM target resolution; and taking the precipitation probability data as an optimized control field of the HASM, and combining a second driving field, and performing space simulation by using the HASM method to obtain a precipitation probability curved surface of the target area.
The embodiment realizes the space simulation of the rainfall probability curved surface. Specifically, the precipitation probability data is divided by the number of observation sites to average them to obtain the average precipitation probability of the area, and then a second driving field of the HASM is generated according to the range of the target area and the target resolution, and the second driving field is denoted by P2. It will be appreciated that the second driving field of the HASM is also a curved surface consisting of a constant grid of values of average precipitation probability. Then, P2 is used as a driving field of the HASM, precipitation probability observation data is used as an optimal control field, and the HASM method is used for simulation to obtain a precipitation probability curved surface R2 of the target area.
In some embodiments, generating precipitation probability data for the target area from daily precipitation observations comprises: performing binarization processing on the daily precipitation observation data to obtain a binarization processing result of the daily precipitation observation data; and converting the binarization processing result of the daily precipitation observation data into precipitation probability data of the target area.
The method for binarizing solar precipitation observation data comprises the steps of: judging whether the precipitation observation data corresponding to each observation station is larger than a preset precipitation threshold value, if so, assigning 1, otherwise, assigning 0, and obtaining a binarization processing result of daily precipitation observation data.
Further, converting the binarization processing result of the daily precipitation observation data into precipitation probability data of the target area, specifically: and judging each value of the binarization processing result of the daily precipitation observation data, converting into 100% if the value is 1, and converting into 0% if the value is 0, so as to obtain precipitation probability data of the target area.
Based on the foregoing, the precipitation probability data may be obtained in a variety of ways, however, the existing methods for calculating the precipitation probability data are complex. In order to solve the problems that the calculated precipitation probability data is large and cannot be obtained quickly, in the embodiment, by combining the daily precipitation observation data with a preset precipitation threshold value, quick and massive generation of precipitation probability data is realized.
Wherein the preset precipitation threshold is preferably 0.1mm. Generating precipitation probability data by precipitation value observation data, specifically: binarizing daily precipitation observation data, recording as 1 if precipitation exists in a certain observation station on the same day (for example, precipitation amount is larger than 0.1 mm), generating new observation data with values of 0 and 1 only if no precipitation exists as 0, and converting the value of the observation data with the value of 1 into 100%, and converting the value of the observation data with the value of 0 into 0%.
In some embodiments, before multiplying the precipitation value curved surface of the target area and the precipitation probability curved surface of the target area to obtain the solar precipitation data space simulation result, the method further includes: according to the positions of a plurality of observation sites in the target area, extracting simulation values of precipitation probability curved surfaces at the positions of the observation sites; comparing the simulation values of the rainfall probability curved surfaces at the positions of the observation sites with the corresponding daily rainfall observation data to determine an optimal prediction probability threshold; and determining a precipitation probability curved surface of the target area according to the optimal prediction probability threshold.
It should be understood that the precipitation probability curved surface is formed by a group of grids obtained after the target area is split, the resolution of each grid is set to be the target resolution of the HASM, the value of each grid represents the precipitation probability of the area covered by the grid in the simulation, the value range of the precipitation probability is [0%,100% ], then, for all grids forming the precipitation probability curved surface, one precipitation probability threshold value must exist, so that the precipitation probability curved surface can be matched with the daily precipitation observed data corresponding to actual measurement to the greatest extent, that is, an optimal prediction probability threshold value exists, and the precipitation probability can accurately predict the precipitation condition reflected by the daily precipitation observed data to the greatest extent.
In order to determine the optimal prediction probability threshold, further, in this embodiment, comparing the simulation value of the precipitation probability curved surface at each observation site position with the corresponding daily precipitation observation data to determine the optimal prediction probability threshold includes: taking a preset precipitation probability interval as an iteration step length, and iteratively executing the following steps: determining a precipitation probability threshold value in the current step according to the initial precipitation probability threshold value and the precipitation probability interval; according to the precipitation probability threshold value in the current step, binarizing the simulation values of the precipitation probability curved surfaces at the positions of all the observation sites to obtain binary sequences corresponding to the simulation values of the precipitation probability curved surfaces at the positions of all the observation sites; comparing the binary sequence corresponding to the simulation value of the rainfall probability curved surface at each observation site position with daily rainfall observation data corresponding to each observation site, and calculating the rainfall probability prediction accuracy; and taking the corresponding precipitation probability threshold value when the precipitation probability prediction accuracy rate is highest as the optimal prediction probability threshold value.
Wherein the preset precipitation probability interval may be 0.5%, 1%, 2% …, preferably the preset precipitation probability interval is set to 1%. That is, the optimal prediction probability threshold is iteratively solved with 1% as the iteration step.
Specifically, an initial precipitation probability threshold value, for example, an initial precipitation probability threshold value is 1%, and binarization processing is performed on the simulation values of the precipitation probability curved surfaces at the positions of each observation site, that is, grid values with values (i.e., simulation values) greater than or equal to 1% on the precipitation probability curved surfaces are set as 1, grid values with values less than 1% are set as 0, so as to obtain binary sequences corresponding to the simulation values of the precipitation probability curved surfaces at the positions of each observation site. Then, comparing the binary sequence corresponding to the simulation value of the rainfall probability curved surface at each observation site position with daily rainfall observation data corresponding to each observation site, and calculating the correct observation site occupation ratio of rainfall probability prediction, so as to obtain the rainfall probability prediction correct rate corresponding to the initial rainfall probability threshold; then, increasing iteration step length on the basis of an initial precipitation probability threshold value, namely increasing iteration step length by 1% on the basis of the initial precipitation probability threshold value to obtain 2%, taking 2% as a new precipitation probability threshold value under the current iteration step, and performing binarization processing on the simulation value of the precipitation probability curved surface at each observation site position, namely setting the value of a grid with the value of more than or equal to 2% on the precipitation probability curved surface as 1, setting the value of a grid with the value of less than 2% as 0, and calculating the correct observation site occupation ratio of precipitation probability prediction again to obtain precipitation probability prediction correct rate with the precipitation probability threshold value of 2%; and then sequentially carrying out the same operation on the precipitation probability threshold values of 3% and 4% … …% to obtain precipitation probability prediction correct rates corresponding to the precipitation probability threshold values, and selecting the precipitation probability threshold value corresponding to the precipitation probability prediction correct rate with the highest precipitation probability prediction correct rate as a final threshold value, namely the optimal prediction probability threshold value. And finally, carrying out binarization processing on the rainfall probability curved surface according to the optimal prediction probability threshold value, namely setting the value of a grid larger than or equal to the optimal prediction probability threshold value on the rainfall probability curved surface as 1, setting the value of a grid smaller than the optimal prediction probability threshold value on the rainfall probability curved surface as 0, and obtaining the final rainfall probability curved surface of the target area, wherein the final rainfall probability curved surface is the rainfall probability binarization curved surface, as shown in fig. 1. At the moment, the binary curved surface and the rainfall value curved surface of the target area are subjected to curved surface synthesis through multiplication processing, and a solar rainfall data space simulation result is obtained.
It should be appreciated that in practice, not all of the precipitation data at the observation site is valid data, and for this purpose, in some embodiments, prior to generating precipitation probability data for the target area from daily precipitation observations, further comprises: and checking daily precipitation observation data by observing the sites one by one, and if the daily precipitation observation data of the observing sites are lack of measurement, taking the observing sites as invalid observing sites and deleting the daily precipitation observation data of the observing sites from the daily precipitation observation data.
The main purpose of this embodiment is to preprocess daily precipitation observation data of an observation site, specifically, preprocess daily precipitation observation data of an observation site on a certain day in a target area after collecting the daily precipitation observation data, and if the observation site is lack of measurement, consider the observation site as an invalid site, and delete the daily precipitation observation data of the observation site, so as to ensure the data quality of input HASM, and further improve the simulation precision.
In summary, in order to obtain daily precipitation grid data with high precision and high accuracy, the embodiment of the application provides a daily precipitation data space simulation method based on HASM.
Exemplary System
The embodiment of the application provides a daily precipitation data space simulation system based on HASM, as shown in figure 3, which comprises: an acquisition unit 301, a generation unit 302, a simulation unit 303, and a curved surface synthesis unit 304. Wherein:
an acquiring unit 301 configured to acquire daily precipitation observation data corresponding to a plurality of observation sites in a target area; the daily precipitation observation data characterizes daily precipitation observations of the observation site.
A generating unit 302 configured to generate precipitation probability data of the target area based on the daily precipitation observation data.
The simulation unit 303 is configured to input daily precipitation observation data and precipitation probability data into the HASM respectively for space simulation, and correspondingly obtain a precipitation value curved surface of the target area and a precipitation probability curved surface of the target area.
And the curved surface synthesis unit 304 is configured to multiply the rainfall value curved surface of the target area with the rainfall probability curved surface of the target area to obtain a daily rainfall data space simulation result.
The daily precipitation data space simulation system based on the HASM provided by the embodiment of the application can realize the steps and the flow of the daily precipitation data space simulation method based on the HASM provided by any embodiment, and achieve the same technical effects, and are not described in detail herein.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. The daily precipitation data space simulation method based on HASM is characterized by comprising the following steps of:
acquiring daily precipitation observation data corresponding to a plurality of observation sites in a target area; the daily precipitation observed data corresponding to each observation station point represents a daily precipitation observed value of the observation station;
generating precipitation probability data of the target area according to the daily precipitation observation data;
respectively carrying out space simulation on the daily precipitation observation data and the precipitation probability data by using a HASM (transient phase change memory), and correspondingly obtaining a precipitation value curved surface of a target area and a precipitation probability curved surface of the target area;
and multiplying the rainfall value curved surface of the target area and the rainfall probability curved surface of the target area to obtain a daily rainfall data space simulation result.
2. The method of claim 1, wherein spatially modeling the daily precipitation observation data using a HASM to obtain a precipitation value surface for a target area, comprising:
calculating the average precipitation value of the target area according to the daily precipitation observation data;
generating a first driving field of the HASM based on the average precipitation value of the target area, the range of the target area and the preset HASM target resolution;
and taking the daily precipitation observation data as an optimized control field of the HASM, and combining the first driving field to perform space simulation by using a HASM method so as to obtain a precipitation value curved surface of the target area.
3. The method of claim 1, wherein spatially modeling the precipitation probability data using a HASM to obtain a precipitation probability surface for the target area, comprising:
calculating the average precipitation probability of the target area according to the precipitation probability data;
generating a second driving field of the HASM based on the average precipitation probability of the target area, the range of the target area and the preset HASM target resolution;
and taking the precipitation probability data as an optimized control field of the HASM, and combining the second driving field to perform space simulation by using a HASM method so as to obtain a precipitation probability curved surface of the target area.
4. The method of claim 1, wherein generating precipitation probability data for the target area from the daily precipitation observations comprises:
performing binarization processing on the daily precipitation observation data to obtain a binarization processing result of the daily precipitation observation data;
and converting the binarization processing result of the daily precipitation observation data into precipitation probability data of the target area.
5. The method according to claim 4, wherein binarizing the daily precipitation observation data to obtain a binarized result of the daily precipitation observation data, comprises:
and judging whether the precipitation observed data corresponding to each observation station is larger than a preset precipitation threshold, if so, assigning 1, otherwise, assigning 0, and obtaining a binarization processing result of the daily precipitation observed data.
6. The method according to claim 4, wherein the binarization processing result of the daily precipitation observation data is converted into precipitation probability data of the target area, specifically:
and judging each value of the binarization processing result of the daily precipitation observation data, converting into 100% if the value is 1, and converting into 0% if the value is 0, so as to obtain precipitation probability data of the target area.
7. The method according to claim 1, wherein before multiplying the precipitation value curved surface of the target area and the precipitation probability curved surface of the target area to obtain the solar precipitation data space simulation result, the method further comprises:
extracting simulation values of precipitation probability curved surfaces at the observation station points according to the positions of a plurality of observation stations in the target area;
comparing the simulation values of the precipitation probability curved surfaces at the observation station points with the corresponding daily precipitation observation data to determine an optimal prediction probability threshold;
and determining a rainfall probability curved surface of the target area according to the optimal prediction probability threshold.
8. The method of claim 7, wherein comparing the simulated values of the precipitation probability surface at each of the observation sites to the corresponding daily precipitation observations to determine an optimal prediction probability threshold comprises:
taking a preset precipitation probability interval as an iteration step length, and iteratively executing the following steps:
determining a precipitation probability threshold value in the current step according to the initial precipitation probability threshold value and the precipitation probability interval;
performing binarization processing on the simulation values of the rainfall probability curved surfaces at the observation station points according to the rainfall probability threshold value in the current step to obtain binary sequences corresponding to the simulation values of the rainfall probability curved surfaces at the observation station points;
comparing the binary sequence corresponding to the simulation value of the rainfall probability curved surface at each observation station point with the daily rainfall observation data corresponding to each observation station point, and calculating the rainfall probability prediction accuracy;
and taking the corresponding precipitation probability threshold value when the precipitation probability prediction accuracy rate is highest as the optimal prediction probability threshold value.
9. The method according to any one of claims 1 to 8, further comprising, prior to generating precipitation probability data for the target area from the daily precipitation observations:
and checking the daily precipitation observation data from the observation sites one by one, taking the observation site as an invalid observation site if the daily precipitation observation data of the observation site is lack of measurement, and deleting the daily precipitation observation data of the observation site from the daily precipitation observation data.
10. Daily precipitation data space simulation system based on HASM, characterized by comprising:
the acquisition unit is configured to acquire daily precipitation observation data corresponding to a plurality of observation stations in the target area; the daily precipitation observation data represents a daily precipitation observation value of the observation site;
a generation unit configured to generate precipitation probability data of the target area according to the daily precipitation observation data;
the simulation unit is configured to input the daily precipitation observation data and the precipitation probability data into the HASM respectively for space simulation, and correspondingly obtain a precipitation value curved surface of the target area and a precipitation probability curved surface of the target area;
and the curved surface comprehensive unit is configured to multiply the rainfall value curved surface of the target area and the rainfall probability curved surface of the target area to obtain a daily rainfall data space simulation result.
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