CN117970529B - Mode forecasting method for correcting subsurface parameters of ground by regional automatic station - Google Patents
Mode forecasting method for correcting subsurface parameters of ground by regional automatic station Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a mode forecasting method for correcting subsurface parameters by an area automatic station, which comprises the following steps: obtaining first prediction temperature and humidity field data according to the soil temperature and humidity field data, obtaining abnormal data and normal data in the surface heat flux data, obtaining processed surface heat flux data, obtaining second prediction temperature and humidity field data according to the soil temperature and humidity field data and the processed surface heat flux data, and obtaining the difference degree of the soil temperature and humidity fields; obtaining the characteristic degree of the change of the surface heat flux according to the processed surface heat flux data, and obtaining the weight of the data at each moment in the second predicted temperature and humidity field data; and presetting a weight of data at each moment in the first predicted temperature-humidity field data to obtain final predicted temperature-humidity field data, and then carrying out mode prediction according to the final predicted temperature-humidity field data. According to the invention, the ground surface heat flux data and the soil temperature and humidity field data are processed, so that the accuracy of future weather prediction is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a mode forecasting method for correcting subsurface parameters by an area automatic station.
Background
Pattern prediction is a method of modeling and predicting weather and climate change in the atmosphere, sea and earth systems using models, and weather prediction becomes very important because disastrous weather can cause huge economic loss and serious social impact.
The global numerical mode is one of mode forecast, and when the global numerical mode is used for forecasting the soil temperature and humidity field data, the soil temperature and humidity field data of partial places are monitored, the soil temperature and humidity field data of the rest places are obtained in an interpolation mode, and the soil temperature and humidity field data of a future period of time are forecasted according to the soil temperature and humidity field data of all places; for places with large ecological environment differences, the accuracy of the soil temperature and humidity field data obtained by interpolation is low, so that the prediction accuracy of the soil temperature and humidity field data is low.
Disclosure of Invention
The invention provides a mode forecasting method for correcting subsurface parameters by an area automatic station, which aims to solve the existing problems.
The invention relates to a mode forecasting method for correcting subsurface parameters by an automatic regional station, which adopts the following technical scheme:
one embodiment of the present invention provides a method for model prediction of an area automatic station to correct subsurface parameters, the method comprising the steps of:
Collecting earth surface heat flux data and soil temperature and humidity field data, wherein the earth surface heat flux data and the soil temperature and humidity field data are time sequence data;
Obtaining first prediction temperature and humidity field data according to the soil temperature and humidity field data, obtaining abnormal data and normal data in the surface heat flux data according to the distribution characteristics of the surface heat flux data on time sequence, processing the abnormal data in the surface heat flux data according to the normal data in the surface heat flux data to obtain processed surface heat flux data, obtaining second prediction temperature and humidity field data according to the soil temperature and humidity field data and the processed surface heat flux data, and obtaining the difference degree of the soil temperature and humidity field according to the first prediction temperature and humidity field data and the second prediction temperature and humidity field data;
Obtaining the characteristic degree of the change of the earth surface heat flux according to the difference of the data of the adjacent moments in the processed earth surface heat flux data, and obtaining the weight of the data of each moment in the second predicted temperature and humidity field data according to the difference degree of the soil temperature and humidity field and the characteristic degree of the change of the earth surface heat flux;
the method comprises the steps of presetting a weight of data at each moment in first predicted temperature and humidity field data, weighting the data at each moment in the first predicted temperature and humidity field data and the data at each moment in the second predicted temperature and humidity field data according to the weight of the data at each moment in the first predicted temperature and humidity field data and the weight of the data at each moment in the second predicted temperature and humidity field data, obtaining final predicted temperature and humidity field data, and carrying out mode prediction according to the final predicted temperature and humidity field data.
Further, according to the distribution characteristics of the surface heat flux data in time sequence, the abnormal data and the normal data in the surface heat flux data are obtained, and the method comprises the following specific steps:
decomposing the surface heat flux data by using an STL time sequence decomposition algorithm to obtain a season term, a trend term and a residual term, fitting a Gaussian model according to all data of the residual term, and obtaining the Gaussian model Interval of principleThe residual item data is not in the/> -of the Gaussian modelThe surface heat flux data at all time points in the interval of the principle are recorded as abnormal data, and residual item data are recorded in the/>' of a Gaussian modelThe earth surface heat flux data at all moments in the interval of the principle are recorded as normal data;
Wherein, Mean value of all data representing residual term,/>Representing the standard deviation of all data of the residual term.
Further, the processing the abnormal data in the surface heat flux data according to the normal data in the surface heat flux data to obtain processed surface heat flux data comprises the following specific steps:
The normal data of the left side of each abnormal data, which is closest to the abnormal data, is recorded as the left adjacent normal data of each abnormal data, and the normal data of the right side of each abnormal data, which is closest to the abnormal data, is recorded as the right adjacent normal data of each abnormal data; and acquiring left adjacent normal data and right adjacent normal data of each abnormal data in the surface heat flux data, and taking the average value of the left adjacent normal data and the right adjacent normal data of each abnormal data as the processed data value of each abnormal data to obtain the processed surface heat flux data.
Further, the second predicted temperature and humidity field data is obtained according to the soil temperature and humidity field data and the processed surface heat flux data, and the method comprises the following specific steps:
And predicting data of each hour in two days in the future by land surface flux assimilation technology according to the soil temperature and humidity field data and the processed ground surface heat flux data, and marking the data as second predicted temperature and humidity field data.
Further, the method for obtaining the first predicted thermal-wet field data according to the soil thermal-wet field data comprises the following specific steps:
And predicting data of each hour in two future days through a global numerical mode according to the soil temperature and humidity field data, and marking the data as first predicted temperature and humidity field data.
Further, the difference degree of the soil temperature and humidity field is obtained according to the first predicted temperature and humidity field data and the second predicted temperature and humidity field data, and the calculation formula is as follows:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data indicating the ith time in the first predicted temperature-humidity field data, N indicates all data numbers of the first predicted temperature-humidity field data, wherein, the number of all data of the second predicted temperature-humidity field data is equal to the number of all data of the first predicted temperature-humidity field data,/>Representing first predicted temperature-humidity field data,/>Representing second predicted temperature-humidity field data,/>Representing the pearson correlation coefficient between the first predicted temperature-humidity field data and the second predicted temperature-humidity field data,/>Representing an exponential function based on a natural constant,/>Representing absolute value sign,/>Indicating the degree of difference in the soil temperature and humidity field.
Further, according to the difference of the data of adjacent moments in the processed surface heat flux data, the surface heat flux change characteristic degree is obtained, and the calculation formula is as follows:
In the method, in the process of the invention, Slope between position coordinates of data at j-1 th time and position coordinates of data at j-th time in processed surface heat flux data,/>The slope between the position coordinates of the data at the j-th time and the position coordinates of the data at the j+1th time in the processed surface heat flux data is represented by t, which represents the number of data at all times in the processed surface heat flux data,/>Data representing the j-th moment in the processed surface heat flux data,Data representing the j+1st moment in the processed surface heat flux data,/>Mean value representing difference between data at all adjacent moments in processed surface heat flux data,/>Representing the characteristic degree of the change of the surface heat flux,/>Representing absolute value symbols;
Wherein the abscissa of the position coordinates is time and the ordinate is the data value of the processed surface heat flux data.
Further, the weight of the data at each moment in the second predicted temperature-humidity field data is obtained according to the difference degree of the soil temperature-humidity field and the characteristic degree of the surface heat flux change, and the method comprises the following specific steps:
and adding the difference degree of the soil temperature and humidity field and the characteristic degree of the change of the earth surface heat flux to obtain the weight of the data at each moment in the second predicted temperature and humidity field data.
Further, the specific step of obtaining the weight of the data at each moment in the first predicted temperature and humidity field data is as follows:
Taking the preset value d as the weight of the data at each moment in the first predicted temperature and humidity field data.
Further, the weighting is performed on the data at each time in the first predicted temperature-humidity field data and the data at each time in the second predicted temperature-humidity field data according to the weight of the data at each time in the first predicted temperature-humidity field data and the weight of the data at each time in the second predicted temperature-humidity field data, so as to obtain final predicted temperature-humidity field data, which includes the following calculation formula:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data representing the ith time instant in the first predicted temperature-humidity field data,/>Weight value of data representing each time in the second predicted temperature-humidity field data, d represents weight value of data representing each time in the first predicted temperature-humidity field data,/>Data representing the i-th time of the final predicted temperature-humidity field data.
The technical scheme of the invention has the beneficial effects that: according to the method, first prediction temperature and humidity field data are obtained according to the soil temperature and humidity field data, abnormal data and normal data in the surface heat flux data are obtained, the abnormal data in the surface heat flux data are processed according to the normal data in the surface heat flux data to obtain processed surface heat flux data, second prediction temperature and humidity field data are obtained according to the soil temperature and humidity field data and the processed surface heat flux data, the difference degree of the soil temperature and humidity field is obtained according to the first prediction temperature and humidity field data and the second prediction temperature and humidity field data, the characteristic degree of the surface heat flux change is obtained according to the processed surface heat flux data, and the difference between the data in the region is defined; obtaining a weight of data at each moment in the second predicted temperature-humidity field data according to the difference degree of the soil temperature-humidity field and the characteristic degree of the earth surface heat flux change; acquiring the weight of the data at each moment in the first predicted temperature and humidity field data, acquiring final predicted temperature and humidity field data according to the weight of the data at each moment in the first predicted temperature and humidity field data and the weight of the data at each moment in the second predicted temperature and humidity field data, performing mode prediction according to the final predicted temperature and humidity field data, acquiring the weight of the second predicted temperature and humidity field data according to the difference between the data in the area, and correcting the second predicted temperature and humidity field data according to the weight, so that the accuracy of future weather prediction is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a method for predicting a pattern for correcting subsurface parameters in an area automatic station according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following description refers to the specific implementation, structure, characteristics and effects of a mode forecasting method for correcting subsurface parameters by an area automatic station according to the present invention, which is provided by the present invention, with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a mode forecasting method for correcting subsurface parameters by an area automatic station provided by the invention with reference to the accompanying drawings.
In the prediction process of the soil temperature and humidity field data, the predicted soil temperature and humidity field data are analyzed according to the soil temperature and humidity field data and the ground surface heat flux data acquired before, so that the accuracy of the soil temperature and humidity field data and the difference between the ground surface heat flux data are influenced according to the prediction of the soil temperature and humidity field data, and the difference between the acquired soil temperature and humidity field data and the ground surface heat flux data is larger for places with larger ecological environment difference, so that the predicted soil temperature and humidity field data are corrected according to the difference between the data, and the accuracy of data prediction is improved.
Referring to fig. 1, a flowchart of a method for predicting a mode of correcting a subsurface parameter by an area automatic station according to an embodiment of the invention is shown, the method includes the following steps:
Step S001: and collecting surface heat flux data and soil temperature and humidity field data.
It should be noted that, since the prediction of weather is performed based on the data of the weather change at some previous time, the data of the weather change at some previous time is acquired.
Specifically, the surface heat flux data and the soil thermal wet field data of the past week in the regional automation station are acquired at one hour intervals.
Thus, the ground surface heat flux data and the soil temperature and humidity field data are obtained.
Step S002: obtaining first prediction thermal-wet field data according to the soil thermal-wet field data, obtaining abnormal data and normal data in the surface thermal flux data according to the distribution characteristics of the surface thermal flux data on time sequence, processing the abnormal data in the surface thermal flux data according to the normal data in the surface thermal flux data to obtain processed surface thermal flux data, obtaining second prediction thermal-wet field data according to the soil thermal-wet field data and the processed surface thermal flux data, and obtaining the difference degree of the soil thermal-wet field according to the first prediction thermal-wet field data and the second prediction thermal-wet field data.
It should be noted that, the prediction data may be directly obtained through a global numerical model according to the soil temperature and humidity field data, but when the prediction data is performed through the land flux assimilation technology, the prediction is performed according to the change rule of other types of data, such as the surface heat flux data, but during the collection process, the collected surface heat flux data may be abnormal due to the interference of the outside, so that the surface heat flux data needs to be preprocessed to abnormal data, and then is analyzed and processed according to the processed surface heat flux data.
It should be further noted that, since the above data are all obtained according to a time sequence, the time sequence data are characterized in that all residual items are obtained by decomposing the time sequence data, that is, the residual item data under normal conditions are stable, that is, the fluctuation degree is not large, so that the abnormal data are screened out according to the stability of the residual item data, and then the abnormal data are processed to obtain a group of ground surface heat flux data which is not interfered by the outside.
Specifically, using an STL time sequence decomposition algorithm to decompose the surface heat flux data to obtain a season term, a trend term and a residual term, wherein each surface heat flux data corresponds to the season term data, the trend term data and the residual term data; fitting a Gaussian model according to all data of the residual error item to obtain the Gaussian modelInterval of principle/>The residual item data is not in the/> -of the Gaussian modelThe surface heat flux data at all time points in the interval of the principle are recorded as abnormal data, and residual item data are recorded in the/>' of a Gaussian modelThe earth surface heat flux data at all moments in the interval of the principle are recorded as normal data; wherein/>Mean value of all data representing residual term,/>Representing the standard deviation of all data of the residual term.
The normal data of the left side of each abnormal data, which is closest to the abnormal data, is recorded as the left adjacent normal data of each abnormal data, and the normal data of the right side of each abnormal data, which is closest to the abnormal data, is recorded as the right adjacent normal data of each abnormal data; acquiring left adjacent normal data and right adjacent normal data of each abnormal data, and taking the average value of the left adjacent normal data and the right adjacent normal data of each abnormal data as the processed data value of each abnormal data, so that the processed surface heat flux data can be obtained. The STL timing decomposition algorithm is a well-known technique, and is not described in detail herein; the gaussian model is also a well-known technique, and will not be described in detail here.
Predicting data of each hour in two days in the future according to the soil temperature and humidity field data through a global numerical mode, and marking the data as first predicted temperature and humidity field data G; and predicting data of each hour in two days in the future by land surface flux assimilation technology according to the soil temperature and humidity field data and the processed ground surface heat flux data, and marking the data as second predicted temperature and humidity field data H. The global numerical mode and the land flux assimilation technology are known technologies, and are not described in detail herein.
Thus, first predicted temperature-humidity field data and second predicted temperature-humidity field data are obtained.
When the data of the weather condition is predicted by using two modes of a global numerical mode and a land flux assimilation technology, the smaller the predicted error is, the less the difference between the data of the same time is between the two modes; when the difference between the two predicted groups of data is smaller, the larger the correlation coefficient between the two groups of data is obtained through the pearson correlation coefficient, the more relevant the two groups of data are, namely the larger the corresponding correlation coefficient is, the smaller the difference between the two groups of data is, which means that the more accurate the predicted data is, namely the more normal the predicted data is; when the difference between the two predicted sets of data is larger, the smaller the correlation coefficient between the two sets of data is obtained through the pearson correlation coefficient, the less the two sets of data are related, namely the smaller the corresponding correlation coefficient is, the larger the difference between the two sets of data is, the more inaccurate the predicted data is, namely the more abnormal the predicted data is.
Specifically, the difference degree of the soil temperature and humidity field is obtained according to the first predicted temperature and humidity field data and the second predicted temperature and humidity field data, and is expressed as:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data indicating the ith time in the first predicted temperature-humidity field data, N indicates all data numbers of the first predicted temperature-humidity field data, wherein, the number of all data of the second predicted temperature-humidity field data is equal to the number of all data of the first predicted temperature-humidity field data,/>Representing first predicted temperature-humidity field data,/>Representing second predicted temperature-humidity field data,/>Representing the pearson correlation coefficient between the first predicted temperature-humidity field data and the second predicted temperature-humidity field data,/>Representing an exponential function based on a natural constant,/>Representing absolute value sign,/>Indicating the degree of difference in the soil temperature and humidity field.
Wherein,The difference between the first predicted temperature and humidity field data and the second predicted temperature and humidity field data is represented, namely the difference between the predicted data in two prediction modes is also represented, and the larger the difference between the predicted data is, the more abnormal the difference is, and the lower the accuracy of the predicted data is represented; /(I)For the pearson correlation coefficient between the first predicted temperature-humidity field data and the second predicted temperature-humidity field data, when the accuracy of the prediction of the two sets of data is higher, the correlation coefficient between the two sets of data is larger, the corresponding degree of abnormality should be smaller, and when the accuracy of the prediction of the two sets of data is worse, the correlation coefficient between the two sets of data is smaller, the corresponding degree of abnormality should be larger.
Thus, the difference degree of the soil temperature and humidity field is obtained.
Step S003: and obtaining the characteristic degree of the earth surface heat flux change according to the processed earth surface heat flux data, and obtaining the weight of the data at each moment in the second predicted temperature and humidity field data according to the differential degree of the soil temperature and humidity field and the characteristic degree of the earth surface heat flux change.
It should be noted that, the processed surface heat flux data in the above steps is only analyzed and processed according to the residual terms, but a set of time series data should also consider trend characteristics, and when the trend characteristics of a set of data are not obvious, the reliability of the obtained processed surface heat flux data is not high; and because the second predicted temperature and humidity field data is obtained according to the processed surface heat flux data, the accuracy of the correlation between the obtained second predicted temperature and humidity field data and the first predicted temperature and humidity field data is not high, so that the processed surface heat flux data needs to be analyzed, and the change characteristics of the data in the processed surface heat flux data are obtained.
Specifically, a two-dimensional plane coordinate system is established by taking the data value of the processed surface heat flux data as a vertical axis and taking the time sequence as a horizontal axis; and placing the processed surface heat flux data in a two-dimensional plane coordinate system to obtain the position coordinate of each datum in the processed surface heat flux data. Obtaining the characteristic degree of the change of the surface heat flux according to the processed surface heat flux data, and expressing the characteristic degree as follows by a formula:
In the method, in the process of the invention, Slope between position coordinates of data at j-1 th time and position coordinates of data at j-th time in processed surface heat flux data,/>The slope between the position coordinates of the data at the j-th time and the position coordinates of the data at the j+1th time in the processed surface heat flux data is represented by t, which represents the number of data at all times in the processed surface heat flux data,/>Data representing the j-th moment in the processed surface heat flux data,Data representing the j+1st moment in the processed surface heat flux data,/>Mean value representing difference between data at all adjacent moments in processed surface heat flux data,/>Representing the characteristic degree of the change of the surface heat flux,/>Representing absolute value symbols. Wherein the difference represents the absolute value of the difference.
Wherein,The difference between adjacent slopes is represented, when the difference is larger, the characteristic degree of the change of the earth surface heat flux is represented to be larger, namely the weight of the corresponding data which needs to be corrected is also larger, and when the difference is smaller, the characteristic degree of the change of the earth surface heat flux is represented to be smaller, namely the weight of the corresponding data which needs to be corrected is also smaller; /(I)The larger the variance value is, the less credible the first predicted temperature-humidity field data and the second predicted temperature-humidity field data are, namely, the lower the accuracy is, the larger the weight is required to be corrected, and the smaller the variance value is, the more credible the first predicted temperature-humidity field data and the second predicted temperature-humidity field data are, namely, the higher the accuracy is, the smaller the weight is required to be corrected.
Thus, the characteristic degree of the change of the surface heat flux is obtained.
It should be noted that, when the second predicted temperature-humidity field data is obtained, the data is obtained by land surface flux assimilation technology according to the processed surface heat flux data, so that the data distribution characteristics in the processed surface heat flux data also affect the predicted second predicted temperature-humidity field data; and when the difference between the first predicted temperature-humidity field data and the second temperature-humidity field data is larger, the accuracy of the predicted first predicted temperature-humidity field data and the second temperature-humidity field data is not too high, so that the weight of each data in the second predicted temperature-humidity field data is obtained according to the difference between the first predicted temperature-humidity field data and the second temperature-humidity field data and the data distribution characteristics in the processed surface heat flux data, and the weight is corrected.
Specifically, the weight of the data at each moment in the second predicted temperature-humidity field data is obtained according to the difference degree of the soil temperature-humidity field and the characteristic degree of the earth surface heat flux change, and the weight is expressed as follows:
In the method, in the process of the invention, Representing the difference degree of soil temperature and humidity field,/>Representing the characteristic degree of the change of the surface heat flux,/>The weight of the data representing each time in the second predicted temperature-humidity field data.
When the difference degree of the soil temperature and humidity fields is larger, the characteristic degree of the change of the surface heat flux is larger, and the weight of corresponding data is larger.
So far, the weight of the data at each moment in the second predicted temperature-humidity field data is obtained.
Step S004: and acquiring the weight of the data at each moment in the first predicted temperature-humidity field data, and acquiring final predicted temperature-humidity field data according to the weight of the data at each moment in the first predicted temperature-humidity field data and the weight of the data at each moment in the second predicted temperature-humidity field data.
It should be noted that, since the weight of each data in the second predicted thermal-wet field data is obtained in the above description, and since the corrected data is obtained by correcting the corresponding data according to the weight of each data in the second predicted thermal-wet field data, then the corrected data is used as the difference between the first predicted thermal-wet field data and the accurate data, the first predicted thermal-wet field data is kept unchanged.
Specifically, a value d is preset, where the embodiment is described by taking d=1 as an example, and the embodiment is not specifically limited, where d may be determined according to the specific implementation situation. Taking the preset value d as the weight of the data at each moment in the first predicted temperature and humidity field data, weighting according to the weight of the data at each moment in the first predicted temperature and humidity field data and the weight of the data at each moment in the second predicted temperature and humidity field data to obtain final predicted temperature and humidity field data, and obtaining the final predicted temperature and humidity field data according to the weight of the data at each moment in the first predicted temperature and humidity field data and the weight of the data at each moment in the second predicted temperature and humidity field data, wherein the final predicted temperature and humidity field data is expressed by a formula:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data representing the ith time instant in the first predicted temperature-humidity field data,/>Weight value of data representing each time in the second predicted temperature-humidity field data, d represents weight value of data representing each time in the first predicted temperature-humidity field data,/>Data representing the i-th time of the final predicted temperature-humidity field data.
So far, the final predicted temperature and humidity field data at all moments of two days in the future are obtained.
This embodiment is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for model prediction of regional automatic station correction subsurface parameters, the method comprising the steps of:
Collecting earth surface heat flux data and soil temperature and humidity field data, wherein the earth surface heat flux data and the soil temperature and humidity field data are time sequence data;
Obtaining first prediction temperature and humidity field data according to the soil temperature and humidity field data, obtaining abnormal data and normal data in the surface heat flux data according to the distribution characteristics of the surface heat flux data on time sequence, processing the abnormal data in the surface heat flux data according to the normal data in the surface heat flux data to obtain processed surface heat flux data, obtaining second prediction temperature and humidity field data according to the soil temperature and humidity field data and the processed surface heat flux data, and obtaining the difference degree of the soil temperature and humidity field according to the first prediction temperature and humidity field data and the second prediction temperature and humidity field data;
Obtaining the characteristic degree of the change of the earth surface heat flux according to the difference of the data of the adjacent moments in the processed earth surface heat flux data, and obtaining the weight of the data of each moment in the second predicted temperature and humidity field data according to the difference degree of the soil temperature and humidity field and the characteristic degree of the change of the earth surface heat flux;
the method comprises the steps of presetting a weight of data at each moment in first predicted temperature and humidity field data, weighting the data at each moment in the first predicted temperature and humidity field data and the data at each moment in the second predicted temperature and humidity field data according to the weight of the data at each moment in the first predicted temperature and humidity field data and the weight of the data at each moment in the second predicted temperature and humidity field data, obtaining final predicted temperature and humidity field data, and carrying out mode prediction according to the final predicted temperature and humidity field data.
2. The method for predicting the mode of correcting the subsurface parameters by the regional automatic station according to claim 1, wherein the obtaining of the abnormal data and the normal data in the surface heat flux data according to the distribution characteristics of the surface heat flux data in time sequence comprises the following specific steps:
decomposing the surface heat flux data by using an STL time sequence decomposition algorithm to obtain a season term, a trend term and a residual term, fitting a Gaussian model according to all data of the residual term, and obtaining the Gaussian model Interval of principle/>The residual item data is not in the/> -of the Gaussian modelThe surface heat flux data at all time points in the interval of the principle are recorded as abnormal data, and residual item data are recorded in the/>' of a Gaussian modelThe earth surface heat flux data at all moments in the interval of the principle are recorded as normal data;
Wherein, Mean value of all data representing residual term,/>Representing the standard deviation of all data of the residual term.
3. The method for predicting the mode of correcting the subsurface parameters by the regional automatic station according to claim 1, wherein the processing the abnormal data in the surface heat flux data according to the normal data in the surface heat flux data to obtain the processed surface heat flux data comprises the following specific steps:
The normal data of the left side of each abnormal data, which is closest to the abnormal data, is recorded as the left adjacent normal data of each abnormal data, and the normal data of the right side of each abnormal data, which is closest to the abnormal data, is recorded as the right adjacent normal data of each abnormal data; and acquiring left adjacent normal data and right adjacent normal data of each abnormal data in the surface heat flux data, and taking the average value of the left adjacent normal data and the right adjacent normal data of each abnormal data as the processed data value of each abnormal data to obtain the processed surface heat flux data.
4. The method for predicting the mode of correcting the subsurface parameters of the subsurface by using the regional automatic station according to claim 1, wherein the second predicted temperature-humidity field data is obtained according to the soil temperature-humidity field data and the processed surface heat flux data, comprising the following specific steps:
And predicting data of each hour in two days in the future by land surface flux assimilation technology according to the soil temperature and humidity field data and the processed ground surface heat flux data, and marking the data as second predicted temperature and humidity field data.
5. The method for predicting the mode of correcting the subsurface parameters of the subsurface by using the regional automatic station according to claim 1, wherein the step of obtaining the first predicted temperature-humidity field data according to the soil temperature-humidity field data comprises the following specific steps:
And predicting data of each hour in two future days through a global numerical mode according to the soil temperature and humidity field data, and marking the data as first predicted temperature and humidity field data.
6. The method for predicting the mode of correcting the parameters of the subsurface mat at the automatic station in the area according to claim 1, wherein the difference degree of the soil temperature and humidity field is obtained according to the first predicted temperature and humidity field data and the second predicted temperature and humidity field data, and the calculation formula comprises the following steps:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data indicating the ith time in the first predicted temperature-humidity field data, N indicates all data numbers of the first predicted temperature-humidity field data, wherein, the number of all data of the second predicted temperature-humidity field data is equal to the number of all data of the first predicted temperature-humidity field data,/>Representing first predicted temperature-humidity field data,/>Representing second predicted temperature-humidity field data,/>Representing the pearson correlation coefficient between the first predicted temperature-humidity field data and the second predicted temperature-humidity field data,/>Representing an exponential function based on a natural constant,/>The sign of the absolute value is represented,Indicating the degree of difference in the soil temperature and humidity field.
7. The method for predicting the mode of correcting the subsurface parameters by the regional automatic station according to claim 1, wherein the obtaining the characteristic degree of the change of the surface heat flux according to the difference of the data of the adjacent moments in the processed surface heat flux data comprises the following calculation formula:
In the method, in the process of the invention, Slope between position coordinates of data at j-1 th time and position coordinates of data at j-th time in processed surface heat flux data,/>The slope between the position coordinates of the data at the j-th time and the position coordinates of the data at the j+1th time in the processed surface heat flux data is represented by t, which represents the number of data at all times in the processed surface heat flux data,/>Data representing the j-th moment in the processed surface heat flux data,Data representing the j+1st moment in the processed surface heat flux data,/>Mean value representing difference between data at all adjacent moments in processed surface heat flux data,/>Representing the characteristic degree of the change of the surface heat flux,/>Representing absolute value symbols;
Wherein the abscissa of the position coordinates is time and the ordinate is the data value of the processed surface heat flux data.
8. The method for predicting the mode of correcting the subsurface parameters by the regional automatic station according to claim 1, wherein the step of obtaining the weight of the data at each moment in the second predicted thermal-wet field data according to the differential degree of the soil thermal-wet field and the characteristic degree of the change of the surface heat flux comprises the following specific steps:
and adding the difference degree of the soil temperature and humidity field and the characteristic degree of the change of the earth surface heat flux to obtain the weight of the data at each moment in the second predicted temperature and humidity field data.
9. The method for predicting the mode of correcting the subsurface parameters by the regional automatic station according to claim 1, wherein the specific obtaining step of the weight of the data at each moment in the first predicted temperature-humidity field data is as follows:
Taking the preset value d as the weight of the data at each moment in the first predicted temperature and humidity field data.
10. The method for predicting the mode of correcting the subsurface parameters of the ground by the regional automatic station according to claim 1, wherein the weighting of the data at each time in the first predicted thermal-wet field data and the data at each time in the second predicted thermal-wet field data according to the weight of the data at each time in the first predicted thermal-wet field data and the weight of the data at each time in the second predicted thermal-wet field data to obtain the final predicted thermal-wet field data comprises the following calculation formulas:
In the method, in the process of the invention, Data representing the ith time instant in the second predicted temperature-humidity field data,/>Data representing the ith time instant in the first predicted temperature-humidity field data,/>Weight value of data representing each time in the second predicted temperature-humidity field data, d represents weight value of data representing each time in the first predicted temperature-humidity field data,/>Data representing the i-th time of the final predicted temperature-humidity field data.
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