Indoor floor temperature inversion method based on combined observation and reanalysis data

Abstract

The invention discloses an indoor floor temperature inversion method based on the combination of observation and reanalysis data, comprising: obtaining observation data and reanalysis data of an area to be inverted for indoor floor temperature, screening out multivariate meteorological elements as substitute elements for indoor floor temperature and correcting them, establishing a multivariate linear regression fitting model, and inverting indoor floor temperature; the invention utilizes a correlation analysis method to screen out multivariate meteorological elements for indoor floor temperature fitting and corrects them, then establishes an indoor floor temperature regression model through a multivariate linear regression analysis method, establishes a quantitative relationship between the observed indoor floor temperature and the outdoor air temperature, dew point and relative humidity in the reanalysis data, realizes the substitution of indoor floor temperature under observation shortage conditions, can effectively expand limited indoor floor temperature observation data to the time scale covered by the reanalysis data, and has high scientific value and application value for the prediction of interannual changes in the number of days of the return of the south wind, and even the prediction of interdecadal changes.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor floor temperature observation, and in particular to an indoor floor temperature inversion method based on a combination of observation and reanalysis data. Background Art

[0002] The return of the south wind is the name of a weather phenomenon in southern China. It usually refers to the phenomenon that the temperature begins to warm up and the humidity rises sharply in the spring every year. At this time, the temperature of indoor objects is still at a relatively low temperature. When the temperature rises rapidly, the surfaces of colder objects such as the floor and walls in the room are prone to condensation and produce water droplets, and the air humidity is close to saturation. This situation is often accompanied by heavy fog, and visibility is significantly reduced. The whole area becomes damp and gloomy, hot and sultry, which has an impact on people's mood, physical health, crop growth and yield, transportation and other aspects. For example, in terms of transportation, high temperature and high humidity climate conditions are likely to lead to poor road conditions and increase the risk of accidents. The humid condensation of the return of the south wind will cause certain damage to power equipment. Therefore, sufficient attention needs to be paid to the return of the south wind weather, and corresponding measures should be taken, such as strengthening meteorological monitoring and forecasting, adjusting agricultural production strategies, etc., to minimize the adverse effects of the "return of the south wind" phenomenon.

[0003] When studying the phenomenon of the return of the south wind, the indoor floor temperature is one of the necessary data. However, due to the lack of observation data, only a few areas have carried out the prediction of indoor floor temperature, and only the data of recent years have been accumulated, and it is impossible to share it in the short term. On the other hand, the current method of replacing the indoor floor temperature with the daily minimum temperature is to judge the phenomenon of the return of the south wind by the difference between the daily minimum temperature and the dew point temperature at a specific time being less than zero. This statistical method has a high probability of misjudging the morning dew as the phenomenon of the return of the south wind, resulting in inaccurate prediction results of the indoor floor temperature. In addition, the indoor floor temperature observations for the phenomenon of the return of the south wind are very rare, and it is impossible to perform climate scale analysis, resulting in a certain gap between the predicted indoor floor temperature and the actual indoor floor temperature, which brings challenges to the theoretical and technical research on the climate prediction of the phenomenon of the return of the south wind. Therefore, the present invention eliminates the indoor floor temperature inversion method based on the combination of observation and reanalysis data to solve the problems existing in the prior art. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to eliminate the indoor floor temperature inversion method based on the combination of observation and reanalysis data, to solve the problem that the prediction results of indoor floor temperature are not accurate enough, the existing indoor floor temperature prediction method has a high probability of misjudging morning dew as the return of the south wind phenomenon, and there is a certain gap between the predicted indoor floor temperature and the actual indoor floor temperature, resulting in the inability to perform climate scale analysis and prediction.

[0005] In order to achieve the purpose of the present invention, the present invention is implemented by the following technical scheme: a method for inverting indoor floor temperature based on the combination of observation and reanalysis data, comprising the following steps:

[0006] Step 1: First observe the station data of the area where the indoor floor temperature is to be inverted and obtain the observation data, and then collect the reanalysis data of the area where the indoor floor temperature is to be inverted;

[0007] Step 2: Based on the observation data, multivariate meteorological elements are selected from the reanalysis data as substitutes for indoor floor temperature, and then the selected multivariate meteorological elements are corrected;

[0008] Step 3: First, the multivariate meteorological factors corrected in step 2 are established as a multivariate linear regression fitting model through the method of multivariate linear regression analysis, and then the multivariate linear regression fitting model is used to invert the indoor floor temperature.

[0009] A further improvement is that in step 1, the observation data includes the site dew point temperature, the site outdoor air temperature and the site relative humidity.

[0010] A further improvement is that in step 1, the reanalysis data includes reanalysis of dew point temperature, reanalysis of outdoor air temperature and reanalysis of relative humidity.

[0011] A further improvement is that in step 2, the multi-element meteorological elements include outdoor temperature, dew point temperature and relative humidity, and the outdoor temperature is replaced by the ground 2m temperature of the reanalysis data.

[0012] A further improvement is that after the multivariate meteorological elements are corrected, the fitted site dew point temperature data and the fitted site relative humidity are obtained, the fitted site dew point temperature data are obtained by fitting the reanalysis dew point temperature and the site dew point temperature, and the fitted site relative humidity is obtained by fitting the reanalysis relative humidity and the site relative humidity.

[0013] A further improvement is that in the step 2, uncertain factors are eliminated during the establishment of the multivariate linear regression fitting model, and the uncertain factors include topography and floor material.

[0014] A further improvement is that in step 2, the formula of the multivariate linear regression fitting model is:

[0015] T 室内地板 =0.1589*T 室外 +0.3345*Td 订正 +0.0071*Rh 订正 +10.6276

[0016] Among them, T 室内地板 is the indoor floor temperature, T 室外is the outdoor temperature, Td 订正 To fit the site dew point temperature data, Rh 订正 is the fitted site relative humidity.

[0017] A further improvement is that in step three, the return of the south wind phenomenon is determined based on the indoor floor temperature calculated by fitting and the dew point temperature of the day, and the determination criterion is: when the indoor floor temperature is less than the dew point temperature, the return of the south wind phenomenon is determined to have occurred.

[0018] The beneficial effects of the present invention are as follows: the present invention utilizes the correlation analysis method to screen and revise the multivariate meteorological factors used for indoor floor temperature fitting, and then establishes an indoor floor temperature regression model through the multivariate linear regression analysis method, and establishes a quantitative relationship between the observed indoor floor temperature and the outdoor air temperature, dew point and relative humidity in the reanalysis data, thereby realizing the replacement of the indoor floor temperature under the condition of observation shortage, and can effectively expand the limited indoor floor temperature observation data to the time scale covered by the reanalysis data, and has high scientific value and application value for the prediction of the interannual change of the number of days of the return of the south wind, and even the prediction of the interdecadal change. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 It is a schematic diagram of the process of the indoor floor temperature inversion method of the present invention;

[0021] Figure 2 is a schematic diagram comparing the site floor temperature change and the fitted floor temperature change in the first embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the changes in the floor temperature and dew point temperature of a site during a return of southerly winds process in the first embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the changes in the floor temperature and dew point temperature of a site during a process of the return of the south wind in the first embodiment of the present invention;

[0024] Figure 5 is a correlation analysis diagram of each data in the second embodiment of the present invention;

[0025] Figure 6 It is a floor temperature station data of the first fitting formula in the second embodiment of the present invention and an hourly temperature variation diagram of the fitting station data;

[0026] Figure 7 It is a floor temperature site data and hourly temperature variation diagram of the fitting site data of the second fitting formula in the second embodiment of the present invention;

[0027] Figure 8 This is a diagram of the floor temperature station data and hourly temperature changes of the fitting station data according to the optimal fitting formula in the second embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The present invention provides an indoor floor temperature inversion method based on the combination of observation and reanalysis data, comprising the following steps:

[0030] Step 1: First observe the station data of the area where the indoor floor temperature is to be inverted and obtain the observation data, then collect the reanalysis data of the area where the indoor floor temperature is to be inverted. The observation data include the station dew point temperature, the station outdoor temperature and the station relative humidity, and the reanalysis data include the reanalysis dew point temperature, the reanalysis outdoor temperature and the reanalysis relative humidity;

[0031] Step 2: Based on the observation data, multivariate meteorological elements are selected from the reanalysis data as substitutes for indoor floor temperature, and then the selected multivariate meteorological elements are corrected. The multivariate meteorological elements include outdoor temperature, dew point temperature and relative humidity. The outdoor temperature is replaced by the ground 2m temperature of the reanalysis data. After the multivariate meteorological elements are corrected, the fitted site dew point temperature data and the fitted site relative humidity are obtained. The fitted site dew point temperature data is obtained by fitting the reanalysis dew point temperature with the site dew point temperature, and the fitted site relative humidity is obtained by fitting the reanalysis relative humidity with the site relative humidity.

[0032] Step 3: First, the multivariate meteorological factors corrected in step 2 are established as a multivariate linear regression fitting model through the method of multivariate linear regression analysis, and then the multivariate linear regression fitting model is used to invert the indoor floor temperature. In the process of establishing the multivariate linear regression fitting model, uncertain factors are eliminated, including topography and floor material. The formula of the multivariate linear regression fitting model is:

[0033] T 室内地板 =0.1589*T 室外 +0.3345*Td 订正 +0.0071*Rh订正 +10.6276

[0034] Among them, T 室内地板 is the indoor floor temperature, T 室外 is the outdoor temperature, Td 订正 To fit the site dew point temperature data, Rh 订正 To fit the site relative humidity;

[0035] The return of the south wind phenomenon is determined based on the indoor floor temperature calculated by fitting and the dew point temperature of the day. The determination standard is: when the indoor floor temperature is lower than the dew point temperature, the return of the south wind phenomenon occurs.

[0036] Embodiment 1

[0037] Through correlation analysis, it is found that the correlation coefficient between dew point temperature and indoor floor temperature is 0.77, indicating that there is a certain correlation between the two variables, and the linear correlation is high. In addition, the daily variation of dew point temperature is relatively gentle compared with the outdoor temperature. Therefore, we can try to use dew point temperature to perform univariate fitting on indoor floor temperature and establish a fitting equation.

[0038] See also Figure 2 , Figure 3 and Figure 4 In this embodiment, the equation is established with the reanalysis data as the independent variable. Since there is a certain error between the reanalysis dew point temperature and the site dew point temperature, in order to minimize the error, the reanalysis dew point temperature is fitted with the site dew point temperature to obtain the fitted site dew point temperature data, referred to as Td 订正 , and then use it to establish a fitting equation with the indoor floor temperature. The fitting model established by this scheme will be more suitable for the data of the reanalysis data, where Td 订正 The correlation coefficient with the indoor floor temperature is 0.82, which is higher than the correlation coefficient before correction, indicating that it is beneficial to establish a fitting model after correcting the reanalysis data.

[0039] The fitting model is established by using the fitting site dew point temperature and indoor floor temperature to obtain the formula:

[0040] T 室内地板 =0.1401*Td 订正 +16.1785

[0041] The calculated fitted indoor floor temperature data is plotted as a time series graph and compared with the indoor floor temperature of the actual site, such as Figure 2 As shown in the figure, the general trend of the fitting is consistent with the actual situation, the daily variation trend is gentle, and the error is within 2℃. Through the correlation test analysis, the correlation coefficient between the fitting result and the actual temperature is 0.70, indicating that Td 订正 With T 室内地板There is a high correlation between the changes in

[0042] like Figure 3 As shown in the figure, the temperature time series is drawn by using the indoor floor temperature and the site dew point temperature of a return of south wind process in a specific time period. The occurrence of the return of south wind phenomenon is determined by the standard of indoor floor temperature-dew point temperature < 0. In this time period, the return of south wind phenomenon occurred for 7 days in total.

[0043] like Figure 4 To prove the feasibility of the fitting model, the following is drawn: Figure 3 The fitting effect diagram of the same time series shows that there were 7 return of south wind phenomena in this period, which is consistent with the actual situation. From the fitting effect of the temperature change of the overall return of south wind process, the fitting effect of the return of south wind process is also good, which can be used to fit the indoor floor temperature -Td 订正 The number of days of the return of the south wind is calculated based on the standard of <0.

[0044] Embodiment 2

[0045] See also Figure 5 , Figure 6 , Figure 7 and Figure 8 Due to limited data, it is currently impossible to know the weights of influencing factors other than meteorological factors on indoor floor temperature. Therefore, this embodiment eliminates uncertain factors such as topography and floor material, and only uses part of the currently available dew point temperature, relative humidity and outdoor temperature data to fit the multivariate linear regression model of indoor floor temperature, so as to calculate the indoor floor temperature of the long time series as accurately as possible.

[0046] Based on the method of fitting indoor floor temperature with dew point temperature, relative humidity and outdoor temperature (outdoor temperature is replaced by the air temperature at 2m above the ground from reanalysis data) are added to establish a multivariate linear regression fitting model.

[0047] like Figure 5 As shown ( Figure 5 (a) is a scatter plot of the correlation between floor temperature and site dew point temperature. Figure 5 (b) is a scatter plot of the correlation between floor temperature and site relative humidity. Figure 5 (c) is a scatter plot of the correlation between floor temperature and reanalysis outdoor temperature. Figure 5 (d) is the scatter plot of reanalysis dew point temperature and site dew point temperature. Figure 5 (e) is a scatter plot of the correlation between reanalysis relative humidity and station relative humidity. Figure 5 (f) is the station dew point temperature and Td 订正 Correlation scatter plot, Figure 5 (g) is the relative humidity of the site and Rh 订正Correlation scatter plot), the site dew point temperature, site relative humidity and reanalysis 2m outdoor temperature and indoor floor temperature at the same time were correlated. All ordinates represent indoor floor temperature (unit: ℃), and the abscissas represent the above three influencing factors, with correlation coefficients of 0.85, 0.27, and 0.79, respectively. The results show that the site dew point temperature is strongly correlated with the reanalysis 2m outdoor temperature and indoor floor temperature, while the site relative humidity is weakly correlated. After correcting the reanalysis dew point temperature and relative humidity, its Td 订正 The correlation with indoor floor temperature decreased slightly, but still remained strong. 订正 The correlation with indoor floor temperature is improved to 0.36.

[0048] Combined with reanalysis of 2m outdoor temperature, Td 订正 and Rh 订正 The first fitting formula obtained by multivariate fitting of indoor floor temperature is:

[0049] T 室内地板 =0.1723*T 室外 +0.3429*Td 订正 +0.0008*Rh 订正 +10.8938

[0050] The correlation coefficient r = 0.84, indicating that the indoor floor temperature and the fitted indoor floor temperature have a high correlation. Figure 6 As shown, their changing trends and fluctuations are similar.

[0051] Similarly, the second fitting formula is calculated using data from January to April 2021:

[0052] T 室内地板 =0.2301*T 室外 +0.2269*Td 订正 +0.0669*Rh 订正 +6.8327

[0053] The correlation coefficient is as high as 0.9. Figure 7 As shown, their changing trends and fluctuations are similar.

[0054] pass Figure 6 It can be concluded that the indoor floor temperature after multivariate fitting is strongly correlated with the indoor floor temperature of the actual site, and the change trend is roughly consistent. In order to further test the fitting effect, the first 2500 samples are selected to establish the fitting model, and the remaining samples are used for trial reporting. Figure 6 As shown, there are some deviations in the test results.

[0055] The fitting formula of 2019 and the fitting formula of 2021 are combined to construct a unified fitting formula, with a total of 5760 samples. The first 5000 samples are selected to build the fitting model, and the remaining samples are used for trial reporting. Figure 7 As shown, the test report effect is good. The ordinate represents the indoor floor temperature (unit: ℃), and the abscissa represents the dates from January to April 2019 and January to April 2021. Due to the large number of dates, they are chosen to be ignored. After all samples are used for fitting, the optimal fitting formula is obtained:

[0056] T 室内地板 =0.1589*T 室外 +0.3345*Td 订正 +0.0071*Rh 订正 +10.6276

[0057] The correlation coefficient is 0.88. Figure 8 The results show that the fitting formula has a good fitting effect.

[0058] In this embodiment, the judgment criterion for the occurrence of the return of the south wind phenomenon is set as the fitting floor temperature-fitting site td<<1°C, so as to more accurately count the number of days of the return of the south wind phenomenon, so that the counted data is closer to the actual value.

[0059] In summary, based on T 室内地板 =0.1589*T 室外 +0.3345*Td 订正 +0.0071*Rh 订正 The indoor floor temperature fitting scheme of +10.6276 model is feasible and has considerable accuracy and reliability.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The indoor floor temperature inversion method based on the combination of observation and reanalysis data is characterized by: The following steps are involved: Step 1: First observe the station data of the area where the indoor floor temperature is to be inverted and obtain the observation data, and then collect the reanalysis data of the area where the indoor floor temperature is to be inverted; Step 2: Based on the observation data, multivariate meteorological elements are selected from the reanalysis data as substitutes for indoor floor temperature, and then the selected multivariate meteorological elements are corrected; Step 3: First, the multivariate meteorological elements corrected in step 2 are established as a multivariate linear regression fitting model by the method of multivariate linear regression analysis, and then the indoor floor temperature is inverted using the multivariate linear regression fitting model. The formula of the multivariate linear regression fitting model is: T 室内地板 =0.1589*T 室外 +0.3345*Td 订正 +0.0071*Rh 订正 +10.6276 Among them, T 室内地板 is the indoor floor temperature, T 室外 is the outdoor temperature, Td 订正 To fit the site dew point temperature data, Rh 订正 is the fitted site relative humidity.

2. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: In the step 1, the observation data includes the site dew point temperature, the site outdoor air temperature and the site relative humidity.

3. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: In the step 1, the reanalysis data includes reanalysis dew point temperature, reanalysis outdoor air temperature and reanalysis relative humidity.

4. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: In the step 2, the multivariate meteorological elements include outdoor temperature, dew point temperature and relative humidity, and the outdoor temperature is replaced by the ground 2m temperature of the reanalysis data.

5. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: After the multivariate meteorological elements are corrected, the fitted site dew point temperature data and the fitted site relative humidity are obtained. The fitted site dew point temperature data are obtained by fitting the reanalysis dew point temperature and the site dew point temperature, and the fitted site relative humidity is obtained by fitting the reanalysis relative humidity and the site relative humidity.

6. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: In the step 2, uncertain factors are eliminated during the establishment of the multivariate linear regression fitting model, and the uncertain factors include topography and floor material.

7. The indoor floor temperature inversion method based on the combination of observation and reanalysis data according to claim 1 is characterized in that: In the step three, the return of the south wind phenomenon is determined based on the indoor floor temperature calculated by fitting and the dew point temperature of the day, and the determination standard is: when the indoor floor temperature minus the dew point temperature is less than 1°C, it is determined that the return of the south wind phenomenon has occurred.

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