Method and system for evaluating the influence of heat waves on global navigation satellite system positioning accuracy

Abstract

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

Description

TECHNICAL FIELD

[0001] The application belongs to the field of heat wave event analysis, and particularly relates to a method and system for evaluating the influence of heat waves on the positioning accuracy of a global navigation satellite system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Under the background of global warming, the frequency and intensity of heat waves have significantly increased. Extreme climate events are often accompanied by abnormalities in the land-atmosphere system, and therefore, it is of great scientific significance to study the abnormal changes in land-atmosphere parameters during heat waves. Soil moisture plays a key role in the land-atmosphere coupling process and is a research focus in the field of meteorology. Atmospheric water vapor is also an important parameter of the climate system, which can affect climate change by amplifying the "greenhouse effect" of carbon dioxide. In addition, atmospheric water vapor can also be used to improve the positioning accuracy of the global navigation satellite system (GNSS), which is of great significance to high-precision navigation and positioning. The total electron content of the ionosphere, as a key parameter representing the state of the ionosphere, directly affects the estimation accuracy of the ionospheric delay error of GNSS.

[0004] Most current research focuses on the causes of heat waves and their impact on human health, ecosystems and the economy, with few studies on the abnormal changes in land-atmosphere parameters during heat waves. There is no research on the abnormal changes in the total electron content of the ionosphere during heat waves, and there is no research on the impact of heat waves on the positioning accuracy of GNSS, which is not conducive to improving the reliability of navigation under extreme weather and enhancing the robustness of high-precision positioning services. SUMMARY

[0005] To solve the above problems, the present application provides a method and system for evaluating the influence of heat waves on the positioning accuracy of a global navigation satellite system. Based on multi-source data, the present application establishes a multi-parameter collaborative analysis framework for soil moisture (Soil Moisture, Soil W), atmospheric precipitable water vapor (Precipitable Water Vapor, PWV) and the total electron content of the ionosphere (Total Electron Content, TEC) during heat wave events, enriches the research content of heat wave events from a new perspective, evaluates the influence of heat wave events on the positioning accuracy of GNSS, and helps users understand the changes in the positioning accuracy of GNSS under extreme weather, so as to take appropriate improvement measures in advance.

[0006] According to some embodiments, the present application adopts the following technical solutions:

[0007] An evaluation method of heat wave influence on global navigation satellite system positioning accuracy, comprising the following steps:

[0008] Determine the research area and research year of the target heat wave event, and determine the corresponding climate state according to the research year;

[0009] Obtain the air temperature data of each meteorological station in the research area, and interpolate and supplement the missing data;

[0010] According to the interpolated and supplemented data, determine the research period of the heat wave;

[0011] Obtain the two-meter-above-ground temperature and soil moisture grid data, and analyze the spatial distribution of the daily average values of the two-meter-above-ground temperature and soil moisture in the research period of the research area;

[0012] Using the atmospheric precipitable water data and the hourly air temperature data of each station, analyze the hourly air temperature and atmospheric precipitable water hourly anomaly value in the research period, and explain the reason for the atmospheric precipitable water anomaly by analyzing the corresponding relationship between the atmospheric long-wave radiation and the atmospheric precipitable water hourly value;

[0013] Using the ionospheric total electron content grid data, bilinearly interpolate the ionospheric total electron content data to the meteorological station, and analyze the hourly air temperature and ionospheric total electron content hourly anomaly value in the research period;

[0014] Using the GNSS station related data, compare the differences in global navigation satellite system positioning accuracy between the heat wave period and the non-heat wave period in the research period, and obtain the evaluation result.

[0015] As an optional implementation, the process of obtaining the air temperature data of each meteorological station in the research area and interpolating and supplementing the missing data includes:

[0016] Obtain the daily air temperature data of each meteorological station in the research area in the research year and the climate state summer, determine the maximum air temperature and the minimum air temperature, and calculate the maximum air temperature and the minimum air temperature of each day in the research year and the climate state summer according to the two-meter-above-ground temperature hourly data of the reanalysis data;

[0017] Remove the meteorological stations with data missing values greater than a set proportion, and bilinearly interpolate the maximum air temperature and the minimum air temperature calculated by the reanalysis data to the corresponding meteorological stations for the remaining meteorological stations;

[0018] Fit the linear relationship of the non-missing data of each meteorological station and the corresponding interpolated two-meter-above-ground temperature data, and calculate the supplementary value of the missing data of each meteorological station combined with the linear relationship and the interpolated two-meter-above-ground temperature data.

[0019] As an alternative embodiment, the process of determining the study period of heat wave according to the interpolated data comprises:

[0020] The daily extreme high temperature threshold of each meteorological station in the study year is calculated using the climate data in the set time period, and the days exceeding the corresponding extreme high temperature threshold are defined as high temperature days. The heat wave is constituted by the consecutive set days and above reaching the high temperature day standard, and the regional heat wave is defined when the heat wave covers the study area exceeding the set value;

[0021] The time period when the regional heat wave occurs is selected and extended by n days before and after as the study period for subsequent parameter analysis.

[0022] As a further embodiment, the value of n needs to ensure that there is no regional heat wave after extension.

[0023] As an alternative embodiment, the process of analyzing the abnormal value spatial distribution of the two-meter above ground temperature and soil moisture daily average value in the study period of the study area comprises:

[0024] Using the two-meter above ground temperature and soil moisture hourly data in the study period, the daily average value is calculated, and then the daily average climate value of the same period in the study year is subtracted, and finally the daily average abnormal value is obtained;

[0025] Select k meteorological stations in the study area that are uniformly distributed in space, meet the heat wave standard in the study period, and do not meet the high temperature day standard in the same period of the study year before the study year;

[0026] The two-meter above ground temperature and soil moisture daily average value grid data are bilinearly interpolated to the above k meteorological stations, and the change of the two-meter above ground temperature and soil moisture daily average value in the study period is analyzed.

[0027] As an alternative embodiment, the process of analyzing the change of the hourly abnormal value of the air temperature and the atmospheric precipitable water in the study period comprises:

[0028] The GNSS-precipitable water data obtained with a time resolution of a set value is resampled to the whole hour time point of each day;

[0029] Select k GNSS sites with the most complete data near the meteorological stations, and use the atmospheric precipitable water data of the GNSS sites to replace the atmospheric precipitable water data of the meteorological stations;

[0030] The air column water vapor total amount grid data is bilinearly interpolated to the GNSS site, and the atmospheric precipitable water missing data is interpolated and supplemented using the air column water vapor total amount data;

[0031] Combined with the hourly air temperature data of the meteorological station, the hourly abnormal value of the atmospheric precipitable water and the hourly air temperature of the k meteorological stations in the study period is analyzed.

[0032] The atmospheric long-wave radiation grid data is bilinearly interpolated to the weather stations, and the corresponding relationship between the atmospheric precipitable water and the atmospheric long-wave radiation of each hour in the study period is analyzed to explain the cause of the atmospheric precipitable water anomaly during the heat wave.

[0033] As an alternative implementation, the ionospheric total electron content grid data is used, the ionospheric total electron content data is bilinearly interpolated to the weather stations, and the process of analyzing the hourly anomaly value changes of the air temperature and the ionospheric total electron content of each hour in the study period includes:

[0034] The ionospheric total electron content and the atmospheric layer top incident solar radiation grid data are bilinearly interpolated to the k weather stations, and the hourly changes of the ionospheric total electron content and the atmospheric layer top incident solar radiation in the study period are analyzed to exclude the influence of solar activity on the change of the ionospheric total electron content during the heat wave.

[0035] In combination with the hourly air temperature data of the weather stations, the hourly changes of the ionospheric total electron content and the hourly air temperature of each hour in the study period at the k weather stations are analyzed.

[0036] To exclude the influence of the seasonal change rule on the daily change difference of the ionospheric total electron content, the average value of the same period in the preset years before the study year is deducted, and the hourly anomaly values of the ionospheric total electron content and the hourly air temperature of each hour in the study period at the k weather stations are analyzed.

[0037] As an alternative implementation, the process of obtaining the evaluation result by comparing the difference in the positioning accuracy of the global navigation satellite system during the heat wave period and the non-heat wave period in the study period includes:

[0038] Obtaining the observation value file, ephemeris and clock error file and antenna phase center correction file of the GNSS station in the study area, and calculating the GNSS station observation data file in the study period;

[0039] The accuracy of GNSS positioning is calculated every day in the study period, and the difference in positioning accuracy during the heat wave period and the non-heat wave period is compared.

[0040] An evaluation system for the influence of a heat wave on the positioning accuracy of a global navigation satellite system, comprising:

[0041] A research target determination module configured to determine a research area and a research year of a target heat wave event, and determine a corresponding climate state according to the research year;

[0042] A data preprocessing module configured to obtain air temperature data of each weather station in the research area, and interpolate and supplement missing data therein;

[0043] The research period determination module is configured to determine a research period of the heat wave according to the interpolated and supplemented data.

[0044] The soil moisture content anomaly analysis module is configured to obtain two-meter-above-ground temperature and soil moisture content grid data, and analyze the spatial distribution of abnormal values of the two-meter-above-ground temperature and the soil moisture content daily average values in the research period of the research area.

[0045] The atmospheric precipitable water anomaly analysis module is configured to analyze the changes of the hourly abnormal values of the air temperature and the atmospheric precipitable water in the research period by using the atmospheric precipitable water data and the hourly air temperature data of each station, and explain the reasons for the atmospheric precipitable water anomaly by analyzing the corresponding relationship between the atmospheric long-wave inverse radiation and the atmospheric precipitable water hourly value.

[0046] The ionosphere total electron content anomaly analysis module is configured to analyze the changes of the hourly abnormal values of the air temperature and the ionosphere total electron content in the research period by using the ionosphere total electron content grid data and bilinearly interpolating the ionosphere total electron content data to the weather stations.

[0047] The positioning accuracy influence evaluation module is configured to compare the differences between the global navigation satellite system positioning accuracy in the heat wave period and the non-heat wave period in the research period by using the GNSS station related data, and obtain an evaluation result.

[0048] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are run by the processor, the steps in the above method are completed.

[0049] Compared with the prior art, the beneficial effects of the present application are:

[0050] The present application uses multi-source data to establish a multi-parameter collaborative analysis framework of soil moisture content, atmospheric precipitable water and ionosphere total electron content in heat wave events, which to some extent reveals the coupling relationship between land-atmosphere multi-parameters during heat waves, and is expected to further provide a reference for heat wave forecasting. In addition, the present application also evaluates the influence of heat wave on GNSS positioning accuracy, helps users understand the changes of GNSS positioning accuracy under extreme weather, and takes corresponding improvement measures in advance.

[0051] In order to make the above-mentioned purposes, characteristics and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are used for explanation. BRIEF DESCRIPTION OF DRAWINGS

[0052] The drawings accompanying the specification of the present application form a part of the present application, and are used to provide a further understanding of the present application, the illustrative embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation of the present application.

[0053] Figure 1 Figure 1 is a schematic diagram of a heatwave research period selection process according to an embodiment;

[0054] Figure 2 Figure 2 is a schematic diagram of a multi-parameter analysis process according to an embodiment;

[0055] Figure 3 Figure 3 is a schematic diagram of a heatwave impact on GNSS positioning accuracy evaluation process according to an embodiment;

[0056] Figure 4 Figure 4 is a schematic diagram of T2M and SoilW daily average variation of meteorological stations from August 16-30, 2023 according to an embodiment, wherein (a) SP000004452, (b) SP000009434, (c) SPE00120035, (d) SPE00120233, (e) SPE00120242, (f) SPE00120296, (g) SPE00120359, and (h) SPE00120593;

[0057] Figure 5 Figure 5 is a schematic diagram of TMP and PWV hourly anomalies of meteorological stations from August 16-30, 2023 according to an embodiment, wherein (a) SP000004452, (b) SP000009434, (c) SPE00120035, (d) SPE00120233, (e) SPE00120242, (f) SPE00120296, (g) SPE00120359, and (h) SPE00120593;

[0058] Figure 6 Figure 6 is a schematic diagram of PWV and STRD hourly variation of meteorological stations from August 16-30, 2023 according to an embodiment, wherein (a) SP000004452, (b) SP000009434, (c) SPE00120035, (d) SPE00120233, (e) SPE00120242, (f) SPE00120296, (g) SPE00120359, and (h) SPE00120593;

[0059] Figure 7 Figure 7 is a schematic diagram of TEC, TISR, and TMP hourly variation of meteorological station SP000004452 from August 16-30, 2023 according to an embodiment, wherein (a) TEC, TISR, (b) TMP;

[0060] Figure 8is a TEC and TMP hourly outliers of meteorological stations for the period 16-30 August 2023 in an embodiment, wherein (a) SP000004452, (b) SP000009434, (c) SPE00120035, (d) SPE00120233, (e) SPE00120242, (f) SPE00120296, (g) SPE00120359 and (h) SPE00120593;

[0061] Figure 9 is the RMSE daily values of PWV hourly changes and precise point positioning (PPP) of GNSS stations of MADR in Spain for the period 16-30 August 2023 in an embodiment. DETAILED DESCRIPTION

[0062] The application will be further described with reference to the drawings and embodiments.

[0063] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, 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 application belongs.

[0064] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be noted that the terms "comprises", "comprising", "includes", "including", "contains", "containing" or variations thereof do not specify an exhaustive inclusion but rather are intended to allow for the inclusion of additional steps, features, components, elements, or the like.

[0065] The embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0066] Embodiment One

[0067] An evaluation method of the influence of heat wave on the positioning accuracy of global navigation satellite system, comprising the following steps:

[0068] Determine the research area and research year of the target heat wave event, and determine the corresponding climate state according to the research year;

[0069] Obtain the air temperature data of each meteorological station in the research area, and interpolate and supplement the missing data;

[0070] According to the interpolated and supplemented data, determine the research period of the heat wave;

[0071] Obtain grid data of temperature and soil moisture content two meters above the ground surface, and analyze the spatial distribution of outliers of the daily average values ​​of temperature and soil moisture content two meters above the ground surface in the study area during the study period.

[0072] Using atmospheric precipitable water data and hourly air temperature data from various stations, we analyzed the changes in hourly air temperature and hourly precipitable water anomalous values ​​during the research period, and explained the causes of atmospheric precipitable water anomalous values ​​by analyzing the correspondence between atmospheric longwave back radiation and hourly atmospheric precipitable water values.

[0073] Using gridded data of total ionospheric electron content, the total ionospheric electron content data was bilinearly interpolated to meteorological stations to analyze the changes in hourly air temperature and hourly outliers of total ionospheric electron content during the study period.

[0074] Using relevant GNSS station data, the differences in positioning accuracy of the Global Navigation Satellite System during heatwave and non-heatwave periods within the study period were compared, and the evaluation results were obtained.

[0075] The following is a detailed introduction to each step.

[0076] In this embodiment, as Figure 1 As shown, the data obtained is from NOAA weather stations.

[0077] Currently, data from other legitimate and authoritative weather stations can also be obtained.

[0078] First, the study area and study year are determined, and the corresponding climatology is determined based on the study year (30 years in this example). Daily Tmax and Tmin data for the summer (June-August) of each NOAA weather station within the study area are selected for the study year and climatology. Then, based on hourly data of 2-meter temperature (T2M) above the surface from ERA5, the daily maximum temperature T2M_max and minimum temperature T2M_min for the summer (June-August) of the study year and climatology are calculated. Next, weather stations with more than 10% missing NOAA data are removed. For the retained weather stations, bilinear interpolation of T2M_max and T2M_min from ERA5 (using data from four grid points surrounding the weather station) is performed. Subsequently, a linear relationship is fitted between the missing NOAA data and the corresponding interpolated T2M data for each weather station. Supplementary values ​​for missing NOAA data at each weather station are calculated by combining the linear relationship and the interpolated T2M data. The interpolated maximum temperature Tmax_combined and minimum temperature Tmin_combined of the weather station are used for selecting the time period in subsequent heat wave studies.

[0079] Similarly, ERA5 data can be replaced by other legitimate and authoritative sources.

[0080] In the process of heat wave research period selection, the climatological temperature data of the meteorological station is used to calculate the 95th percentile threshold of the extreme high temperature of each meteorological station in the study area in summer. Then, the data of each meteorological station in the summer of the study year is analyzed. For a certain meteorological station, when Tmax_combined and Tmin_combined are both greater than the extreme high temperature threshold, the day is defined as a high temperature day. If a heat wave lasts for 3 days or more, it is defined as a regional heat wave when the heat wave covers more than 5% of the study area. Finally, the time period of regional heat wave is selected and extended by n days (if no regional heat wave occurs, it is convenient for subsequent comparative analysis) as the research period for subsequent parameter analysis.

[0081] As shown in Figure 2 , the anomaly analysis of SoilW during the heat wave period:

[0082] For the anomaly analysis of SoilW during the heat wave period, first, the hourly data of T2M and SoilW in the research period is used to calculate the daily average value, and then the daily average climate value of the same period in the previous 5 years is subtracted to obtain the daily average anomaly value. Secondly, select k meteorological stations in the study area that are evenly distributed in space and meet the heat wave criteria in the research period and do not meet the high temperature criteria in the same period of the previous 5 years. Finally, the T2M and SoilW daily average value grid data is bilinearly interpolated to the k meteorological stations, and the change of T2M and SoilW daily average value in the research period is analyzed.

[0083] Anomaly analysis of PWV during the heat wave period:

[0084] For the anomaly analysis of PWV during the heat wave period, first, the GNSS-PWV data of NGL with a time resolution of 5 minutes is resampled to the whole point time every day. Then, select k GNSS stations near the meteorological station with the most complete data, and use the PWV data of the GNSS station to replace the PWV data of the meteorological station. Then, the TCWV grid data of ERA5 is bilinearly interpolated to the GNSS station, and the total column water vapor (TCWV) data is used to interpolate and supplement the missing PWV data. Then, combined with the TMP data of NOAA meteorological station, the hourly anomaly value of k meteorological stations PWV and TMP relative to the average value of the same period in 2018-2022 is analyzed. Finally, the Surface Thermal Radiation Downwards (STRD) grid data is bilinearly interpolated to the meteorological station, and the hourly correspondence between k meteorological stations PWV and STRD in the research period is analyzed to explain the reason for the anomaly of PWV during the heat wave period.

[0085] Anomaly analysis of TEC during the heat wave period:

[0086] For the analysis of TEC anomalies during heatwaves, first, the TEC and Top Of the Atmosphere Incident Solar Radiation (TISR) grid data are bilinearly interpolated to k weather stations, and the hourly changes of TEC and TISR during the study period are analyzed to exclude the influence of solar activity on TEC changes during heatwaves. Second, combined with the TMP data of NOAA, the hourly changes of TEC and TMP at k weather stations during the study period are analyzed. Finally, to exclude the difference in daily variation of TEC affected by seasonal variation, the average value of the same period in the previous 5 years is deducted, and the hourly anomaly values of TEC and TMP at k weather stations during the study period are analyzed.

[0087] As shown in Figure 3 , the evaluation of the influence of heatwaves on GNSS positioning accuracy:

[0088] First, the observation value file (o file), ephemeris and clock error file (sp3 file and clk file), antenna phase center correction file (atx file) of GNSS stations in the study area are obtained, and the observation data file of GNSS stations is calculated in the study period by using RTKLIB software. Then, the accuracy of GNSS positioning in each day of the study period is analyzed, and the difference between the positioning accuracy during heatwaves and that during non-heatwaves is compared.

[0089] This embodiment verifies the feasibility of the above method by taking the heatwave in Spain in 2023 as an example. The study period of the example is August 16-30, 2023, m=15, n=5, and k=8. The GNSS positioning method used in the example is precise point positioning (PPP), and the GNSS station selected is MADR station. Table 1 shows the names, latitudes, longitudes, and elevations of the 8 weather stations, and Table 2 shows the corresponding GNSS station information.

[0090] Table 1 Names, latitudes, longitudes, and elevations of 8 weather stations

[0091]

[0092] Table 2 Names, corresponding weather stations, and distances from corresponding weather stations of 8 GNSS stations

[0093]

[0094] The analysis results of this embodiment are as follows:

[0095] SoilW anomalies during heatwaves:

[0096] As a typical example, the anomalies of T2M and SoilW in Spain from August 16 to 30, 2023 are obtained, combined with the average values of the same period from 2018 to 2022, and analyzed, which can be obtained: The anomalies of T2M and SoilW daily average values from August 16 to 30, 2023 show a certain correlation in spatial distribution, and their evolution can be roughly divided into three time periods. From August 16 to 20, 2023, the daily average value of T2M in most areas of Spain is about 3-5℃ higher than the average value of the same period from 2018 to 2022. In particular, from August 16 to 18, 2023, the daily average value of T2M in the southwest of Spain is about 3℃ lower. From August 16 to 20, 2023, the SoilW in the southwest of Spain is about 0.15 / , the SoilW in the east and north of Spain is about 0.15-0.2 / lower. From August 21 to 25, 2023, the range of T2M daily average value anomaly is expanded, and the anomaly value is increased to about 5-7℃. At the same time, the range of SoilW daily average value anomaly is expanded, and the SoilW in the southwest of Spain is about 0.15 / lower. From August 26 to 30, 2023, the daily average value of T2M in most areas of Spain is abnormally low, and the range of SoilW daily average value anomaly is expanded, and the SoilW in the north of Spain is about 0.2-0.25 / higher. In summary, the soil is the driest during the heat wave concentrated period from August 21 to 25, 2023.

[0097] The changes of T2M and SoilW daily average values of 8 weather stations from August 16 to 30, 2023 are analyzed, as shown in Figure 4 , it can be seen that the changes of SoilW and T2M daily average values show a negative correlation. When the daily average value of T2M of each weather station is greater than 25℃ from August 21 to 23, 2023, the daily average value of SoilW is low in its own time series, but there is a large difference in the low value of SoilW daily average value of different weather stations, because the underlying surface of different weather stations is different, and the soil water storage capacity is different. Through analysis, it can be seen that for the same area, the soil moisture in the high temperature period is relatively small than in the low temperature period. Because when the soil is dry, the soil evaporation decreases, the surface latent heat flux decreases, and the surface sensible heat flux increases. The surface sensible heat flux can directly heat the atmosphere, and its effect is more obvious than the surface latent heat flux.

[0098] Anomaly of PWV during heat wave:

[0099] From Figure 5It can be seen that the hourly anomalies of PWV and TMP generally show similar changes, and the TMP anomalies from August 16 to 25, 2023 are about 5-10℃ higher than those from August 27 to 30, 2023, and the corresponding PWV anomalies are about 5-20 mm higher. That is, the PWV anomalies are higher during the heat wave.

[0100] Atmospheric water vapor is an important component of the atmosphere and is closely related to atmospheric long-wave radiation. Therefore, we analyzed the hourly correspondence between PWV and STRD at 8 weather stations from August 16 to 30, 2023 to explain the reason for the higher PWV anomalies during the heat wave, as shown in Figure 6 It can be seen that PWV and STRD have a positive correlation, and they not only have similar diurnal fluctuation changes, but also have larger STRD when PWV is larger from August 16 to 25, 2023. STRD is an important component of the energy input to the surface, which can affect the surface temperature, and the near-surface air mainly relies on absorbing long-wave radiation from the surface to warm up. Especially at night without solar radiation, the influence of STRD on the surface temperature becomes larger. Therefore, in general, the atmospheric water vapor content is higher during the high temperature period. However, it is worth noting that the PWV anomalies of weather stations SP000004452, SPE00120233, SPE00120296 and SPE00120593 on August 19, 2023 are about 5-15 mm higher than those of the previous and subsequent periods, but their TMP anomalies are not much different from those of the previous and subsequent periods, as shown in Figure 5 It may be because when PWV is too high, water vapor is easy to reach saturation state to form precipitation, thereby increasing the soil moisture content. Combined with the previous analysis, when the soil moisture content is high, the surface evapotranspiration increases and the surface latent heat flux release is also high. Therefore, although the STRD may be larger when the PWV is high, the surface loses more energy under the action of the surface latent heat flux.

[0101] TEC anomalies during the heat wave:

[0102] After analyzing the TEC of each weather station in Spain from August 16 to 30, 2023, it was found that the TEC change trends of each weather station are roughly the same. Figure 7 is the hourly change of TEC from August 16 to 30, 2023 at weather station SP000004452 and the corresponding hourly changes of TISR and TMP. It can be seen that the daily TEC has a rising and then falling change trend, which is the same as the change trend of TISR. The peak time of TEC roughly corresponds to the peak time of TISR or there is a delay of about 2h, as shown in Figure 7The energy source of ionospheric ionization is mainly solar radiation, so the ionospheric electron content generally presents a change rule of high in the daytime and low at night. However, by analyzing the changes of the daily maximum values of TEC and TISR, it is found that the daily maximum values of TISR present a gradually decreasing trend from August 16 to 30, 2023, and the maximum change is small, which is obviously different from the change trend of the daily maximum values of TEC. It can be seen from Figure 7 (b) that the change trend of the daily maximum values of TEC has a good corresponding relationship with the change trend of the daily maximum values of TMP, and the daily maximum values of TEC during the concentrated time period of the heat wave in Spain from August 21 to 24, 2023 are about 10 TECU larger than the daily maximum values of TEC from August 26 to 28, 2023.

[0103] Figure 8 The figure shows the hourly anomaly values of TEC and TMP of 8 weather stations from August 16 to 30, 2023. It can be seen that compared with the average values from 2018 to 2022, the TEC in 2023 is generally abnormally high, because the solar activity in 2023 is more intense than that from 2018 to 2022, and the TEC is correspondingly higher. However, in order to compare the TEC differences between high temperature days and low temperature days, we only focus on the TEC increments of high temperature days and low temperature days. By comparing and analyzing the anomaly values of TEC, it is found that the TEC increment when TMP is greater than the average value is larger than the TEC increment when TMP is less than the average value, and the change trend of the anomaly values of TMP and TEC has a good corresponding relationship. That is to say, the TEC during the heat wave has an abnormal increase, which may be related to the effects of gravity waves and planetary waves on the ionosphere during the heat wave.

[0104] Evaluation of the influence of heat wave on GNSS positioning accuracy:

[0105] After analyzing the daily RMSE of precise point positioning (PPP) from August 16 to 30, 2023, it is found that compared with non-heat wave periods, the positioning accuracy will decrease during the heat wave. It can be seen from Figure 9 that except for August 22, August 23 and August 26, when the PWV of the station is high, the corresponding RMSE is also large. On August 19, the PWV of the station is 15 mm higher than that from August 27 to 29, and the corresponding RMSE is about 19 cm. According to the previous analysis, the PWV will increase significantly during the heat wave, and the increase of atmospheric water vapor content will lead to the increase of tropospheric delay error, resulting in the decrease of positioning accuracy. In summary, the loss of positioning accuracy during the heat wave is about 10 cm.

[0106] Example two

[0107] An evaluation system for the influence of heat wave on GNSS positioning accuracy, comprising:

[0108] The research target determination module is configured to determine a research region and a research year of a target heat wave event, and determine a corresponding climate state according to the research year.

[0109] The data preprocessing module is configured to obtain air temperature data of each meteorological station in the research region, and interpolate and supplement missing data in the air temperature data.

[0110] The research period determination module is configured to determine a research period of the heat wave according to the interpolated and supplemented data.

[0111] The soil moisture content anomaly analysis module is configured to obtain two-meter temperature above the ground and soil moisture content grid data, and analyze spatial distribution of abnormal values of daily average values of the two-meter temperature above the ground and the soil moisture content in the research period of the research region.

[0112] The atmospheric precipitable water anomaly analysis module is configured to analyze changes of hourly abnormal values of the hourly air temperature and the atmospheric precipitable water in the research period by using atmospheric precipitable water data and hourly air temperature data of each station, and explain reasons for the atmospheric precipitable water anomaly by analyzing a corresponding relationship between atmospheric long-wave inverse radiation and the hourly value of the atmospheric precipitable water.

[0113] The ionosphere total electron content anomaly analysis module is configured to bilinearly interpolate ionosphere total electron content data to meteorological stations by using ionosphere total electron content grid data, and analyze changes of hourly abnormal values of the hourly air temperature and the ionosphere total electron content in the research period.

[0114] The positioning accuracy influence evaluation module is configured to compare differences in global navigation satellite system positioning accuracy in the heat wave period and the non-heat wave period in the research period by using GNSS station related data.

[0115] Embodiment three

[0116] A computer readable storage medium for storing computer instructions, the computer instructions being executed by a processor to complete the steps in the method provided in embodiment one.

[0117] Embodiment four

[0118] An electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, the computer instructions being executed by the processor to complete the steps in the method provided in embodiment one.

[0119] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, various software modules in accordance with embodiments of the present application are stored in a memory such as a computer program product (e.g., a disk storage) and executed by a computer processor. As such, various computer program products code, when executed, enable the computer to function as a special purpose computer programmed to carry out the steps described herein. The software modules, when executed, enable the computer to provide various example embodiments of the present application as discussed herein. CD - ROM The present application is described in reference to the drawings, which are as follows:

[0120] The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: The present application is described in reference to the drawings, which are as follows:

[0121] The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: The present application is described in reference to the drawings, which are as follows:

[0122] The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: Figure 1 The present application is described in reference to the drawings, which are as follows: The present application is described in reference to the drawings, which are as follows:

[0123] The present application is described in reference to the drawings, which are as follows: The above description is embodied in the context of the preferred embodiments of the application. The application is not restricted to the preferred embodiments and can be modified and changed by those skilled in the art without departing from the spirit and principles of the application. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative efforts should be included in the protection scope of the application.

Claims

1. A method for assessing the impact of heat waves on the positioning accuracy of global navigation satellite systems, characterized in that, Includes the following steps: Determine the study area and study year of the target heat wave event, and determine the corresponding climate state based on the study year; Temperature data from various meteorological stations within the study area were obtained, and missing data were interpolated to fill in the gaps. The study period for the heat wave was determined based on the interpolated data. Obtain grid data of temperature and soil moisture content two meters above the ground surface, and analyze the spatial distribution of outliers of the daily average values ​​of temperature and soil moisture content two meters above the ground surface in the study area during the study period. Using atmospheric precipitable water data and hourly air temperature data from various stations, we analyzed the changes in hourly air temperature and hourly precipitable water anomalous values ​​during the research period, and explained the causes of atmospheric precipitable water anomalous values ​​by analyzing the correspondence between atmospheric longwave back radiation and hourly atmospheric precipitable water values. Using gridded data of total ionospheric electron content, the total ionospheric electron content data was bilinearly interpolated to meteorological stations to analyze the changes in hourly air temperature and hourly outliers of total ionospheric electron content during the study period. Using relevant GNSS station data, the differences in positioning accuracy of the Global Navigation Satellite System during heatwave and non-heatwave periods within the study period were compared, and the evaluation results were obtained. The process of determining the study period of the heat wave based on the interpolated data includes: Using climatological data, the daily extreme high temperature thresholds for each meteorological station within a set time period in the study year are calculated. Days exceeding the corresponding extreme high temperature threshold are defined as high temperature days. A heat wave is defined when the number of consecutive set days or more reaches the high temperature day standard. A heat wave is defined as a regional heat wave when it covers the study area exceeding the set value. The study period for subsequent parameter analysis is determined by selecting the time period of the heat wave that occurred in the region and extending it forward and backward by n days. Using gridded data of total ionospheric electron content, the data is bilinearly interpolated to meteorological stations. The process of analyzing the hourly changes in air temperature and hourly outliers of total ionospheric electron content over a given period includes: The total ionospheric electron content and the solar radiation incident at the top of the atmosphere were bilinearly interpolated to k meteorological stations to analyze the hourly variations of the total ionospheric electron content and the solar radiation incident at the top of the atmosphere during the study period, excluding the influence of solar activity on the variation of the total ionospheric electron content during heat waves. By combining hourly air temperature data from meteorological stations, we analyze the hourly changes in total ionospheric electron content and hourly air temperature at k meteorological stations during the study period. To rule out the possibility that the diurnal variation in total ionospheric electron content is influenced by seasonal patterns, after deducting the average value of the same period in the previous year, we analyzed the hourly anomalies of total ionospheric electron content and hourly air temperature at k meteorological stations during the study period.

2. The method for assessing the impact of heat waves on the positioning accuracy of a global navigation satellite system as described in claim 1, characterized in that, The process of acquiring temperature data from various meteorological stations within the study area and interpolating to fill in missing data includes: Obtain daily temperature data for summer in the study year and climatological conditions from meteorological stations within the study area, determine the highest and lowest temperatures, and calculate the daily highest and lowest temperatures for summer in the study year and climatological conditions based on hourly temperature data two meters above the surface from the reanalysis data. Remove weather stations with missing data values ​​exceeding a set proportion. For the retained weather stations, bilinearly interpolate the highest and lowest temperatures calculated from the reanalysis data to the corresponding weather stations. The linear relationship between the missing data from each meteorological station and the corresponding interpolated temperature data two meters above the ground surface is fitted. The supplementary values ​​of the missing data from each meteorological station are calculated by combining the linear relationship and the interpolated temperature data two meters above the ground surface.

3. The method for assessing the impact of heat waves on the positioning accuracy of a global navigation satellite system as described in claim 1, characterized in that, The value of n needs to ensure that no regional heat waves occur after the extension.

4. The method for assessing the impact of heat waves on the positioning accuracy of global navigation satellite systems as described in claim 1, characterized in that, The process of analyzing the spatial distribution of outliers in the daily average values ​​of temperature and soil moisture content two meters above the ground in the study area during the study period includes: The daily average values ​​were calculated by using hourly data of temperature two meters above the surface and soil moisture content during the study period. The daily average climate values ​​of the same period in the previous year were then subtracted to obtain the daily average anomaly values. Select k meteorological stations in the study area that are spatially evenly distributed, reach the heat wave standard during the study period, and have no high-temperature days in the same period of the previous year. The daily average values ​​of temperature two meters above the ground and soil moisture content were bilinearly interpolated to the above k meteorological stations to analyze the changes in the daily average values ​​of temperature two meters above the ground and soil moisture content during the study period.

5. The method for assessing the impact of heat waves on the positioning accuracy of a global navigation satellite system as described in claim 1, characterized in that, The process of analyzing the changes in hourly anomalies of air temperature and atmospheric precipitable water during the study period includes: The acquired GNSS-atmospheric precipitable water data with a set time resolution are resampled to the hourly time of each day. Select k GNSS stations with the most complete data near the meteorological stations, and use the atmospheric precipitable water data of these GNSS stations to replace the atmospheric precipitable water data of the meteorological stations. The total atmospheric water vapor content grid data was bilinearly interpolated to GNSS stations, and the total atmospheric water vapor content data was used to interpolate and supplement the missing atmospheric precipitable water content data. By combining hourly air temperature data from meteorological stations, we analyze and study the hourly anomalies of atmospheric precipitable water and hourly air temperature at k meteorological stations during the research period. By bilinearly interpolating atmospheric longwave back radiation grid data to meteorological stations, and analyzing the hourly correspondence between atmospheric precipitable water and atmospheric longwave back radiation at k meteorological stations during the study period, the cause of atmospheric precipitable water anomalies during heat waves can be explained.

6. The method for assessing the impact of heat waves on the positioning accuracy of a global navigation satellite system as described in claim 1, characterized in that, The process of comparing the differences in positioning accuracy of the Global Navigation Satellite System during heatwave and non-heatwave periods using relevant GNSS station data to obtain the evaluation results includes: Obtain the observation data files, ephemeris and clock error files, and antenna phase center correction files of GNSS stations within the study area, and perform calculations on the GNSS station observation data files during the study period; The accuracy of daily GNSS positioning during the study period was calculated, and the difference in positioning accuracy during heat waves and non-heat waves was compared.

7. An assessment system for the impact of heat waves on the positioning accuracy of global navigation satellite systems, characterized in that, include: The research objective determination module is configured to determine the research area and research year of the target heat wave event, and determine the corresponding climate state based on the research year. The data preprocessing module is configured to acquire temperature data from various meteorological stations within the study area and interpolate and supplement any missing data. The study period determination module is configured to determine the study period of the heat wave based on the interpolated supplemented data; The soil moisture content anomaly analysis module is configured to acquire grid data of temperature and soil moisture content two meters above the ground surface, and analyze the spatial distribution of anomalies in the daily average values ​​of temperature and soil moisture content two meters above the ground surface in the study area during the study period. The atmospheric precipitable water anomaly analysis module is configured to use atmospheric precipitable water data and hourly air temperature data from each station to analyze the changes in hourly air temperature and hourly atmospheric precipitable water anomaly values ​​during the study period, and to explain the causes of atmospheric precipitable water anomalies by analyzing the correspondence between atmospheric longwave back radiation and hourly atmospheric precipitable water values. The total ionospheric electron content anomaly analysis module is configured to use total ionospheric electron content grid data to bilinearly interpolate the total ionospheric electron content data to meteorological stations, and analyze the changes in hourly air temperature and hourly anomaly values ​​of total ionospheric electron content during the study period. The positioning accuracy impact assessment module is configured to use relevant GNSS station data to compare the differences in global navigation satellite system positioning accuracy during heatwave and non-heatwave periods within the study period, and obtain assessment results. The process of determining the study period of the heat wave based on the interpolated data includes: Using climatological data, the daily extreme high temperature thresholds for each meteorological station within a set time period in the study year are calculated. Days exceeding the corresponding extreme high temperature threshold are defined as high temperature days. A heat wave is defined when the number of consecutive set days or more reaches the high temperature day standard. A heat wave is defined as a regional heat wave when it covers the study area exceeding the set value. The study period for subsequent parameter analysis is determined by selecting the time period of the heat wave that occurred in the region and extending it forward and backward by n days. Using gridded data of total ionospheric electron content, the data is bilinearly interpolated to meteorological stations. The process of analyzing the hourly changes in air temperature and hourly outliers of total ionospheric electron content over a given period includes: The total ionospheric electron content and the solar radiation incident at the top of the atmosphere were bilinearly interpolated to k meteorological stations to analyze the hourly variations of the total ionospheric electron content and the solar radiation incident at the top of the atmosphere during the study period, excluding the influence of solar activity on the variation of the total ionospheric electron content during heat waves. By combining hourly air temperature data from meteorological stations, we analyze the hourly changes in total ionospheric electron content and hourly air temperature at k meteorological stations during the study period. To rule out the possibility that the diurnal variation in total ionospheric electron content is influenced by seasonal patterns, after deducting the average value of the same period in the previous year, we analyzed the hourly anomalies of total ionospheric electron content and hourly air temperature at k meteorological stations during the study period.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method according to any one of claims 1-6.

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