AU2021105536A4 - A High Spatial-Temporal Resolution Method for Near-Surface Air Temperature Reconstruction - Google Patents
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Abstract
The invention belongs to the technical field of temperature estimation, A high spatial
temporal resolution near-surface air temperature reconstruction method is disclosed, The
construction method of the near-surface air temperature model with high spatial and temporal
resolution comprises the following steps: Firstly, the weather from 1979 to 2018 is divided into
sunny days and non-sunny days, Through the existing reanalysis data set, Using meteorological
station data, Combined with the hourly temperature data of weather stations driven by surface
meteorological elements in China and MODIS daily Ts, The maximum and minimum
temperatures are calculated by establishing daily maximum and minimum temperature models
under different weather conditions, and the daily average temperature data set is obtained by
adding and averaging. After linear regression correction, the daily near-surface temperature
data set of China from 1979 to 2018 is finally output, with a spatial resolution of 0.10. The data
set constructed by the invention can better estimate daily maximum temperature, minimum
temperature and average temperature, which is convenient for further analyzing seasonal and
periodic changes of regional temperature in China.
1/9
China is divided into six regions according to the natural /
geographical environment and climatic conditions
I-S102
The daily weather state is divided into sunny and non sunny
States, and the temperature is estimated
According to different weather conditions, the temperatureyS103
data models are constructed respectively
1S104
The linear regression correction of air temperature data
model is carried out respectively
Figure 1
Construction Area division module
system of high 2
temporal and Weatherstatedivisionmodule
spatial
resolution r3
near surface Temperature estimation module
air temperature
model
Temperature data model building module
Linear regression correction module 5
Figure 2
Description
1/9
China is divided into six regions according to the natural
/ geographical environment and climatic conditions
I-S102 The daily weather state is divided into sunny and non sunny States, and the temperature is estimated
According to different weather conditions, the temperatureyS103 data models are constructed respectively
1S104 The linear regression correction of air temperature data
model is carried out respectively
Figure 1
Construction Area division module system of high 2 temporal and Weatherstatedivisionmodule
spatial resolution r3 near surface Temperature estimation module air temperature model Temperature data model building module
Linear regression correction module 5
Figure 2
A High Spatial-Temporal Resolution Method for Near-Surface Air Temperature Reconstruction
[01] The invention belongs to the technical field of temperature estimation, in particular to a high space-time resolution near-surface air temperature reconstruction method.
[02] At present, temperature is an important physical quantity reflecting the degree of cold and heat and climate change. Understanding the real-time change of temperature is very important for the study of global warming trend, urban heat island effect, ecological environment change, vegetation phenology development, crop yield fluctuation and energy dynamic balance.
[03] (1) Among the existing monitoring methods using weather stations, because the distribution of weather stations in China is sparse and extremely uneven, the data of weather stations in the western region is less than one fifth of that in the eastern region, and most weather stations are located in sparsely populated areas far away from cities, so it is impossible to accurately monitor the urban temperature changes caused by heat island effect.
[04] (2) Single weather stations are distributed in the form of points, and their coverage is very small, which can not meet the needs of large-scale regional research and can not reflect the spatial differences of air temperature.
[05] (3) Weather station equipment is prone to aging and abnormal loss of instruments, which requires a lot of manpower and material resources to maintain and manage the weather station equipment.
[06] (4) Using the surface temperature obtained from remote sensing satellite data to infer the temperature can not eliminate the data deviation caused by the influence of cloud and rain and the different transit time of remote sensing satellite. TVX method faces some problems such as the difference of NDVI estimation monitored by different sensors.
[07] (5) Existing reanalysis data mostly focus on daily temperature values or other driving factors on the ground, but seldom study daily maximum and minimum temperature values, and the spatial resolution of reanalysis data is low.
[08] (6) At present, the estimation of near-surface air temperature is mostly based on interpolation of meteorological station data or remote sensing surface temperature data, which is still affected by sparse distribution and small number of stations, and cloud and rain weather reduces the accuracy of remote sensing images.
[09] In order to provide temporal and spatial continuity of near-surface air temperature and ensure the accuracy of drought monitoring and climate change model prediction, it is necessary to reconstruct the missing or inaccurate air temperature.
[010] An Aiming at the problems existing in the prior art, the invention provides a method, a system and equipment for constructing a near-surface air temperature model with high spatial and temporal resolution, in particular to a method for repairing and producing the highest temperature and the lowest temperature of near-surface air with high spatial and temporal resolution.
[011] The invention is realized in such a way that a method for constructing a near-surface air temperature model with high spatial-temporal resolution comprises the following steps:
[012] Firstly, the weather from 1979 to 2018 is divided into sunny days and non sunny days, Through the existing reanalysis data set, Using meteorological station data, Combined with the hourly temperature data of weather stations driven by surface meteorological elements in China and MODIS daily Ts, the daily maximum and minimum temperature models under different weather conditions are established, and the daily average temperature data set is obtained by adding and averaging. After linear regression correction, the daily near-surface temperature data set of China from 1979 to 2018 is finally output, with a spatial resolution of0.1.
[013] Further, the construction method of the high spatial-temporal resolution near-surface air temperature model comprises the following steps:
[014] Step 1, China is divided into six regions according to the natural geographical environment and climatic conditions;
[015] Step 2, dividing the daily weather state into sunny and non-sunny states, and estimating the temperature;
[016] Step 3 Constructing temperature data models according to different weather conditions;
[017] Step 4, linear regression correction processing of air temperature data model is carried out respectively.
[018] Further, in Step 1, China is divided into six regions according to natural geographical environment and climatic conditions, including (I) northeast region of temperate monsoon climate zone, (II) south of temperate monsoon climate zone, (III) subtropical monsoon climate zone, (IV) tropical monsoon climate zone, (V) temperate continental monsoon climate zone and (VI) plateau mountain climate zone.
[019] Among them, (I) the northeast of temperate monsoon climate is mainly Northeast China, which is located to the east of Daxing'anling. The annual precipitation is 400-1000 mm, which gradually decreases from east to west. The annual cumulative temperature is between 2500-4000 °C, the winter is cold and long, and the summer is hot and rainy; This area is an important commodity grain base in China. Crops are more sensitive to climate change and are extremely vulnerable to extreme weather events. (II) In the south of the monsoon temperate climate zone, the annual accumulated temperature is between 3000-4500°C, which is hot and rainy in summer and cold and dry in winter; Affected by monsoon, extreme weather disasters are more likely to occur. (III) Subtropical monsoon climate is south of Huaihe River in Qinling Mountains, north of tropical monsoon climate zone, east of Hengduan Mountains and Taiwan; The annual accumulated temperature is between 4500-8000°C, and the precipitation is mostly between 800-1600 mm. Summer is hot and winter is warm. (IV) Tropical monsoon climate is usually located south of Tropic of Cancer; The annual accumulated temperature is greater than 800°C, the annual minimum temperature is not lower than °C, there is no frost all the year round, and the annual precipitation is mostly 1500 2000 mm. (V) The temperate continental climate is mainly distributed in the inland areas above 40 degrees north latitude in China, located in the northwest of Daxing'anling-Yinshan-Hengduan Mountain line; Far from the coast, it is difficult to transport water vapor; The annual precipitation is between 300-500mm; The daily temperature difference and annual temperature difference are very large, including temperate desert climate, temperate grassland climate and sub-cold temperate coniferous forest climate. (VI) The climate of plateau and mountain area is mainly distributed in Qinghai-Tibet Plateau; The annual accumulated temperature is lower than 2000 °C, the daily temperature difference is large, the annual temperature difference is small, the solar radiation is strong, the sunshine is abundant, and the precipitation is less; Different from other climatic types, biodiversity is affected by latitude and altitude, and the climate in plateau and mountainous areas is mainly affected by altitude.
[020] Further, in step 2, the daily weather state is divided into sunny and non sunny states, comprising:
[021] Firstly, the daily weather phenomena are discriminated to determine the calculation method of daily temperature according to different weather conditions. Affected by cold front, cyclone circulation, Due to the influence of complex weather systems such as high and low pressure and thunderstorm, the occurrence time of daily maximum temperature and minimum temperature is aperiodic and uncertain, so the anomaly of weather conditions can be judged according to the occurrence time anomaly of daily maximum temperature, and then the daily weather phenomena during the study period can be divided into sunny and non-sunny states.
[022] Using statistical method and two strategies, the daily occurrence time of the highest temperature and the lowest temperature of each pixel is obtained, The first strategy is to determine the daily maximum temperature input parameters in areas where stations are densely distributed, that is, areas where the distance between adjacent stations is less than 30km. Four methods are used: 1) When the measured data of stations are complete and there is no abnormal value, the hourly station data are used to determine the occurrence time of daily maximum temperature and minimum temperature; 2) When there are missing values but discontinuous missing values in the measured data of the station, under the condition of the same space range, the temperature before and after the same station is used to fill and repair to determine the time when the daily maximum temperature appears; 3) When the observation data of stations are continuously missing, under the condition of the same time range, filling is carried out according to the time when the daily maximum value appears at adjacent stations to determine the time when the daily maximum temperature appears at the point. The method is based on the principle that the closer the distance between stations, the stronger the spatial consistency and correlation of temperature changes; 4) When the site data is continuously missing and the adjacent site data cannot be filled, other related data are used to repair in the same time and space range. According to the approximate consistency trend of daily surface temperature and air temperature, the hourly surface temperature of the same station is used to determine the daily maximum air temperature. This method is suitable for stations with too many missing values, no nearby stations near 30km of weather stations and incomplete time data before and after.
[023] The second strategy is to determine the occurrence time of the daily maximum temperature in the area where the stations are sparse and the Euclidean distance between two adjacent stations is greater than 30km. With the help of CMFD data and MODIS data, ERA5 data is downscaled to determine the occurrence time of daily maximum temperature and minimum temperature. The spatial resolution of ERA5 data is 30km, that of CMFD data is 0.10, and that of MODIS data is 1km. Firstly, the km grid of ERA5 is downscaled to 0.1° grid, and then the downscaled ERA5 data is traversed day by day to get the occurrence time of daily maximum and minimum temperature, and finally the occurrence time of daily maximum temperature in each region is output. The CMFD data is introduced to ensure the validity and integrity of the daily maximum time data inputted by the invention, and the MODIS data is introduced to improve the spatial resolution and refine the precision value.
[024] The calculation steps are as follows: Since MODIS can obtain four LST observations of 1km a day since 2002, the time series is divided into two stages: 1979 2001 and 2002-2018. Arrange daily ERA5 data and CMFD data according to time and the same central longitude and central latitude in the study period, and distribute hourly ERA5 data and three-hour CMFD data according to the near time; Each pixel of ERA5 is divided into the same pixel size as that of CMFD and each pixel corresponding to a single pixel of ERA5 in CMFD is regarded as a whole; After ERA5 segmentation, the spatial correlation between CMFD data and CMFD data is established, and the hourly ERA5 data is downscaled to 0.10by using the ratio of CMFD pixels to ERA5 corresponding pixels.
[025] After 2002, according to the correlation between air temperature and LST diurnal variation, ERA5 data was downscaled by CMFD data and MODIS data, and the accuracy of the results was tested. Since the CMFD data is obtained every three hours, the time corresponding to the daily maximum temperature and the daily minimum temperature is obtained by ERA5, and the temperature of each pixel of CMFD corresponding to the temporary time is used for spatial downscaling.
[026] Wherein, the calculation method and formula factors are expressed as follows:
TE~x., y.) TC (X., y.).
[027] 0 T(x.v,)
[028] Wherein, TE is EARA5 date, CMFD data is represented by Tc , and
MODIS data is represented by TM ;TE(Xy is the air temperature data after ERA5
data is downscaled at pixel position (x- Y) , Tc(x Mis the air temperature data of
CMFD at pixel position (°-°3, the sum of air temperature values of each pixel
position in the area where CMFD corresponds to ERA5 pixel is , and the air temperature corresponding to ERA5 original spatial resolution image is
[029] Further, in Step 2, the temperature estimation comprises:
[030] (1)Temperature estimation in sunny days
[031] Firstly, the approximate time of daily minimum temperature and maximum temperature is determined by statistical method, and the deduced piecewise sine function and the occurrence time of daily minimum temperature and maximum temperature are input into the function model as parameters; Secondly, based on the least square fitting method, the temperature of CMFD reanalysis data set is parameterized every three hours to obtain the daily maximum and minimum temperature curves, and finally the daily maximum and minimum temperature are output as preliminary results for subsequent correction and analysis.
[032] According to the approximate periodicity of daily temperature change and the asymmetry of the occurrence time of the maximum and minimum temperatures, the piecewise sine function curves around the occurrence time of the maximum and minimum temperatures are deduced by outputting the occurrence time of the maximum and minimum temperatures as parameters in the piecewise sine function; Using the least square method, the CMFD reanalysis data and the daily maximum and minimum temperature occurrence time are substituted into the equation, and the values of parameters A and B are obtained to construct a piecewise sine function. The daily maximum and minimum temperature occurrence time is substituted into the derivation formula again to output the daily maximum and minimum temperature.
[033] Wherein, the daily minimum temperature change function is:
T~,=A, *sin[T- 1 -]HB
[034]
[035] The daily maximum temperature change function is:
Tt=At * sin[ t-H.)x - ] +Br
[036] Hm - Hat 2
[037] Where Hminis the time when the lowest temperature occurs every day, and Hmax is the time when the highest temperature occurs every day. Because of the periodicity of temperature occurrence, the occurrence time of the daily minimum temperature of the next day is set to Hmin+24; According to the periodicity of sine function, the sine formulas of daily minimum temperature and daily maximum temperature are derived. At and Bt are unknown parameters.
[038] (2)Estimation of air temperature in non-sunny weather
[039] The daily maximum temperature, minimum temperature and average temperature measured by corresponding meteorological stations are used to fill in. The measured data have undergone strict quality control and evaluation, and the influence of altitude on temperature has been eliminated through topographic correction. When there is no corresponding weather station at the pixel position, ERA5 hourly temperature is used to reduce the spatial scale by using CMFD hourly data. For the downscaling process in non-sunny days, the hourly temperature data of ERA5 corresponding to the area without pixel position are traversed to find out the highest temperature and the lowest temperature in the pixel.
[040] (3) Estimation of daily average temperature
[041] Adding and averaging the corrected output daily maximum and minimum temperature data sets with eight daily temperature values of CMFD, The daily average temperature value is obtained and verified with the meteorological station data, and then the daily average temperature output value is corrected by multiple linear regression according to the meteorological station data to improve the accuracy, and finally the daily average temperature data set is output.
[042] Further, in Step 4, the linear regression correction processing of the air temperature data model is carried out, comprising:
[043] (1)Temperature data correction scheme
[044] Because the temperature is sensitive to the change of altitude and is easily affected by the surrounding environment, the data of various weather stations used have been highly corrected by the vertical attenuation rate of the average atmospheric temperature; Unify the observation data to the sea level height; The data correction or interpolation process is completed by the temperature corresponding to the sea level, and then corrected to its altitude; Use a unified standard, that is, for every 100 meters of altitude increase, the atmospheric temperature drops vertically by 0.65 °C, and vice versa; Wherein, the modified equation is as follows:
[045] TZL = Ts +0.0065H
[046] Where TsL is sea level temperature, Ts is weather station temperature, H is sea level height, and the unit is m.
[047] Based on the folding knife method, 699 meteorological stations in China are divided into 140 verification stations and 559 fitting correction points according to the proportion of 20% and 80%, so as to establish multiple linear regression equation. It can be seen from the preliminary accuracy results of the air temperature change model, Although the overall accuracy is high, there is still the problem of abnormal temperature value of model output data caused by drastic fluctuation of daily air temperature, Further correction is needed to reduce the size of the deviation and improve the accuracy of the data set. For the abnormal temperature value, the invention replaces the measured data of the weather station for the pixel with the weather station at the pixel position, and corrects the temperature of the adjacent pixel without the weather station at the pixel position. Multivariate linear regression is carried out on the final output data. The multivariate linear regression interpolation method establishes the stepwise regression relationship between the measured value of the station and the fitting value of the corresponding pixel, calculates the regression temperature prediction value according to the regression equation, and calculates the measured value and the regression prediction value to obtain the temperature residual. The residual error is interpolated to the full graph and the two are added according to the spatial distribution of each pixel to obtain the correction value of the regression equation. The formula is as follows:
[048] V(x, y) = ii(x, y)+2(x, y)
[049] 4° ;
[050] Where X and Y are the number of rows and columns of pixels, V(x,)is
the correction value of regression equation, ' is the regression prediction value
of temperature and )is the residual error; y is the measured value and yo is the regression predicted value.
[051] (2)Precision verification method
[052] Select three indicators to measure the accuracy of variables, namely R2, MAE and RMSE; R2 is the determination coefficient or goodness of fit; MAE is the average absolute error, which is the average value of absolute error and is used to reflect the actual situation of predicted value error; RMSE is the root mean square error, which is the sum of the square sum deviation between the observed value and the true value.
[053] (3)Temporal and spatial variation trend
[054] The daily maximum, minimum and average temperatures obtained from the final data set are used to analyze the regional air temperature changes in China, and further test the effect and regional applicability of the data set. ETCCDI, an expert group on climate change detection and index, put forward a set of extreme climate indexes at the climate change monitoring conference, and twenty-seven indexes were regarded as its core indexes, including sixteen temperature indexes and eleven precipitation indexes. Four of them were selected, That is, the highest temperature, the lowest temperature, the number of warm days and the number of Leng Ye days, and make certain adjustments to comprehensively analyze the change trend of extreme temperature in each year, The maximum temperature and/or minimum temperature is the average value of the maximum temperature and/or minimum temperature in each month of each year and subtracted from the sum of the maximum temperature and/or minimum temperature in each month of the study period for 40 years, and the annual total maximum temperature and/or minimum anomaly temperature are obtained, and the interannual variation trend of the maximum temperature and/or minimum temperature is calculated by linear regression. The number of warm days is to analyze the number of warm days and cold nights caused by climate fluctuation in each year by sorting the highest and/or lowest temperatures in each month in the 40-year study period according to the ascending order of temperature, and taking more than 90% and less than 10% of them corresponding to the number of days in each year.
[055] Using linear regression method and correlation coefficient to calculate the interannual change rate and correlation of daily average temperature, the formula is as follows: Using linear regression method and correlation coefficient to calculate the interannual change rate and correlation of daily average temperature, the formula is as follows:
K j2~ii if
[056] ;
[057];
[058] Use double-tailed t-test for significance test to quantify the significance of temperature and time series changes:
[059] ;
[060] Where n represents the total number of years of the time series length, i represents the year, and Ti represents the annual average temperature in the first year; K > 0 indicates that the air temperature is increasing in the time series range, and K < indicates that the air temperature is decreasing in the time series range; R denotes the correlation between temperature and time series, R > 0 denotes the positive correlation between temperature and time series, R < 0 denotes the negative correlation between temperature and time series, and R value is between-1 and 1; According to the correlation coefficient R, the significance is proved by t test. The confidence values are a = 0.05 and a = 0.01. By consulting the t distribution table, which obeys the t distribution with degree of freedom y = n-2, the regions and passing ranges with significant correlation between air temperature and time series development are obtained.
[061] Another object of the invention is to provide a construction system of a high spatial-temporal resolution near-surface air temperature model applying the construction method of the high spatial-temporal resolution near-surface air temperature model. The construction system of the high spatial-temporal resolution near-surface air temperature model comprises:
[062] Regional division module, which is used to divide China into six regions according to natural geographical environment and climatic conditions;
[063] A weather state dividing module is used for dividing daily weather states into sunny days and non-sunny days;
[064] The temperature estimation module is used for estimating the temperature in sunny days, the temperature in non-sunny days and the daily average temperature respectively;
[065] The air temperature data model building module is used for building air temperature data models in different weather states respectively;
[066] The linear regression correction module is used for the linear regression correction processing of the air temperature data model respectively.
[067] It is another object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the following steps:
[068] Firstly, the weather from 1979 to 2018 is divided into sunny days and non sunny days, Through the existing reanalysis data set, Using meteorological station data, Combined with the hourly temperature data of weather stations driven by surface meteorological elements in China and MODIS daily Ts, the daily maximum and minimum temperature models under different weather conditions are established, and the daily average temperature data set is obtained by adding and averaging. After linear regression correction, the daily near-surface temperature data set of China from 1979 to 2018 is finally output, with a spatial resolution of 0.1°.
[069] Another object of the present invention is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the following steps:
[070] Firstly, the weather from 1979 to 2018 is divided into sunny days and non sunny days, Through the existing reanalysis data set, Using meteorological station data, Combined with the hourly temperature data of weather stations driven by surface meteorological elements in China and MODIS daily Ts, the daily maximum and minimum temperature models under different weather conditions are established, and the daily average temperature data set is obtained by adding and averaging. After linear regression correction, the daily near-surface temperature data set of China from 1979 to 2018 is finally output, with a spatial resolution of0.1.
[071] Another object of the present invention is to provide an information data processing terminal for realizing the construction system of the high spatial-temporal resolution near-surface air temperature model.
[072] Combined with all the technical schemes mentioned above, The invention has the advantages and positive effects as follows: The invention provides a method, a system and a device for constructing a near-surface air temperature model with high spatial and temporal resolution, Using meteorological station data and reanalysis data, the daily maximum and minimum temperature models under different weather conditions are established, and the daily average temperature data set is obtained by adding and averaging, and finally the daily near-surface temperature data set (maximum, minimum and average) of China from 1979 to 2018 is output, with a spatial resolution of 0.1; The results show that the accuracy of this data set has been improved obviously and the applicability of this data set is higher than that of the existing reanalysis data set and the measured data of meteorological stations; The average precision ranges are as follows: R2, MAE and RMSE of daily maximum temperature are 0.98, 1.00 and 1.37 respectively; R2, MAE and RMSE of daily minimum temperature are 0.97, 1.17 and 1.59, and R2, MAE and RMSE of daily average temperature are 0.99, 0.53 and 0.77 respectively. Using extreme climate index to study the change trend of daily maximum temperature and minimum temperature with time series, The temperature in all regions of China is warming up, with the highest temperature anomaly rising by 0.42° and the lowest temperature anomaly rising by 0.47° every year, and the daily average temperature is gradually rising, which is consistent with the global warming trend. To sum up, the data set of the invention can better estimate the daily maximum temperature, minimum temperature and average temperature, which is convenient for further analyzing the seasonal and periodic changes of regional temperature in China.
[073] The invention provides a new gridded high-resolution daily maximum, minimum and average temperature data set in China from 1979 to 2018. Based on the particularity of geographical and climatic changes in different regions, the multi-year temperature fluctuation trend and change range are studied. The newly created dataset provides a high-resolution regionalized dataset. Spatial analysis of daily maximum and minimum temperatures in each region. According to the observation data of weather stations, the accuracy of the data set is high, and the model building method is more effective. Accuracy range: the average R2, MAE and RMSE of daily maximum temperature are 0.98, 0.98 and 1.32 °C, and the average R2, MAE and RMSE of daily minimum temperature are 0.97, 0.63 and 1.62 °C respectively. The most stable daily maximum and minimum temperatures in China are in China's tropical monsoon climate zone. The tropical monsoon climate zone is hot all year round, and the daily temperature range is relatively stable and very strong. China's plateau and mountain climate lies in the first step of China. From today, high altitude, thin air, large temperature changes and violent fluctuations are the main reasons for its low accuracy.
[074] By using four maximum temperatures (TXx), warm days (TX90p), minimum temperatures (TNn) and cold night days (TNOp) in 27 extreme climates, the index analysis of daily minimum and maximum temperatures in China is commonly used internationally. In recent years, the days of Leng Ye and warm days fluctuate in each study area, but on the whole, the days of Leng Ye gradually decrease, the number of warm days gradually increases, and the maximum and minimum temperatures gradually increase, which is consistent with global warming. The situation is the same. Since daily maximum and minimum temperatures are highly volatile, the data set needs further improvement. Therefore, in the subsequent research, we can perform more accurate processing and analysis by adding model input parameters to meet the needs of users. Data need to be analyzed with higher accuracy in many aspects.
[075] In order to more clearly explain the technical proposal of the embodiment of the invention, While a brief description will be given below of the drawings required for use in embodiments of the invention, it will be apparent that the drawings described below are only some of the embodiments of the invention from which other drawings may be obtained without creative effort by those of ordinary skill in the art.
[076] Fig.1 is a flow chart of a method for constructing a high spatio-temporal resolution near-surface air temperature model provided by an embodiment of the present invention.
[077] Fig.2 is a structural block diagram of a construction system of a high spatio temporal resolution near-surface air temperature model provided by an embodiment of the present invention;
[078] In the picture: 1. Area division module; 2. Weather state division module; 3. Temperature estimation module; 4. Building module of air temperature data model; 5. Linear regression correction module.
[079] Fig.3 is a range diagram of the total study area and six sub-study areas provided by the embodiment of the present invention.
[080] Fig.4 is a flowchart of a data set construction summary provided by an embodiment of the present invention.
[081] Fig.5 is a diagram of a method for determining the occurrence time of daily maximum and minimum temperature provided by an embodiment of the present invention.
[082] Fig.6 is a spatial downscaling diagram provided by an embodiment of the present invention.
[083] Fig.7 is a schematic diagram of a simulation curve of a daily temperature change trend (after standardization) provided by an embodiment of the present invention.
[084] Fig.8 is a flowchart of data set model correction provided by an embodiment of the present invention.
[085] Fig.9 is a scatter diagram of the weather station monitoring data and the daily maximum temperature data output by the model of the six sub-study areas (I,II, III, IV, V, VI) provided by the embodiment of the present invention, and the diagram shows the evaluation index measures (linear equation, R2, MAE, RMSE) of the corresponding sub-study areas.
[086] Fig.10 is a scatter plot of the weather station monitoring data of six sub study areas (I, II, III, IV, V, VI) provided by the embodiment of the present invention and the daily minimum temperature data output by the model, showing each evaluation index measurement value (linear equation, R2, MAE, RMSE) of the corresponding sub study areas in the plot.
[087] Fig.11 is a scatter plot of the weather station monitoring data of six sub study areas (I, II, III, IV, V, VI) provided by the embodiment of the present invention and the daily average temperature data output by the model, showing each evaluation index measurement value (linear equation, R2, MAE, RMSE) of the corresponding sub study areas in the plot.
[088] Fig.12 shows daily maximum temperature and actual measurement data accuracy of weather stations in six sub-study areas (I, II, III, IV, V, VI) provided by the embodiment of the present invention. Purple color in the figure indicates the maximum temperature data point range and various error values after calibration.
[089] Fig.13 shows daily minimum temperature and actual measurement data accuracy of weather stations in six sub-study areas (I, II, III, IV, V, VI) provided by the embodiment of the present invention, and the calibrated minimum temperature data point range and various error values are represented in color.
[090] Fig.14 shows the daily average temperature of six sub-study areas (I, II,III, IV, V, VI) provided by the embodiment of the present invention and the actual measurement data accuracy of weather stations. Purple color in the figure indicates the calibrated minimum temperature data point range and various error values.
[091] Fig.15 is a schematic diagram of accuracy verification results of daily average air temperature and CMFD and ERA5 reanalysis data provided by the embodiment of the present invention.
[092] Fig.16 is a trend diagram of the highest temperature and warm days for each study area provided by an embodiment of the present invention.
[093] Fig.17 is a schematic diagram of the trend in the number of minimum temperatures and cold nights for each study area provided by an embodiment of the present invention.
[094] In order to make the object, technical proposal and advantages of the present invention more clear, the present invention is described in further detail below in connection with the embodiments. It should be understood that the specific embodiments described herein are intended only to explain the invention and are not intended to limit it.
[095] Aiming at the problems existing in the prior art, the invention provides a method, a system and a device for constructing a near-surface air temperature model with high spatial and temporal resolution, and the invention is described in detail with reference to the accompanying drawings.
[096] As shown in Fig.1, the method for constructing a high spatial-temporal resolution near-surface air temperature model provided by the embodiment of the invention comprises the following steps:
[097] S101, China is divided into six regions according to natural geographical environment and climatic conditions;
[098] S102, dividing the daily weather state into sunny and non-sunny states, and estimating the temperature;
[099] S103, according to different weather conditions, the air temperature data model is constructed respectively;
[0100] S104, respectively carrying out linear regression correction processing of the air temperature data model.
[0101] As shown in Fig.2, the construction system of the near-surface air temperature model with high spatial and temporal resolution provided by the embodiment of the present invention includes:
[0102] Regional division module 1, which is used to divide China into six regions according to natural geographical environment and climatic conditions;
[0103] The weather state dividing module 2 is used for dividing the daily weather state into sunny and non-sunny states;
[0104] The temperature estimation module 3 is used for respectively carrying out the temperature estimation in the sunny day state, the temperature estimation in the non sunny day state and the daily average temperature estimation;
[0105] The air temperature data model building module 4 is used for building air temperature data models in different weather conditions respectively;
[0106] The Linear Regression Correction Module 5 is used for carrying out linear regression correction processing of the air temperature data model respectively.
[0107] The technical scheme of the present invention is further described in connection with embodiments below.
[0108] 1. According to the invention, the daily maximum and minimum temperature models under different weather conditions are established by using meteorological station data and reanalysis data, and the daily average temperature data set is obtained by adding and averaging, and finally the daily near-surface temperature data set (maximum, minimum and average) of China from 1979 to 2018 is output, with a spatial resolution of 0.1° . After validation with the existing reanalysis data set and the measured data of meteorological stations, it shows that the accuracy of this data set has been significantly improved, and its applicability to various regions is high. The average precision range is: R2 of daily maximum temperature is 0.98, MAE is 1.00, RMSE is 1.37°. R2, MAE and RMSE of daily minimum temperature are 0.97,1.17 and 1.59, and R2, MAE and RMSE of daily average temperature are 0.99, 0.53 and 0.77 respectively. Using extreme climate index to study the change trend of daily maximum temperature and minimum temperature with time series, The temperature in all regions of China is warming up, with the highest temperature anomaly rising by 0.42° and the lowest temperature anomaly rising by 0.47° every year, and the daily average temperature is gradually rising, which is consistent with the global warming trend. To sum up, this data set can better estimate the daily maximum temperature, minimum temperature and average temperature, which is convenient for further analysis of seasonal and periodic changes of regional temperature in China.
[0109] The invention creatively provides a method, Combining the existing reanalysis data with the observation data of weather stations, According to the periodicity of daily temperature change, a piecewise sine function is established according to different weather conditions to fit the daily maximum and minimum temperature equations, or a daily value air temperature data set is obtained by downscaling the existing reanalysis data set, and a daily average air temperature data set is further expanded.
[0110] 2. China has a vast territory, rich climate types and complex ecological environment. From south to north, from tropical to cold temperate zone, it covers six climatic zones. The altitude ranges from-154.31 to 8848.86 meters, the terrain height gradually decreases from west to east, and the precipitation gradually increases, showing the change of humidity from drought to humid and from desert to grassland. Considering the differences of geographical position, altitude, climate characteristics and agricultural planting methods of each region, the invention divides China into six regions according to natural geographical environment and climatic conditions. The boundary between this region and China's monsoon climate zone is roughly the same, which accords with the unique climate characteristics of each region. By analyzing the specific types of regions, the daily maximum and minimum temperature models are constructed, so as to further study the temporal and spatial trends of temperature in large continuous regions. The six sub-study areas are (I) northeast of temperate monsoon climate zone (II) south of temperate monsoon climate zone (III) subtropical monsoon climate zone. (IV) Tropical monsoon climate zone (V) Temperate continental monsoon climate zone (VI) Plateau mountain climate zone. (I) The northeastern part of temperate monsoon climate is mainly northeast of China, which is located to the east of Daxing'anling. The annual precipitation is 400-1000mm, which gradually decreases from east to west. The annual cumulative temperature is between 2500-4000 °C, with cold and long winters and hot and rainy summers. This area is an important commodity grain base in China. Crops are more sensitive to climate change and are extremely vulnerable to extreme weather events. (II) In the south of monsoon temperate climate zone, the annual accumulated temperature is between 3000-4500 °C, which is hot and rainy in summer and cold and dry in winter. Affected by monsoon, extreme weather disasters are more likely to occur. (III) Subtropical monsoon climate is south of Huaihe River in Qinling Mountains, north of tropical monsoon climate zone, east of Hengduan Mountains and Taiwan. The annual accumulated temperature is between 4500-8000 °C, and the precipitation is mostly between 800-1600mm. Summer is hot and winter is warm. (IV) Tropical monsoon climate is usually located south of Tropic of Cancer. The annual accumulated temperature is greater than 800 °C, the annual minimum temperature is not lower than 0 °C, there is no frost all the year round, and the annual precipitation is mostly 1500-2000mm. (V) The temperate continental climate is mainly distributed in the inland areas above 40 degrees north latitude in China, located in the northwest of Daxing'anling-Yinshan-Hengduan Mountain line. It is far away from the coast and difficult to transport water vapor. The annual precipitation is between 300 500mm. The daily temperature difference and annual temperature difference are very large, including temperate desert climate, temperate grassland climate and sub-cold temperate coniferous forest climate. (VI) The climate of plateau and mountain area is mainly distributed in Qinghai-Tibet Plateau. The annual accumulated temperature is lower than 2000 °C, the daily temperature difference is large, the annual temperature difference is small, the solar radiation is strong, the sunshine is abundant, and the precipitation is less. Unlike other climatic types, biodiversity is affected by latitude and altitude. The climate in plateau and mountainous areas is mainly affected by altitude. The map of the study area is shown in Figure 3, in which black points indicate the distribution position of meteorological stations, and blue border lines indicate sub-study divisions, which are represented by I,II, III, IV, V and VI respectively.
[0111] 3. Data
[0112] 3.1 Reanalysis data
[0113] The reanalysis data is formed on the basis of assimilating a large number of remote sensing data, existing reanalysis data and meteorological station data, and can carry out meteorological and climate change research on a large range of areas. The data set has long time series and high spatial resolution. The invention mainly uses CMFD data set and ERA5 reanalysis data set.
[0114] 3.2 Meteorological Station Data and Auxiliary Data
[0115] The invention uses weather station observation data issued by China Meteorological Administration, and carries out strict quality control and evaluation, including hourly air temperature data, hourly surface temperature data and daily monitoring data of weather stations (including daily maximum, minimum and average temperature data). According to the invention, the hourly temperature data issued by 699 ground weather stations and the hourly surface temperature data issued by 2399 ground observation stations in China from 1979 to 2018 are utilized to determine the occurrence time of daily maximum temperature and minimum temperature according to a statistical method.
[0116] In the invention, ERA5 data is spatially downscaled, and MOD11A1 and MYDIlAl products in MODIS are used. MODIS data can be downloaded from the LAADS DAAC website. (https://ladsweb.modaps.eosdis.nasa.gov/search/order).
[0117] In this study, the elevation of 90m resolution DEM image from SRTM data of American space shuttle Endeavour radar topographic map was used to correct the temperature data to reduce the influence of topographic fluctuation on temperature. SRTM elevation data are mainly used for environmental analysis and can be obtained through USGS network. (http://srtm.csi.cgiar.org/).
[0118] In this study, the elevation of 90m resolution DEM image from SRTM data of American space shuttle Endeavour radar topographic map was used to correct the temperature data to reduce the influence of topographic fluctuation on temperature. SRTM elevation data are mainly used for environmental analysis and can be obtained through USGS network. (http://srtm.csi.cgiar.org/).
[0119] 4. Invention method
[0120] Firstly, the weather from 1979 to 2018 is divided into sunny days and non sunny days, In addition, through the existing reanalysis data set, Combined with the hourly temperature data of weather stations driven by surface meteorological elements in China and MODIS daily, a model is established under sunny conditions according to the existing research theory that daily temperature changes conform to sinusoidal curves, and the daily temperature data set is determined by multi-steps under non-sunny conditions, and the final daily temperature data set is obtained after linear regression correction. In short, the establishment of the daily temperature data set of the invention is mainly divided into three steps: judging the daily weather state, establishing the model under different weather states (section 4.1. 2 is the model establishment method under sunny weather state, section 4.1. 3 is the data establishment method under non sunny weather state) and the model correction method. In order to show the production process of daily maximum and minimum temperature data sets more intuitively, the detailed data processing method is shown in Figure 4.
[0121] 4.1 Temperature data model
[0122] 4.1. 1 Determination of weather conditions
[0123] The invention firstly discriminates the daily weather phenomenon to determine the calculation method of daily value temperature according to different weather conditions. Affected by cold front, cyclone circulation, The influence of complex weather systems such as high and low pressure and thunderstorms, The occurrence time of daily maximum temperature and minimum temperature is non periodic and uncertain, so the abnormal weather conditions can be judged according to the abnormal occurrence time of daily maximum temperature, and then the daily weather phenomena during the study period can be divided into sunny and non-sunny states, which is convenient for further calculation.
[0124] The present invention mainly uses a statistical method to obtain the daily occurrence time of the highest temperature and the lowest temperature of each pixel by adopting two strategies, and the specific implementation steps of the two strategies are shown in Fig. 5. The first strategy is to determine the input parameters of daily maximum temperature in areas where stations are densely distributed (the distance between adjacent stations is less than 30km). Four methods are mainly used: 1) When the measured data of stations are complete and there is no abnormal value, the hourly station data are used to determine the occurrence time of daily maximum temperature and minimum temperature; 2) When the measured data of the station has missing value but discontinuous missing value, under the condition of the same space range, the invention adopts two timing temperatures before and after the same station to fill and repair so as to determine the time when the daily maximum temperature appears; 3) When the observation data of the station is continuously missing, under the condition of the same time range, the invention fills according to the time when the daily maximum value appears at the adjacent stations to determine the time when the daily maximum temperature appears at the point. This method is mainly based on the principle that the closer the distance between stations, the stronger the spatial consistency and correlation of temperature changes; 4) When the site data is continuously missing and the adjacent site data cannot be filled, other related data are used to repair in the same time and space range. According to the approximate consistency trend of daily surface temperature and air temperature change, the hourly surface temperature of the same station is adopted to determine the daily maximum air temperature. The method is suitable for stations with too many missing values, no adjacent stations near 30km of weather stations and incomplete timing data before and after. Many scholars have studied the diurnal variation trend of air temperature and surface temperature, It is found that the daily variation trend of ground temperature and air temperature has a strong consistency. At present, the land surface temperature retrieved by remote sensing satellites is widely used to estimate the daily minimum temperature and maximum temperature, and many achievements have been made, and its accuracy has been greatly improved, which proves the reliability of the research on estimating air temperature through land surface temperature.
[0125] The second strategy is to determine the occurrence time of the daily maximum temperature in the area where the stations are sparse and the Euclidean distance between two adjacent stations is greater than 30km. Because the spatial resolution of ERA5 data is different from that of the data set of the invention, it is difficult to meet the demand of the invention for obtaining a higher precision air temperature data set. In the invention, ERA5 data is utilized to perform spatial downscaling under the assistance of CMFD data and MODIS data, so as to determine the occurrence time of daily maximum temperature and minimum temperature. The spatial resolution of ERA5 data is 30km, that of CMFD data is 0.10, and that of MODIS data is 1km. The general flow is that the 30km grid of ERA5 is downscaled to 0.1° grid at first, and then the downscaled ERA5 data is traversed day by day to get the occurrence time of daily maximum and minimum temperature, and finally the occurrence time of daily maximum temperature in each region is output. The CMFD data is introduced to ensure the validity and integrity of the daily maximum time data inputted by the invention, and the MODIS data is introduced to improve the spatial resolution and refine the precision value. The specific calculation steps are as follows: since MODIS can obtain LST observation results of 1km four times a day since 2002, the time series is divided into two stages: 1979-2001 and 2002-2018. Firstly, the daily ERA5 data and the CMFD data in the study period are arranged according to the time and the same central longitude and central latitude. The invention distributes the hourly ERA5 data and the triple-hourly CMFD data according to the near time. Secondly, each pixel of ERA5 is divided into the same pixel size as that of CMFD, and each pixel corresponding to a single pixel of ERA5 in CMFD is regarded as a whole. After ERA5 segmentation, the spatial correlation between CMFD data and CMFD data is established, and the hourly ERA5 data is downscaled to 0.l°by using the ratio of CMFD pixels to ERA5 corresponding pixels. After 2002, according to the correlation between air temperature and LST diurnal variation, ERA5 data was downscaled by CMFD data and MODIS data, and the accuracy of the results was tested. Specific calculation method and formula factor expression can be seen in formula (1). Wherein, TE is
EARA5 date, CMFD data is represented by Tc , and MODIS data is represented by
M ;TE , .)is the air temperature data after ERA5 data is downscaled at pixel
position o'yv) , Tc(xY) is the air temperature data of CMFD at pixel position ,the sum of air temperature values of each pixel position in the area where
CMFD corresponds to ERA5 pixel is , and the air temperature
corresponding to ERA5 original spatial resolution image is TE(X. Yn)
[0126] Since the CMFD data is once every three hours, the invention obtains the time corresponding to the occurrence of the daily maximum temperature and the daily minimum temperature through era5, and then uses the temperature of each pixel of CMFD corresponding to the temporary time for spatial downscaling.
TE(x., y.) = *EXf
[0127] ="- = (1)
[0128] 4.1. 2 Estimation of temperature in sunny days
[0129] The specific statistical derivation process of the occurrence time of daily maximum temperature and minimum temperature has been described in 4.1. 1 above, According to the invention, the occurrence time of the obtained daily maximum temperature and the daily minimum temperature is output as parameters in a piecewise sine function, According to the approximate periodicity of the daily temperature change and the asymmetry of the occurrence time of the maximum and minimum temperature, the present invention can deduce the segmented sine function curves near the occurrence time of the maximum and minimum temperature of the day, as shown in formulas (2) and (3). Among them, Formula (2) is the daily minimum temperature change function, and Formula (3) is the daily maximum temperature change function. Using the least square method, the CMFD reanalysis data and the daily maximum and minimum temperature occurrence time are substituted into the equation, and the values of parameters A and B are obtained to construct a piecewise sine function. The daily maximum and minimum temperature occurrence time is substituted into the derivation formula again to output the daily maximum and minimum temperature. Least square method is a mathematical optimization technique, which uses the least square sum of residuals as the estimation standard of the best matching function. This algorithm is usually used in statistical models, and it is the most applicable and widely used parameter estimation method so far. In order to show the establishment process of the relationship between the occurrence time of daily maximum and minimum temperature and sine curve more intuitively and clearly, taking the daily temperature change trend of standardized local area at a specific time as an example, the daily temperature change curve is roughly drawn, as shown in Fig. 7. The blue part in Fig. 7 shows the daily minimum temperature change region covered by the sinusoidal equation constructed by the present invention, The orange part indicates the daily maximum temperature change area covered by sine equation, and the temperature values corresponding to eight time points in the reanalysis data are input into the change chart, and the approximate positions of the maximum temperature and the minimum temperature are displayed. In practice, daily temperature changes fluctuate violently, so it is necessary to analyze the particularity of time and space to meet the characteristics of regional differences.
(t-H.)t ]+
[0130] 24 - HA.+ H 2 (2)
(t-H.j at T,=At * si[ (- ] +Br
[0131] Hym - Hfm" (3)
[0132] Where Hminis the time when the lowest temperature occurs every day, and Hmaxis the time when the highest temperature occurs every day. Due to the periodicity of temperature occurrence, the occurrence time of the daily minimum temperature of the next day is set to Hmin +24. According to the periodicity of sine function, the sine formulas of daily minimum temperature and daily maximum temperature are derived. At and Bt are unknown parameters. Fig. 6 is a schematic diagram of the simulation curve of daily temperature change trend (after standardization), in which x graph in the diagram represents the distribution range of CMFD data set every three hours, and points represent the distribution range of ERA5 reanalyzed data set every hour.
[0133] 4.1. 3 Estimation of temperature under non-sunny conditions
[0134] The daily air temperature fluctuates violently in the non-sunny state, and the calculation of the daily maximum and minimum air temperature of the invention mainly adopts two methods, one is when the pixel position is corresponding to the weather station, and the other is when the pixel position is not corresponding to the weather station. When the pixel position corresponds to the weather station, the invention uses the daily maximum temperature, minimum temperature and average temperature measured by the corresponding weather station to fill, and the measured data have undergone strict quality control and evaluation, and the influence of altitude on the temperature has been eliminated through terrain correction, the terrain correction method will be introduced in 4.2 below. When there is no corresponding weather station at the pixel position, the present invention adopts ERA5 hourly temperature to carry out spatial downscaling by means of CMFD hourly data, and the downscaling process is introduced in 4.1. 1 of the present invention. For the downscaling process in the non sunny state, the invention traverses the hourly temperature data after downscaling of ERA5 corresponding to the region without pixel position, and finds out the daily maximum and minimum temperature values of the pixel, which are the highest temperature and the lowest temperature.
[0135] 4.1. 4 Estimates of daily mean temperature
[0136] Adding and averaging the corrected output daily maximum and minimum temperature data sets with eight daily temperature values of CMFD, Get the daily average temperature value and carry out preliminary accuracy verification with the weather station data (the accuracy verification result is shown in Figure 11 in 5.1 later), Then, according to the weather station data, the daily average temperature output value is corrected by multiple linear regression to improve the accuracy, and finally the daily average temperature data set is output (the final accuracy verification result is shown in Figure 14 in 5.2 below). The quality control and topographic correction of the daily average temperature measured at meteorological stations before correction is the same as in 3.2 above, and the linear correction method is the same as in 4.2 below.
[0137] 4.2 Temperature Data Correction Scheme
[0138] Since the temperature is sensitive to changes in altitude and is susceptible to the influence of the surrounding environment, the data of various weather stations used in the present invention have been highly corrected by the vertical attenuation rate of the average atmospheric temperature. Firstly, the observed data are unified to the sea level height, and then the data correction or interpolation process is completed by the temperature corresponding to the sea level, and then the data is corrected to its altitude. This method can reduce the influence of altitude on temperature and improve the accuracy of data set to a certain extent. In the invention, the invention uses a unified standard, that is, for every 100 meters of elevation, the atmospheric temperature vertically drops by 0.65 °C, and vice versa. The revised equation is shown in Formula (4). Where TSL is sea level temperature, TS is weather station temperature, H is sea level height, and the unit is m.
[0139] TSL =T +00065H (4)
[0140] Based on the folding knife method, 699 meteorological stations in China are divided into 140 verification stations and 559 fitting correction points according to the proportion of 20% and 80%, so as to establish multiple linear regression equation. It can be seen from the preliminary accuracy results of the air temperature change model in 5.1, Although the overall accuracy is high, there is still the problem of abnormal temperature value of model output data caused by drastic fluctuation of daily air temperature, Further correction is needed to reduce the deviation and improve the accuracy of the data set. The data correction process is given in Fig. 8. For abnormal temperature values, the invention replaces the measured data of meteorological stations for pixels with meteorological stations at pixel positions, and corrects the temperature of adjacent pixels without meteorological stations at pixel positions. Multivariate linear regression is carried out on the final output data. The multivariate linear regression interpolation method establishes the stepwise regression relationship between the measured value of the station and the fitting value of the corresponding pixel, calculates the regression temperature prediction value according to the regression equation, and calculates the measured value and the regression prediction value to obtain the temperature residual. The residuals are interpolated into the whole graph and the correction value of the regression equation is obtained by adding the residuals according to the spatial distribution of each pixel. The formula is:
[0141] V(X Y)= rU(X Y)+ (x Y) (5)
[0142] 4 Y =- (6)
[0143] In equations (5) and (6), X and Y are the rows and columns of pixels,
V(7Yis the correction values of the regression equation, is the regression
prediction values of the temperature, )is the residuals. Y is the measured value and yo is the regression predicted value.
[0144] 4.3 Accuracy verification method
[0145] In order to evaluate the accuracy of the data set, the invention selects three indicators to measure the accuracy of the variables, namely R2, MAE and RMSE. R2 is the determination coefficient or goodness of fit; MAE is the average absolute error and the average value of absolute error, which can reflect the actual situation of predicted value error; RMSE is the root mean square error, which is the sum of the squared deviation between the observed value and the true value.
[0146] In order to verify the accuracy of this data set, The method firstly carries out precision verification and corrected precision verification between the air temperature data set output by the invention and the station measured data; Secondly, the method selects the area with uniform surface type and flat terrain under clear sky state as a comparative study area, and carries out precision comparison between the daily value data set of the invention and the existing reanalysis data set. The daily maximum temperature and daily minimum temperature data set ERA5 reanalysis data set are compared with the measured data of meteorological stations respectively, It is worth mentioning that since the ERA5 reanalysis data set is an hourly temperature grid data set, the invention selects the highest temperature among 24 daily temperature values of ERA5 as the daily maximum temperature, and selects the lowest temperature as the daily minimum temperature for accuracy verification. Since the spatial resolution of the ERA5 data set is 30km, the daily maximum and minimum air temperature data sets of the invention are resampled to obtain the same resolution as the ERA5 data for precision evaluation. Finally, the invention verifies the daily average temperature precision, and compares and analyzes the daily average temperature data set, the CMFD daily temperature reanalysis data set, the ERA5 reanalysis data set and the station measured data to obtain the precision verification result of each data set. ERA5 hourly temperature is added and averaged to obtain ERA5 daily average temperature data set, When CMFD was released, it provided the daily average temperature data set in China and adjusted the three data sets to the same spatial resolution by resampling method for verification. All the selected precision comparison data sets are resampled to the same spatial resolution as ERA5 reanalysis data sets, and the final verification results are given in Section 5.2, and the precision effect of this data set and the temporal and spatial variation trend of average temperature are further analyzed.
[0147] 4.4 Temporal and spatial trends
[0148] The invention uses the daily maximum, minimum and average temperatures obtained from the final data set to analyze the change of air temperature in each region of China, and further tests the effect and regional applicability of the data set. The Expert Group on Climate Change Detection and Indicators (ETCCDI) put forward a set of extreme climate indicators at the Climate Change Monitoring Conference, and twenty-seven indicators were regarded as its core indicators, including sixteen temperature indicators and eleven precipitation indicators. The invention selects four items (maximum temperature, minimum temperature, warm day days and cold night days) and makes certain adjustments to comprehensively analyze the change trend of extreme temperature in each year, The maximum temperature (minimum temperature) is the average value of the sum of the maximum temperature (minimum temperature) in each month of each year and subtracted from the sum of the maximum temperature (minimum temperature) in each month of the study period (40 years), and the annual total maximum temperature (minimum temperature) anomaly temperature value is obtained, and the interannual variation trend of the maximum temperature (minimum temperature) is calculated by linear regression. The number of warm days (Leng Ye) is to analyze the number of warm days and Leng Ye caused by climate fluctuation in each year by sorting the highest (lowest) daily temperature in each month in the 40-year study period according to the ascending order of temperature, and taking more than % (less than 10%) of the days corresponding to each year. The invention observes the daily maximum temperature and the daily minimum temperature from the point of view of each study area, which is helpful to understand the difference of air temperature change among regions.
[0149] In order to study the temporal and spatial variation trend of the average temperature, the present invention adopts a linear regression method and a correlation coefficient to calculate the interannual variation rate and correlation of the daily average temperature, and the formulas are given by Eq.7 and Eq.8. The invention uses a double tailed t-test to perform a significance test to quantify the significance of changes in air temperature and time series (Eq.9).
[0150] = -
[0151] -1 -= ),_ -, -r= (8)
[0152] Tts-VIMR(9)
[0153] Where n represents the total number of years of the time series length, i represents the year, and Ti represents the annual average temperature in the first year. K > 0 indicates that the air temperature is increasing in the time series, and K < 0 indicates that the air temperature is decreasing in the time series. R indicates the correlation between temperature and time series, R > 0 indicates the positive correlation between temperature and time series, R < 0 indicates the negative correlation between temperature and time series, and R value is between-i and 1. According to the correlation coefficient (R), T test is carried out to prove its significance. The confidence values of the invention are alpha = 0.05 and alpha = 0.01. By consulting the T distribution table, which obeys the T distribution of the degree of freedom y = n-2, the area with significant correlation between air temperature and time series development and the passing range are obtained.
[0154] 5. Results and verification
[0155] 5.1 Accuracy verification before correction
[0156] The daily minimum and maximum temperature values are obtained by fitting the daily maximum and minimum temperature models, and the daily average temperature is obtained by adding and averaging the daily temperature. According to the sub-study areas divided by six physical geographical areas in Figure 3, the daily maximum, minimum and average temperature are compared and analyzed with the measured data of meteorological stations. Fig. 9 shows a scatter plot of the correlation coefficient between the original output of the daily maximum temperature and the temperature measured at the weather station. The determination coefficient (R2) fluctuated between 0.91 and 0.99, and the average determination coefficient was 0.96.
The mean absolute error (MAE) is between 1.69 and 2.71, and the average MAE is 2.05. The root mean square error (RMSE) fluctuates between 2.15 and 3.20, and the average root mean square error (RMSE) is 2.55. Fig. 10 shows the scatter plot of the daily minimum temperature and the original output of the measured temperature. The determination coefficient (R2) fluctuated between 0.93 and 0.97, and the average determination coefficient was 0.95. The mean absolute error (MAE) is between 1.34 and 2.17, and the average MAE is 1.85. The root mean square error (RMSE) fluctuates between 1.68 and 2.79, with an average RMSE of 2.41. Fig. 11 shows a scatter plot of the correlation coefficient between the original output of the daily average temperature and the temperature measured at the weather station. The accuracy of verification is that the determination coefficient (R2) fluctuates between 0.97 and 0.99, and the average determination coefficient is 0.99; The mean absolute error (MAE) is between 0.58 and 0.96, and the average MAE is 0.78. The root mean square error (RMSE) fluctuates between 0.86 and 1.60, and the average root mean square error (RMSE) is 1.15.
[0157] It can be seen from the figure that the determination coefficients between the estimated daily maximum temperature, minimum temperature and average temperature and the measured temperature at meteorological stations are all greater than 0.91, which indicates that it can better reflect the change characteristics of daily temperature values. However, due to the influence of abnormal weather phenomena, the distribution of some days is more discrete, especially in V and VI study areas, which need further correction to reduce errors and improve the accuracy of data sets.
[0158] 5.2 Verification of Corrected Accuracy
[0159] Based on the linear correction method mentioned in Section 4.2, regression correction is carried out between the original output temperature value and the measured value of meteorological station, and the regression coefficient is determined to reduce outliers. The accuracy of the corrected data is shown in the following figure. It can be seen from the figure that the corrected data has higher consistency, outliers have been greatly reduced, and the linear distribution effect of temperature values gradually approaches the regression line. Figure 12 shows the daily maximum temperature error range before and after correction in each area. Gray dots represent the distribution range of values before correction, and orange dots represent the error range of temperature values after correction. The decision coefficient (R2) fluctuates between 0.96 and 0.99, the average decision coefficient is 0.98, the average absolute error (MAE) varies between 0.63 and 1.40, the average absolute error is 1.00, and the root mean square error (RMSE) fluctuates between 0.86 and 1.78, and the average root mean square error is 1.37. Fig. 13 shows the daily minimum temperature scatter plot before and after correction in each sub-study area. The blue dot indicates the distribution range of the corrected temperature value. The decision coefficient (R2) fluctuates between 0.95 and 0.99, the average decision coefficient is 0.97, the average absolute error (MAE) ranges between 0.58 and 1.61, the average absolute error is 1.17, and the root mean square error (RMSE) fluctuates between 0.78 and 2.09, and the average root mean square error is 1.59. Figure 14 shows the scatter plot of daily mean air temperature before and after correction in each sub-study area. Green dots indicate the distribution range of corrected temperature values. The decision coefficient (R2) fluctuates between 0.99 and 1.00, the average decision coefficient is 0.99, the average absolute error (MAE) varies between 0.27 and 0.68, the average absolute error is 0.52, and the root mean square error (RMSE) fluctuates between 0.35 and 1.00, and the average root mean square error is 0.75.
[0160] It can be seen from the daily maximum, minimum and average temperature charts that the distribution of numerical points in the six regions after correction is more dense. It can also be seen from the invention that the accuracy error (MAE, RMSE) of the IV study area is the smallest, and the error value (MAE, RMSE) of the VI study area is the highest. This is because the IV study area is located in the tropical monsoon climate zone of China, affected by latitude and topography, the annual temperature is higher and located in the eastern part of China, and the meteorological stations are densely distributed, which can improve the coincidence between the data and the measured data. The VI and V study areas with low accuracy are roughly located in Qinghai-Tibet Plateau in southwest China and Xinjiang in northwest China. The terrain in this area is complex, the daily temperature changes greatly and the weather stations are sparse, which leads to a slight improvement in the accuracy after correction, but there are still some outliers. It can be seen from the figure that the accuracy change of air temperature after correction is different in different sub-study areas. The area with the greatest change in accuracy for daily maximum temperature is V study area, which is located in Xinjiang, northwest China. After correction, MAE decreased by 1.13, RMSE decreased by 1.31, temperature values were more concentrated and outliers were greatly reduced. The biggest change of the error evaluation index before and after the correction of the minimum daily temperature and the average daily temperature is in the study area I, which is mainly located in the northeast of China. This may be due to the high latitude in the northeast, the large fluctuation of daily temperature and the unstable time of the maximum and minimum daily temperature. Therefore, the daily minimum temperature value output by sinusoidal model and the daily average temperature obtained by addition and average can not meet the accurate estimation of weather conditions with frequent temperature changes, In view of the dense weather stations in this area, the regression equation correction can better conform to its variation characteristics and greatly improve the accuracy and robustness of the data. After correction, the areas with the smallest change of error measurement index are IV and VI study areas respectively. Consistent with the above, areas IV and VI are strongly influenced by geographical location, daily temperature fluctuations, weather conditions and the degree of distribution of meteorological stations in the region, which may be due to two reasons. On the one hand, the solar radiation in plateau and mountainous areas is relatively large. Although the altitude is higher, the change of daily minimum temperature is more stable, and the time of daily minimum temperature is relatively stable. In addition, due to the sparse distribution and small number of weather stations in this area, the number of weather stations used in linear regression may be insufficient, resulting in a slight change in the accuracy after correction. The present invention further analyzes the accuracy verification results of the highest and lowest temperatures in different regions through Table 2, and the present invention can see that the highest and lowest temperatures are consistent. The smallest error of MAE and RMSE is in tropical monsoon climate region (IV), and the largest error is in plateau and mountain study region (VI).
[0161] At the same time, in order to further demonstrate the data precision range and applicability of the data set, the invention cross-validates the existing CMFD reanalysis data and ERA5 reanalysis data with the data set, and also uses three precision evaluation indexes of R2, MAE and RMSE. First, the invention evaluates the accuracy of the daily maximum temperature and minimum temperature data set and the ERA5 hourly reanalysis data. Obtain the maximum and minimum temperature values of ERA5 data in a day, and compare them as daily maximum temperature and daily minimum temperature. Secondly, the invention compares and evaluates the accuracy of the daily average temperature data set obtained by adding and averaging the hourly data of ERA5 and the daily average temperature data set provided by the CMFD reanalysis data. The cross-validation result of the daily average air temperature of the invention is drawn into a histogram, which is shown in Fig. 15, from which it can be seen that the daily average air temperature of the invention has higher consistency with CMFD and ERA5 data. It can also be clearly seen from the graph and table that the data set has better estimation effect for each region and higher accuracy of the data set.
[0162] 5.3 Spatial and Temporal Trend Analysis
[0163] The invention uses the daily maximum, minimum and average temperatures obtained from the final data set to analyze the change of air temperature in each region of China, and further tests the effect and regional applicability of the data set. The fluctuation trend of daily maximum temperature and minimum temperature is analyzed for each sub-study area, and its change development is shown in Figure 16 and Figure 17. As can be seen from the figure, The maximum temperature anomaly and the minimum temperature anomaly are consistent in regional change trend, Although the annual anomaly fluctuated during the study period, it gradually changed from negative anomaly to positive anomaly. After linear regression, the temperature anomaly increased most strongly in the IV study area, which is located in the tropical monsoon climate zone of China. The maximum temperature anomaly increased by 0.53°/y, and the minimum temperature annual anomaly increased by 0.61°/y. In the other study areas, the maximum temperature anomaly is above 0.30°/y, and the average temperature rise shows a linear trend, which is: (I)regional temperature rise of 0.40°/y, (II) regional temperature rise of 0.38°/y, (III) regional temperature rise of 0.41°/y, (V) regional temperature rise of 0.46°/y and (VI) regional temperature rise of 0.33°/y. The lowest temperature anomalies are all above 0.25°/y, and the variation trends are: (I) regional temperature rise 0.28°/y, (II) regional temperature rise 0.59°/y, (III) regional temperature rise 0.45°/y, (V) regional temperature rise 0.5 0 °/y and (VI) regional temperature rise 0.37°/y. The rapid warming of VI study area is mainly affected by its latitude and geographical location. Tropical monsoon climate zone is located in the tropical region of China, with high annual temperature and close to the ocean, which is greatly affected by monsoon. Correspondingly, the number of warm days in each region also increases, which is closely related to the global warming climate and environment. By arranging the daily maximum temperature (daily minimum temperature) in ascending order and summarizing the days > 90% (< 10%), the number of warm days and cold night days in each year can be obtained, and sorting and summarizing each month can avoid the defect of counting only the months with high (low) temperature caused by sorting each year. It can be seen from Figures 16 and 17 that the number of warm days and the number of cold night days also have a trend consistency. The years with more warm days are more years with less cold night, and the years with more warm days are 1998, 1999, 2005, 2006, 2007, 2010, 2015, 2016, 2017 and 2018. The years with fewer cold night days are 1979, 1980, 1982, 1983, 1984, 1985 and 1987. The decrease of Leng Ye days and the increase of warm days in the same year can show a certain trend. For example, in 2015 and 2017, high temperature weather occurred once in decades, which is consistent with the results of existing meteorological studies, and proves the accuracy of this data set in analyzing climate change trends.
[0164] 6. Results
[0165] The invention provides a new gridded high-resolution daily maximum, minimum and average temperature data set in China from 1979 to 2018. Based on the particularity of geographical and climatic changes in different regions, the multi-year temperature fluctuation trend and change range are studied. The newly created dataset provides a high-resolution regionalized dataset. Spatial analysis of daily maximum and minimum temperatures in each region. According to the observation data of weather stations, the accuracy of the data set is high, and the model building method is more effective. Accuracy range: the average R2, MAE and RMSE of daily maximum temperature are 0.98, 0.98 and 1.32 °C, and the average R2, MAE and RMSE of daily minimum temperature are 0.97, 0.63 and 1.62 °C respectively. The most stable daily maximum and minimum temperatures in China are in China's tropical monsoon climate zone.
[0166] As mentioned above, However, the scope of protection of the present invention is not limited thereto. Any modification, equivalent replacement and improvement made by any person familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.
[0167] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms, in keeping with the broad principles and the spirit of the invention described herein.
[0168] The present invention and the described embodiments specifically include the best method known to the applicant of performing the invention. The present invention and the described preferred embodiments specifically include at least one feature that is industrially applicable
Claims (7)
1. A method for reconstructing near-surface air temperature with high spatial and
temporal resolution, Characterized in that, The construction method of the near
surface air temperature model with high spatial and temporal resolution comprises the
following steps: Firstly, the daily weather is divided into sunny days and non-sunny
days, and the daily maximum and minimum temperature models under different
weather conditions are established by reanalyzing the data set, using the data of
meteorological stations, combining the hourly temperature data of weather stations
driven by surface meteorological elements in China and MODIS daily Ts; The daily
mean air temperature data set is obtained by adding and averaging, and the daily near
surface air temperature data set is finally output after linear regression correction, with
a spatial resolution of 0.1°.
2.The construction method of the high spatial-temporal resolution near-surface air
temperature model according to Claim 1, characterized in that the construction method
of the high spatial-temporal resolution near-surface air temperature model comprises
the following steps:
Step1, China is divided into six regions according to the natural geographical
environment and climatic conditions;
Step2, dividing the daily weather state into sunny and non-sunny states, and
estimating the temperature
Step3Constructing temperature data models according to different weather
conditions;
Step4, linear regression correction processing of air temperature data model is
carried out respectively.
3.The method for constructing a high spatial-temporal resolution near-surface air
temperature model according to Claim 2, It is characterized in that, in step one, the six
regions divided into China according to natural geographical environment and climatic
conditions include (I) northeast region of temperate monsoon climate zone, (II) south
of temperate monsoon climate zone, (III) subtropical monsoon climate zone, (IV)
tropical monsoon climate zone, (V) temperate continental monsoon climate zone and
(VI) plateau mountain climate zone;
Among them, (I) the northeast of temperate monsoon climate is mainly Northeast
China, which is located to the east of Daxing'anling; The annual precipitation is 400
1000 mm, which gradually decreases from east to west. The annual cumulative
temperature is between 2500-4000 °C, the winter is cold and long, and the summer is
hot and rainy; This area is an important commodity grain base in China. Crops are more
sensitive to climate change and are extremely vulnerable to extreme weather events;
(II) In the south of the monsoon temperate climate zone, the annual accumulated
temperature is between 3000-4500 °C, which is hot and rainy in summer and cold and
dry in winter; Affected by monsoon, extreme weather disasters are more likely to occur;
(III) Subtropical monsoon climate is south of Huaihe River in Qinling Mountains, north
of tropical monsoon climate zone, east of Hengduan Mountains and Taiwan; The annual
accumulated temperature is between 4500-8000°C, and the precipitation is mostly
between 800-1600mm. Hot summer and warm winter; (IV) Tropical monsoon climate
is usually located south of Tropic of Cancer; The annual accumulated temperature is
greater than 800 °C, the annual minimum temperature is not lower than 0 °C, there is
no frost all the year round, and the annual precipitation is mostly 1500-2000 mm; (V)
The temperate continental climate is mainly distributed in the inland areas above 40 degrees north latitude in China, located in the northwest of Daxing'anling-Yinshan
Hengduan Mountain line; Far from the coast, it is difficult to transport water vapor; The
annual precipitation is between 300-500mm; The daily temperature difference and
annual temperature difference are very large, including temperate desert climate,
temperate grassland climate and sub-cold temperate coniferous forest climate; (VI) The
climate of plateau and mountain area is mainly distributed in Qinghai-Tibet Plateau;
The annual accumulated temperature is lower than 2000°C, the daily temperature
difference is large, the annual temperature difference is small, the solar radiation is
strong, the sunshine is abundant, and the precipitation is less; Different from other
climatic types, biodiversity is affected by latitude and altitude, and the climate in
plateau and mountainous areas is mainly affected by altitude.
4.The method for constructing a high spatial and temporal resolution near-surface
air temperature model according to Claim 2, characterized in that, in Step 2, the daily
weather state is divided into sunny and non-sunny states, comprising:
Firstly, the daily weather phenomenon is discriminated to determine the
calculation method of daily value temperature according to different weather
conditions; Affected by cold front, cyclone circulation, Due to the influence of complex
weather systems such as high and low pressure and thunderstorm, the occurrence time
of daily maximum temperature and minimum temperature is aperiodic and uncertain,
so the anomaly of weather conditions can be judged according to the occurrence time
anomaly of daily maximum temperature, and then the daily weather phenomena during
the study period can be divided into sunny and non-sunny states;
Using statistical method and two strategies, the daily occurrence time of the
highest temperature and the lowest temperature of each pixel is obtained, The first strategy is to determine the daily maximum temperature input parameters in areas where stations are densely distributed, that is, areas where the distance between adjacent stations is less than 30km. Four methods are used: 1) When the measured data of stations are complete and there is no abnormal value, the hourly station data are used to determine the occurrence time of daily maximum temperature and minimum temperature; 2) When there are missing values but discontinuous missing values in the measured data of the station, under the condition of the same space range, the temperature before and after the same station is used to fill and repair to determine the time when the daily maximum temperature appears; 3) When the observation data of stations are continuously missing, under the condition of the same time range, filling is carried out according to the time when the daily maximum value appears at adjacent stations to determine the time when the daily maximum temperature appears at the point. The method is based on the principle that the closer the distance between stations, the stronger the spatial consistency and correlation of temperature changes; 4) When the site data is continuously missing and the adjacent site data cannot be filled, other related data are used to repair in the same time and space range; According to the approximate consistency trend of daily surface temperature and air temperature change, the hourly surface temperature of the same station is used to determine the daily maximum air temperature. This method is suitable for stations with too many missing values, no nearby stations near 30km of weather stations and incomplete time data before and after;
The second strategy is to determine the occurrence time of the daily maximum
temperature in the area where the stations are sparse and the Euclidean distance between
two adjacent stations is greater than 30km; Using ERA5 data, with the aid of CMFD data and MODIS data, spatial downscaling is carried out to determine the occurrence time of daily maximum temperature and minimum temperature; The spatial resolution of ERA5 data is 30km, that of CMFD data is 0.10, and that of MODIS data is lkm;
Firstly, the 30km grid of ERA5 is downscaled to 0.1grid, and then the downscaled
ERA5 data is traversed day by day to get the occurrence time of daily maximum and
minimum temperature, and finally the occurrence time of daily maximum temperature
in each region is output; The CMFD data is introduced to ensure the validity and
integrity of the daily maximum time data input by the invention, and the MODIS data
is introduced to improve the spatial resolution and refine the precision value;
The calculation steps are as follows: since MODIS can obtain four LST
observations of 1km a day since 2002, the time series is divided into two stages: 1979
2001 and 2002-2018; Arrange daily ERA5 data and CMFD data according to time and
the same central longitude and central latitude in the study period, and distribute hourly
ERA5 data and three-hour CMFD data according to the near time; Each pixel of ERA5
is divided into the same pixel size as that of CMFD and each pixel corresponding to a
single pixel of ERA5 in CMFD is regarded as a whole; After ERA5 segmentation, the
spatial correlation between CMFD data and CMFD data is established, and the hourly
ERA5 data is downscaled to 0.1by using the proportion of CMFD pixels to ERA5
corresponding pixels;
After 2002, according to the correlation between air temperature and LST diurnal
variation, ERA5 data is downscaled by CMFD data and MODIS data, and the accuracy
of the results is tested; Because the CMFD data is once every three hours, the time
corresponding to the daily maximum temperature and the daily minimum temperature is obtained by ERA5, and the temperature of each pixel of CMFD corresponding to the temporary time is used for spatial downscaling;
Wherein, the calculation method and formula factors are expressed as follows:
TE~~x~~y0 )= Txy)T(X"'Y.) r Tcx(.T(y)
) Wherein, I is EARA5 date, CMFD data is represented by Tr , and MODIS data
is represented by TM TE(Xo Yu is the air temperature data after ERA5 data is
downscaled at pixel position (xY 0) , Tc(- Y") is the air temperature data of CMFD at
pixel position (XI) , the sum of air temperature values of each pixel position in the
area where CMFD corresponds to ERA5 pixel is , and the air
temperature corresponding to ERA5 original spatial resolution image isTE(XIw Yn)
5.The method for constructing a high spatial-temporal resolution near-surface air
temperature model according to Claim 2, characterized in that, in Step 2, the air
temperature estimation comprises:
(1) Temperature estimation in sunny days
Firstly, the approximate time of daily minimum temperature and maximum
temperature is determined by statistical method, and the deduced piecewise sine
function and the occurrence time of daily minimum temperature and maximum
temperature are input into the function model as parameters; Secondly, based on the
least square fitting method, the temperature of CMFD reanalysis data set is
parameterized every three hours to obtain the daily maximum and minimum
temperature curves, and finally the daily maximum and minimum temperature are
output as preliminary results for subsequent correction and analysis;
According to the approximate periodicity of daily temperature change and the
asymmetry of the occurrence time of the maximum and minimum temperatures, the
piecewise sine function curves around the occurrence time of the maximum and
minimum temperatures are deduced by outputting the occurrence time of the maximum
and minimum temperatures as parameters in the piecewise sine function; The CMFD
reanalysis data and the daily maximum and minimum temperature occurrence time are
substituted into the equation by the least square method, and the values of parameters
A and B are obtained to build a piecewise sine function, and the daily maximum and
minimum temperature occurrence time is substituted into the derivation formula again
to output the daily maximum and minimum temperature;
Wherein, the daily minimum temperature change function is:
T '.= At * sin[2 4 H H ] + Bt
The daily maximum temperature change function is:
T.= At * sin[ (] +B
Among them, Hmin is the time when the lowest temperature occurs every day, and
Hmax is the time when the highest temperature occurs every day. Because of the
periodicity of temperature occurrence, the occurrence time of the daily minimum
temperature of the next day is set to Hmin+24; According to the periodicity of sine
function, the sine formulas of daily minimum temperature and daily maximum
temperature are derived. At and Bt are unknown parameters;
(2) Estimation of air temperature in non-sunny weather
The daily maximum temperature, minimum temperature and average temperature
measured by corresponding meteorological stations are used to fill in. The measured
data have undergone strict quality control and evaluation, and the influence of altitude
on temperature has been eliminated through topographic correction; When there is no
corresponding weather station at the pixel position, ERA5 hourly temperature is used
to reduce the spatial scale by CMFD hourly data; For the downscaling process in non
sunny days, traverse the hourly temperature data of ERA5 corresponding to the area
without pixel position after downscaling, and find out the highest temperature and the
lowest temperature in the pixel;
(3)Estimation of daily average temperature
Adding and averaging the corrected output daily maximum and minimum
temperature data sets with eight daily temperature values of CMFD, The daily average
temperature value is obtained and verified with the meteorological station data, and
then the daily average temperature output value is corrected by multiple linear
regression according to the meteorological station data to improve the accuracy, and
finally the daily average temperature data set is output.
6.The method for constructing a high spatial-temporal resolution near-surface air
temperature model according to Claim 2, which is characterized in that, in Step 4, the
linear regression correction processing of the air temperature data model is carried out,
comprising:
(1)Temperature data correction scheme
Because the temperature is sensitive to the change of altitude and is easily affected
by the surrounding environment, the data of various weather stations used have been
highly corrected by the vertical attenuation rate of the average atmospheric temperature;
Unify the observation data to the sea level height; The data correction or interpolation
process is completed by the temperature corresponding to the sea level, and then
corrected to its altitude; Use a unified standard, that is, for every 100 meters of altitude
increase, the atmospheric temperature drops vertically by 0.65°C, and vice versa;
Wherein, the modified equation is as follows:
TSL = +0.0065H
Among them, TSL is sea level temperature, Ts is weather station temperature, H is
sea level height, and the unit is unified as m;
Based on the folding knife method, 699 meteorological stations in China are
divided into 140 verification stations and 559 fitting correction points according to the
proportion of 20% and 80%, so as to establish multiple linear regression equations; It
can be seen from the preliminary accuracy results of the air temperature change model,
Although the overall accuracy is high, there is still the problem of abnormal temperature
value of model output data caused by drastic fluctuation of daily air temperature,
Further correction is needed to reduce the deviation and improve the accuracy of the
data set. For abnormal temperature values, the invention replaces the measured data of
meteorological stations for pixels with meteorological stations at pixel positions, and
corrects the temperature of adjacent pixels without meteorological stations at pixel
positions; Multiple linear regression is carried out on the final output data, and the
multiple linear regression interpolation method calculates the regression temperature
prediction value according to the regression equation by establishing the stepwise
regression relationship between the measured value of the station and the fitting value
of the corresponding pixel, and calculates the measured value and the regression
prediction value to obtain the temperature residual; The residual error is interpolated to the full graph and the two are added according to the spatial distribution of each pixel to obtain the correction value of the regression equation. The formula is as follows: v(x, y) =in(x, y) + (x, y).
Where X and Y are the number of rows and columns of pixels, V(-Y) is the
correction value of regression equation, is the regression prediction value of
temperature and is the residual error; Y is the measured value, yo is the
regression prediction value;
(2)Precision verification method
Select three indicators to measure the accuracy of variables, namely R2, MAE and
RMSE; R2 is the determination coefficient or goodness of fit; MAE is the average
absolute error, which is the average value of absolute error and is used to reflect the
actual situation of predicted value error; RMSE is the root mean square error, which is
the sum of the square sum deviation between the observed value and the true value;
(3)Temporal and spatial variation trend
Use the daily maximum, minimum and average temperatures obtained from the
final data set to analyze the changes of air temperature in different regions of China,
and further test the effect and regional applicability of the data set; ETCCDI, an expert
group on climate change detection and index, put forward a set of extreme climate
indexes at the climate change monitoring conference, and twenty-seven indicators were
regarded as its core indicators, including sixteen temperature indicators and eleven
precipitation indicators; The highest temperature, the lowest temperature, the number
of warm days and the number of Leng Ye days are selected and adjusted to comprehensively analyze the change trend of extreme temperature in each year. The maximum temperature and/or minimum temperature is the average value of the maximum temperature and/or minimum temperature in each month of each year and subtracted from the sum of the maximum temperature and/or minimum temperature in each month of the study period for 40 years to obtain the annual total maximum temperature and/or minimum anomaly temperature, and the maximum temperature and/or minimum temperature are linearly regressed to calculate their interannual variation trend; The number of warm days is to analyze the number of warm days and
Leng Ye caused by climate fluctuation in each year by sorting the highest and/or lowest
temperatures in each month in the 40-year study period according to the ascending order
of temperature, and taking more than 90% and less than 10% of them corresponding to
the number of days in each year;
The interannual variation rate and correlation of daily average temperature are
calculated by linear regression method and correlation coefficient, and the formula is
as follows:
nK- 0%1 ~ ) - X=1T
Use double-tailed t-test for significance test to quantify the significance of
temperature and time series changes:
T_test=-
Where n represents the total number of years of the time series length, i represents
the year, and Ti represents the annual average temperature in the first year; K > 0
indicates that the air temperature is increasing in the time series range, and K < 0
indicates that the air temperature is decreasing in the time series range; R denotes the
correlation between temperature and time series, R > 0 denotes the positive correlation
between temperature and time series, R < 0 denotes the negative correlation between
temperature and time series, and R value is between-i and 1; According to the
correlation coefficient R, the significance is proved by t test. The confidence values are
a = 0.05 and a = 0.01. By consulting the t distribution table, which obeys the t
distribution with degree of freedom y = n-2, the regions and passing ranges with
significant correlation between air temperature and time series development are
obtained.
7.A system for constructing a high spatial-temporal resolution near-surface air
temperature model implementing the method for constructing a high spatial-temporal
resolution near-surface air temperature model according to any one of claims 1 to 6,
characterized in that the system for constructing the high spatial-temporal resolution
near-surface air temperature model comprises:
Regional division module, which is used to divide China into six regions according
to natural geographical environment and climatic conditions;
A weather state dividing module is used for dividing daily weather states into
sunny days and non-sunny days;
The temperature estimation module is used for estimating the temperature in sunny
days, the temperature in non-sunny days and the daily average temperature respectively;
The air temperature data model building module is used for building air
temperature data models in different weather states respectively;
The linear regression correction module is used for the linear regression correction
processing of the air temperature data model respectively.
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| CN117932559A (en) * | 2024-03-19 | 2024-04-26 | 南方海洋科学与工程广东省实验室(珠海) | Method and system for reconstructing surface temperature and air temperature of stationary satellite in long time sequence |
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2021
- 2021-08-15 AU AU2021105536A patent/AU2021105536A4/en not_active Ceased
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117932559A (en) * | 2024-03-19 | 2024-04-26 | 南方海洋科学与工程广东省实验室(珠海) | Method and system for reconstructing surface temperature and air temperature of stationary satellite in long time sequence |
| CN117932559B (en) * | 2024-03-19 | 2024-05-31 | 南方海洋科学与工程广东省实验室(珠海) | Method and system for reconstructing surface temperature and air temperature of stationary satellite in long time sequence |
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