55 results about "Surface air temperature" patented technology

A novel nonlinear method for area-wide near surface air temperature precision retrieval is described. The steps include: First, construct the 1st sub-model modelVEC1 to the f-th sub-model modelVECf. Establish and normalize raw data vectors of each gridded pixel sBlkVEC in the targeted area. Calculate the retrieved full maps (surfTf) of near surface air temperatures using each sub-model. Then, identify abnormal samples and define their near-range regions in surfTf Apply a selective arithmetic mean (SAM) approach to achieve precision temperature map surfT. And finally apply further modification to the pixels of surfT where pixlf∈badsurfTδf is true to all f=1, 2, 3, . . . .Using the super nonlinear algorithm, this invention provides a solution of retrieving near surface air temperature based on combinations of various factors (information fusion) to achieve satisfied prediction errors, which are independent of cloud levels and topographic characteristics. Specifically, the information fusion between space and ground surface enables reliable prediction of near surface air temperature maps overcoming the inference of cloud.

The invention provides an atmospheric temperature and humidity profile processing method and system with active and passive remote sensing combined. The system relates to the following steps that atmospheric microwave radiation brightness temperature data are detected and acquired through a microwave radiometer, cloud vertical structure data are detected and acquired through a cloud radar, and ground surface air temperature, humidity and air pressure data of a detecting area are detected and acquired through a ground surface meteorological element sensor; a data processor builds a three-layer feedforward BP neural network model for processing and training, and the BP neural network model with parameters optimized is obtained; the detected atmospheric microwave radiation brightness temperature data, the detected cloud vertical structure data and the detected ground surface air temperature, humidity and air pressure data are input into the BP neural network model by the data processor to serve as input layers, and the atmospheric temperature and humidity profile is obtained through processing and output of a BP neural network. Due to the atmospheric temperature and humidity profile processing method and system with active and passive remote sensing combined, the error for processing the atmospheric temperature and humidity profile in cloud weather is effectively reduced, and the accuracy and the effectiveness of the atmospheric profile are improved.

The invention relates to an automatic northern grassland desertification monitoring device. The automatic northern grassland desertification monitoring device comprises a support and a data acquiring and sending terminal. The bottom end of the support is fixedly installed under the earth's surface, a solar cell panel, a lead storage battery, a horizontal support rod and the data acquiring and sending terminal are installed on the support, and the data acquiring and sending terminal and the storage battery are placed in an airtight waterproof box. An overground air temperature and humidity sensor, an overground CO2 sensor and an air velocity sensor are installed on the horizontal support rod. A total radiation amount sensor, an earth's surface air temperature and humidity sensor, an earth's surface CO2 sensor, an evaporation amount sensor and a rainfall capacity sensor tightly adhere to the earth's surface. A soil moisture temperature sensor is buried under the ground, the sensors and the data acquiring and sending terminal are powered by the lead storage battery, and signal lines of the sensors are connected with the data acquiring and sending terminal through lead wires in the support. The automatic northern grassland desertification monitoring device has the advantages of being simple in structure, convenient to operate and capable of adapting to a working environment of a northern grassland and achieving real-time and long-term measurement work.

The invention relates to the electronic field of meteorology, in particular to portable ground meteorology automatic viewing integral equipment which is suitable for conveniently carrying in the wild for a single person and has the function of obtaining five meteorologic elements such as a ground air temperature, atmospheric pressure, humidity, a wind direction and a wind speed. The equipment comprises an integrated meteorologic sensor, an upright rod, a collection and communication box, a box body and a hand-holding terminal, wherein one end of the upright rod is connected with the box body, the integrated meteorologic sensor is connected with the other end of the upright rod, and the integrated meteorologic sensor is sequentially connected with the collection and communication box as well as the hand-holding terminal. According to the equipment, the integrated detection of the five meteorologic elements is realized, the size of the integral sensor is small, the power supplying mode is diversified, the power supplying time is long, the equipment can be carried on the back of the single person to work for a long time, and the operation is simple and convenient. The meteorologic information can be locally displayed by utilizing the hand-holding terminal and can be displayed and stored via transmitting to a meteorologic central station through the communication mode of a Big Dipper satellite or the communication modes of CDMA (code division multiple access) and shortwave.

The invention belongs to the technical field of atmospheric remote sensing, and discloses a method for estimating a near-surface atmospheric temperature by using thermal infrared data of a geostationary meteorological satellite. The satellite is used for observing brightness temperature, meteorological station and numerical prediction model data to obtain a representative thermal infrared observation brightness temperature and near-surface atmospheric temperature; satellite cloud detection products are used for obtaining matched data sets for the observation brightness temperature, the stationactually measured temperature and auxiliary data under cloudless conditions; based on a stepwise regression method, the relationship between the radiation temperature of satellite observation, atmospheric pressure, relative humidity, a satellite observation angle, Julian daily parameters and the like and near-surface atmospheric temperature is analyzed, and key factors used for estimating the atmospheric temperature are determined; and a inversion model of near-surface temperature estimation is constructed by using a neural network technology. The method can realize the purpose of inversion of the near-surface atmospheric temperature under the clear sky condition of the thermal infrared data of the stationary meteorological satellite.

The invention relates o a near-surface temperature inversion method based on FY-2C thermal-infrared waveband data as well as NCEP (National Centers for Environmental Prediction) atmospheric profile data. The inversion method comprises the steps of A) calculating an atmospheric transmittance tau[i](theta), wherein the tau[i](theta) represents the atmospheric transmittance at the waveband i when the observation zenith angle is theta; B) respectively calculating land surface emissivity epsilon[IR1] and epsilon[IR2] of FY-2C at the wavebands IR1 and IR2; C) calculating a temperature change rate Rt(h) in different atmospheric layers, wherein Rt(h) is the reduced rate of temperature at the height h; D) calculating steam content proportion Rw(h) in different atmospheric layers, wherein the Rw(h) represents the percentage of steam in total water content at the layer whose height is h; and E) calculating the near-surface temperature Ta via the formula Ta=(B0+B1TIR1+B2TIR2-bT0) / a.

The invention discloses a quality control method of surface air temperature data. A random forest algorithm optimized through a genetic algorithm (GA) is used to a meteorological observation station to carry out meteorological element quality control. According to the method, first, surface meteorological observational data in a certain range is collected; afterwards, basic quality control is carried out on the collected data; subsequently, an adjacent station site which is higher in relativity is sought according to variable importance under a random forest model by using the genetic algorithm; training is carried out on the data according to a time sequence and a spatial sequence by using a modified random forest method; a quality control model based on the modified random forest method is obtained; regression prediction is carried out by utilizing a test set; finally, the data are compared with the actual observational data of a target station; the capability that the model identifies wrong data in the observational data is observed. By using the method, valuable meteorological data information accumulated for years by China is effectively utilized; compared with a conventional method, the method is more high-efficient, and is high in accuracy and high in adaptability in a quality control aspect; the quality of the meteorological data of the meteorological observation station is improved.

The invention discloses a near-surface air temperature inversion method comprising the following steps: establishing an original data record set of an unmanned weather station; constructing a first sub-pattern learning set and a first sub-pattern validation set; and acquiring a second sub-pattern to a fth sub-pattern, performing near-surface air temperature inversion to acquire a near-surface air temperature inversion image map of a target zone, and performing error correction to acquire a corrected near-surface air temperature inversion image map. According to the near-surface air temperature inversion method disclosed by the invention, the near-surface air temperature inversion is performed by collecting actually-measured air temperature of the unmanned weather station, collecting meteorological satellite data, DEM data and astronomy and calendar rules and also adopting a super nonlinear algorithm, and the near-surface air temperature inversion image map is then calculated by using a high-performance computer. Results show that the near-surface air temperature inversion method disclosed by the invention is relatively high in pattern accuracy, high in result reliability and strong in generalization ability, and ensures that the interferences of clouds, terrains and the like can be overcome; and a constructed CPU+GPU heterogeneously-cooperative parallel computer ensures that the computation speed can be increased by more than 1000 times, so that the near-surface air temperature inversion method is convenient for large-area application and computing capacity expansion.

The invention discloses a photo-thermal interferometer for monitoring the concentration of particulate matters in the atmosphere and a monitoring method of the photo-thermal interferometer. The photo-thermal interferometer comprises a laser, a first quarter wave plate, a parallel panel pair, an aerosol chamber, a corner reflecting prism, a modulation laser, a non-depolarization beam splitting prism and a polarization beam splitting prism photoelectric detection device. The monitoring method comprises the steps of filtering sampled air by utilizing filter paper to form dust-containing filter paper, and illuminating the dust-containing filter paper by virtue of modulated laser to cause the variation of surface air temperature and refractive index. If components of the particulate matters in the air are constant, the mass of particulate matters on the filter paper is acquired by measuring the variation of the air refractive index, and the mass of particulate matters on the filter paper is equivalent to the mass concentration of the particulate matters in the air by virtue of a sampling volume.

The invention provides a satellite remote sensing data-based near-surface air temperature estimation method under cloud conditions. The method comprises the following steps: (1) acquiring historical data of a meteorological station in a research area; (2) preprocessing the historical data of the atmospheric precipitation, the relative humidity near the ground, the cloud top temperature, the cloudtop height and the cloud optical thickness to obtain a spatio-temporal matching data set; (3) taking the temperature of the instrument shelter as the output of a neural network, taking the data set asthe input of the neural network, and constructing the neural network; (4) obtaining a data set of an area to be subjected to air temperature estimation; (5) carrying out time and space interpolationby utilizing a cubic spline interpolation method according to the latitude, longitude and time information of the meteorological satellite to obtain a data set matched with the space-time of the meteorological satellite; And (6) inputting the data set matched with the space-time space of the meteorological satellite into the constructed neural network to estimate the near-surface air temperature under the cloud day condition. The method is simple and easy to implement and high in precision.

The invention discloses a surface air temperature quality control method based on spatial correlation and surface fitting. The method includes the steps of collecting air temperature data of referencestations near a target station within a certain period of time, conducting basic quality control, determining the relative positions of the reference stations and the target station through the Euclidean distances and azimuth angles between the reference stations and the target station, establishing a spatial correlation function by means of the spatial correlation of air temperature elements ofeach reference station and the target station, conducting surface fitting on a test set through sample strips B to obtain a quality control model combining the spatial correlation and the surface fitting of the sample strips B, predicting the air temperature data of the target station through the test set, comparing a predicted value with an actually-observed value, implanting artificial errors into original data of the target station, and observing the error detection rate of the model. By means of the method, the surface meteorological data in China is effectively used, and a better prediction effect and error detection effect are achieved compared with a traditional spatial quality control algorithm.

The invention relates to a mining winter cooling and mine heat utilization system, which consists of a ground part and an underground part. An air cooler of the ground part is connected with a high-pressure cold water supply pipe and a ground cold water return pipe; the high-pressure cold water supply pipe is connected with a water regulating valve, a water inflow regulating valve and a water chiller set respectively; the water chiller set is connected with the high-pressure cold water supply pipe through a water outlet regulating valve; a ground water pump is connected with a shaft cold water supply pipe; an underground cold water supply pipe and a low-pressure cold water return pipe are connected with a mining heat exchanger at an underground part; the mining heat exchanger is connected with a high-pressure combined type air cooler through an underground cold water inlet pipe and an underground cold water return pipe, and is connected with an air cooler through the shaft cold water supply pipe and a shaft cold water return pipe; and the air cooler is connected with a blower and a heat-insulating air pipe. During operation, when the surface air temperature is low enough, the cold water at the temperature about 5 DEG C is obtained to prepare cold air with a temperature ranging from 12 DEG C to 18 DEG C through an underground water pump and the mining high-pressure combined type air cooler, and the cold air is sent to a working site; if the surface air temperature is not cold enough, a natural cold source and the water chiller set are taken as primary and secondary cold sources; and hot air with a temperature ranging from 8 DEG C to 12 DEG C is prepared through the air cooler and the blower for recycle.

The invention belongs to the technical field of temperature estimation, and discloses a high-temporal-spatial-resolution near-surface air temperature reconstruction method and system, and equipment, and the construction method of a high-temporal-spatial-resolution near-surface air temperature model comprises the steps: dividing the weather of 1979-2018 into weather under a sunny day condition and weather under a non-sunny day condition, and through an existing reanalysis data set; establishing daily maximum and minimum temperature models in different weather states to estimate maximum and minimum temperature values by utilizing meteorological station data and combining with hour temperature data of a Chinese surface meteorological element driven meteorological station and MODIS daily Ts, and obtaining a daily average temperature data set through summation and averaging; and after linear regression correction, finally outputting a Chinese daily value near-surface air temperature data set from 1979 to 2018, wherein the spatial resolution is 0.1 degree. The data set constructed by the method can better estimate the daily maximum temperature, minimum temperature and average temperature, and is convenient for further analyzing seasonal and periodic change conditions of the temperature in the Chinese region.

The invention discloses a mass concrete intelligent temperature measuring device system and an installation method thereof. The device comprises a bottom plate reinforcing bar mesh, a top plate reinforcing bar mesh and a cooling water pool, wherein the bottom plate reinforcing bar mesh is fixedly connected with the top plate reinforcing bar mesh through a plurality of supporting reinforcing bars,a wireless measurement and control terminal is fixedly mounted at the top end of the top plate reinforcing bar mesh through a supporting column, the wireless measurement and control terminal is electrically connected with a plurality of thermodetectors through conducting wires, the plurality of thermodetectors are fixedly arranged at different positions of the top end of the bottom plate reinforcing bar mesh, and the detection probe of the thermodetector is fixed through a binding wire. According to the invention, the temperature values of all monitoring points can be collected at the same time according to any set frequency through temperature measurement conducting wires and temperature detectors which are pre-buried in advance, so that the data precision reaches 0.1 DEG C, and the purpose of real-time synchronous monitoring of all points is achieved; and temperature measurement results can be synchronously checked on mobile equipment such as a PC terminal or a mobile phone and the like, and the temperature measurement results include automatically generated temperature curves and chart values such as concrete surface air temperature difference, inner surface temperature difference and the like.

The invention belongs to the technical field of near-surface air temperature estimation, and discloses a near-surface air temperature estimation method and system, a storage medium and equipment, and the method comprises the steps of obtaining four necessary satellite data inversion parameters related to the near-surface air temperature and seven alternative parameters which can be obtained by a ground meteorological station, and carrying out the preprocessing of the data; using fast Fourier transform to carry out fast conversion on the time sequence information of various observation elements, and extracting a public period between meteorological elements related to the temperature of a meteorological station; constructing a long short-term memory (LSTM) neural network, and training the LSTM network by taking the obtained common period and the estimation time as a data group to obtain an estimation temperature; and determining an estimation model precision evaluation index, constructing an air temperature estimation model based on the FFT-LSTM neural network, estimating the near-surface air temperature, and performing precision evaluation on the model. According to the invention, the model has a good estimation effect and high precision, and the defect of uneven distribution of meteorological station areas is overcome.

The invention discloses an improved nuclear regression ground temperature observation data quality control method. The method comprises the following steps: S1, screening experimental data; S2, introducing a kernel regression method and improving ground air temperature observation data needing to be applied; S3, improving the applicability of the multi-site kernel regression method on the basis ofS2; and S4, introducing an adaptive algorithm and a particle swarm algorithm to improve a window width coefficient in a kernel regression method, and performing regression prediction and corresponding quality control according to the principle. According to the invention, on the aspect of the method, a kernel regression method is introduced, ground temperature observation data is improved, meanwhile, the window width coefficient in the kernel regression method is further improved through the self-adaption and particle swarm optimization algorithm, and the direction of a traditional quality control method is expanded; in the aspect of quality control effect, the method has good effects in the aspects of prediction precision, universality, error detection rate and the like in the field of quality control, and is more beneficial to research and application of quality control of ground air temperature observation data.

The invention provides a ground air temperature quality control method based on a cooperative Kriging method. The ground air temperature quality control method based on the cooperative Kriging methodcomprises the following steps: in a first step, relative humidity observation data and air temperature observation data of meteorological observation stations are collected, basic quality control is performed on collected data, and a relative humidity data set and a temperature observation data set are obtained; in a second step, variation function values of humidity and temperature are calculatedseparately; in a third step, relationship between temperature and humidity is taken into consideration, a covariate functional equation is established, and a covariate function value is calculated; in a fourth step, a cooperative Kriging method quality control equation is established to calculate ground air temperature prediction results; in a fifth step, a prediction value in the fourth step iscompared with an actual observation value, and a model is evaluated. The beneficial effects of the method disclosed in the invention are that automatic real time air temperature observation of the meteorological stations can be improved via the ground air temperature quality control method based on the cooperative Kriging method.

The invention discloses a near-sea-surface air temperature inversion method. The method specifically comprises the steps: 1, selecting a sea area, and extracting and preprocessing data in the area; 2,determining input and output parameters, and establishing a near-sea-surface air temperature inversion model based on the recurrent neural network; 3, carring out weight initialization t; and 4, adding an L2 parameter paradigm penalty term; 5, initializing a BPTT algorithm; 6, performing forward propagation to obtain a predicted value; 7, updating the connection weight through back propagation; 8, calculating a loss function; and 9, returning and storing the metwork parameters. Compared with most near-sea-surface air temperature methods which only use linear regression and shallow neural network methods for modeling, the near-sea-surface air temperature inversion method has the advantages that the deep neural network is used for modeling and training, so that the near-sea-surface air temperature inversion precision is improved.

The invention provides a near-surface air temperature inversion method based on temperature, emissivity and deep learning, and the method comprises the four steps: building a thermal radiation transmission equation, constructing an expert knowledge base, constructing a high-precision database, and calculating and verifying an inversion result. Parameters required by inversion are determined through a radiation transfer mechanism, near-surface heat interaction influence is considered from the perspective of physics, and high inversion precision is realized by using surface temperature and emissivity as priori knowledge and using simulation data and acquired high-precision data, so that the advantages of a physical model and deep learning are fully utilized, and the method is suitable for large-scale popularization and application. According to the near-surface air temperature inversion method based on the DL-NN algorithm, a high-precision near-surface air temperature inversion result can be obtained by combining the near-surface air temperature and the DL-NN algorithm, and the DL-NN algorithm is adopted to process the ill-conditioned problem of the near-surface air temperature inversion and process the nonlinear relationship between the near-surface air temperature and the atmospheric average action temperature in different seasons and regions, so that the inversion precision is improved, and the universality is improved.

The invention provides a method for estimating near-surface air temperature by using remote sensing, which comprises the following steps: taking global ground observation data and reanalysis data as a data source, and equally dividing the global into a plurality of sub-regions according to latitude and land coverage types; establishing a database for each area by using ground observation data and reanalysis data in recent 10 years; establishing a linear or non-linear model, and recording the linear or non-linear model as Model-1; meanwhile, establishing a machine learning model with the same sample library, and recording the machine learning model as Model-2; carrying out weighted fusion on the two models to obtain a final model Model-fused of each sub-region; and for a certain area, based on the established Model-fused model, inputting parameters obtained by remote sensing to obtain a corresponding near-surface temperature. According to the invention, high-precision and time-space continuous near-surface temperature remote sensing estimation based on a conventional remote sensing product is realized, and the defects of low precision, coarse time-space resolution and even dependence on a specific sensor of an existing product are overcome.

The invention discloses a prediction method for rock landslide susceptibility in a frozen-thawed zone of a high-altitude mountainous area. The prediction method solves the problem that in the prior art, long-term and real-time deformation data is obtained by relying on sensors additionally installed at specific sites of a slope to realize landslide prediction. According to the prediction method for rock landslide susceptibility in the frozen-thawed zone of the high-altitude mountainous area, based on surface air temperature data of a prediction place and an SAR image, a surface air temperaturechange period of the prediction place is divided according to the surface air temperature data, the time sequence-temperature data and time sequence-rock deformation quantity data at each stage of the change period are extracted, then the identified critical temperature index is used for determining a function relationship between the surface temperature change and the rock mass deformation, theaverage freezing temperature-rock deformation rate of the perdition place is calculated, finally, based on critical conditions of landslide generation, the degree of landslide susceptibility of the prediction place is judged, and the prediction of landslide susceptibility is realized. The prediction method solves the problem of landslide susceptibility prediction of a slope which does not have thecondition to implement sensor monitoring and early warning. The implementation cost is economic, and the perdition method is suitable for high-altitude areas.

The invention provides a double-ball type wave velocity and wavelength remote sensing device. The double-ball type wave velocity and wavelength remote sensing device comprises a main ball, an auxiliary ball, an interconnection barrel, side wings, a tail wing, a temperature sensor, an angle sensor, a Hall sensor, a hydraulic pump, a water turbine, a micro-generator, a storage battery, a circuit board, a tubular antenna and the like, wherein the main ball, the interconnection barrel, the auxiliary ball, the tail wing and the side wings constitute a main device body; the hydraulic pump, the water turbine, the micro-generator and the storage battery constitute a power supply mechanism. The device can measure wave velocity and wavelength and can further measure wave height, wave inclination, shallow water temperature, near sea surface air temperature, marine vortex parameters and the like, and all sea parameters can be remotely sensed through radio transmission. The device has the advantages of simple structure, low cost and long service life and is easy to operate, environment-friendly, convenient and practicable, the popularization is facilitated, self-sufficiency of a power supply is realized, and the device is applicable to marine engineering, marine construction, marine scientific investigation and the like.

The invention discloses a drought index calculation method based on saturated water vapor pressure difference, and the method comprises the steps: defining a standardized saturated water vapor pressure difference index as a drought index, representing the drought condition, and monitoring the drought condition of a land ecosystem; the standardized saturated water vapor pressure difference index calculation method comprises the following steps: acquiring meteorological data of ground temperature, ground air pressure and relative humidity of a meteorological station or a field observation station; calculating saturated water vapor pressure and actual water vapor pressure according to meteorological data; and calculating a standardized saturated water vapor pressure difference index according to the saturated water vapor pressure difference. The new drought index provided by the invention is applied to drought monitoring of agricultural or ecological systems, can reflect drought conditions except atmospheric precipitation supply shortage, is simple in calculation method and accurate and stable in result, and observation data participating in calculation is easy to obtain and does not depend on additional observation instruments, so that the installation and maintenance cost of the additional instruments can be avoided, time and space continuity is achieved, and drought occurring in land ecosystems such as farmlands, forests and grassland can be described more accurately.

The invention discloses a surface temperature high-temperature and low-temperature data set reconstruction method. Different estimation models of near-surface air temperature data are constructed for different weathers through the division of different weathers, the maximum value and minimum value of the air temperature and the average air temperature of each day are calculated then temperature correction models of different areas are built according to natural conditions of different areas in China, correction and precision evaluation are further conducted on the highest temperature, the lowest temperature and the average temperature, a highest temperature data set, a lowest temperature data set and an average temperature data set are built, and fluctuation trends of the highest temperature and the lowest temperature are analyzed on the basis of the highest temperature data set, the lowest temperature data set and the average temperature data set. Sunny day and non-sunny day weather states are distinguished, a plurality of evaluation indexes are adopted to analyze the temporal and spatial change trend of near-surface air temperature, CMFD data and an ERA5 data set are taken as reanalysis data sources, the method has relatively high spatial resolution and reliable precision, the regional air temperature change condition can be accurately captured, and climate correlation analysis and research on other earth surface driving factors are facilitated.

The invention discloses a calculation method for satellite observation of an urban surface temperature (LST) angle effect. The method comprises the following steps: S1, reading a remote sensing image and meteorological station data; s2, the influence of weather changes on the surface temperature (LST) is removed; and S3, calculating the directivity of the MODIS surface temperature (LST). According to the method, on the basis of evaluating the residual atmospheric effect in a temperature product, the item of removing the atmospheric effect influence by using a water body is omitted, so that the method does not need the water body surface temperature and the near-surface air temperature as input data any more, and the defect that an existing method is only suitable for coastal (water) cities is overcome; according to the method, large-range and long-time calculation of the satellite angle effect can be realized, the universality of the method applied to evaluation of the urban surface satellite surface temperature (LST) angle effect is greatly expanded, and the satellite angle effect of global cities can be calculated.

This application provides a remote sensing monitoring method for near-surface air temperature, including: obtaining remote sensing products including surface temperature, leaf area index, snow coverage ratio and altitude data of the area to be measured; performing spatio-temporal reconstruction analysis on the remote sensing products, Obtain the surface temperature, leaf area index and snow cover ratio data of the time-spatial integrity of the area to be measured; according to the preset near-surface air temperature calculation model, and the surface temperature and leaf area of ​​the corresponding time according to the time-spatial integrity Index, snow cover ratio and altitude data to obtain the temperature value of the area to be measured. In the embodiment of this application, the remote sensing monitoring of air temperature is realized by establishing the statistical relationship between air temperature and surface temperature, leaf area index, snow cover ratio and altitude, and these data are obtained quickly and accurately. At the same time, the spatial resolution of remote sensing products is getting higher and higher, so that it can provide fast, accurate and high spatial resolution temperature monitoring services at a lower cost.

The invention discloses a ground air temperature quality control method based on cosine similarity and moving surface fitting, and relates to the technical field of ground air temperature quality control algorithms. The method specifically comprises the following steps: selecting the daily average temperature time series data of a target station and the daily average temperature time series data of an adjacent station at the same time slot; calculating a cosine similarity COS value and a root mean square error RMSE value of each piece of adjacent station data and the target station data, and selecting a station with a high COS value and a small RMSE value as an experiment reference station; fitting a spatial correlation curved surface by using a moving curved surface fitting algorithm, andpredicting daily average temperature data of the target station to obtain a predicted value; analyzing results in combination with evaluation indexes, wherein the evaluation indexes include a root-mean-square error, an average absolute error and a Nash coefficient; and comparing the predicted value with the observed value, if the difference value meets the condition, considering that the data passes the inspection, and if the difference value does not meet the condition, considering that the data is suspicious, and marking the suspicious data. And the accuracy, stability and adaptability of the algorithm are effectively improved.

The invention discloses a method for observing atmospheric water vapor of Qinghai-Tibet Plateau through sounding. The method comprises the steps of S100 collecting data and processing the data; S200 performing sounding observation to obtain data; and S300 calculating the total amount of sounding observation atmospheric water vapor in the sounding observation area. According to the the method for observing atmospheric water vapor of Qinghai-Tibet Plateau through sounding, a database is established by collecting meteorological data over the years of an area with a foundation GPS station and a sounding observation station; a GPS remote sensing atmospheric water vapor total amount curve graph of the foundation GPS station over the years is creatively used as a basis; the solar zenith angle isproposed; the ground temperature of the environment where the sounding observation station is located is an important factor influencing the total amount of atmospheric water vapor for sounding observation; a revision formula for sounding observation of the total amount of atmospheric water vapor is creatively proposed; and the accuracy of sounding observation of the total amount of atmospheric water vapor is greatly improved.