CN114371519A - Foundation microwave radiometer atmospheric temperature and humidity profile inversion method based on non-deviation bright temperature - Google Patents
Foundation microwave radiometer atmospheric temperature and humidity profile inversion method based on non-deviation bright temperature Download PDFInfo
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Abstract
The invention discloses a foundation microwave radiometer atmospheric temperature and humidity profile inversion method based on non-deviation bright temperature, which comprises the following steps: step one, selecting data; step two, data processing; constructing a temperature and humidity profile inversion model by using a neural network algorithm; and step four, analyzing and applying the accuracy of the atmospheric temperature and humidity profile inversion model. The method solves the problems that sounding data lack liquid water information and the deviation of a microwave radiometer observation bright temperature and sounding simulation bright temperature exists in a system, and improves the inversion accuracy of the atmosphere temperature and humidity profile based on the foundation microwave radiometer by the constructed atmospheric temperature and humidity profile inversion method based on the non-deviation bright temperature.
Description
Technical Field
The invention belongs to the field of space remote sensing, relates to an atmospheric temperature and humidity profile inversion method, and particularly relates to an atmospheric temperature and humidity profile inversion algorithm based on non-deviation brightness temperature of a foundation microwave radiometer.
Background
The atmospheric temperature and humidity profiles with high space-time resolution have important significance for understanding the thermal and dynamic structures of weather processes with different scales. Currently, sounding data can provide atmospheric temperature and humidity vertical profiles with higher accuracy, but the observation cost is high, the spatial resolution is low, the distance between sounding stations is about 300km or more, and the time resolution (12h) is low, so that the fine variation of the atmospheric temperature and humidity vertical profiles cannot be provided. The meteorological satellite is influenced by a complex underlying surface, so that the detection accuracy of the temperature and humidity profile of the meteorological satellite on the near ground is low. The microwave radiometer is a new type of detector belonging to passive remote sensing, mainly measures the downward radiation from the earth atmosphere, can continuously detect the temperature and humidity profile information of atmosphere with the height of 0-10km near the stratum, and is a useful supplement for the sounding data and satellite data.
In recent years, with the rapid development of foundation microwave radiometers, a great deal of research is carried out on inversion methods at home and abroad, and common methods comprise an optimal estimation method, a one-dimensional variational inversion method, a forward model inversion algorithm, a regression algorithm, a neural network method and the like. And the Huangxinyou and the like respectively adopt a neural network algorithm and a multiple linear regression method to invert the atmospheric temperature and water vapor density profile, and the result shows that the inversion result of the BP neural network is more excellent. And refining atmospheric temperature and humidity profiles in different seasons by Liuya and the like for inversion, and finding that the BP neural network algorithm is superior to the inversion result of a Stuttgart neural network simulator carried by a microwave radiometer. The Bayansong and the like establish a BP neural network inversion model based on correction data through a bright temperature correction method, and the inversion accuracy of the atmospheric temperature and humidity profile is improved. In conclusion, the neural network algorithm has higher precision for the temperature and humidity profile inversion of the foundation microwave radiometer and is widely applied.
In view of the great potential of the vertical ground-based detection data in improving medium and small-scale forecasting services, the microwave radiometer based on the ground will be one of the important fields of the future atmospheric detection service development. At present, the international ground-based microwave radiometer mainly comprises RPG-HATPRO in Germany and MP-3000 in America, the development is rapid in recent years in China, QFW-6000 in Middle 22 and MWP967KV in weapons 206 are main devices, and the inversion methods used in the devices are basically neural network inversion algorithms. The related profile product is widely applied to the fields of weather modification, major event weather guarantee service, aeronautical weather, urban pollution monitoring and the like. However, sounding data used in a traditional foundation microwave radiometer inversion model training data set lack liquid water information, and certain deviation exists between simulated brightness temperature and observed brightness temperature, so that the accuracy of the foundation microwave radiometer in inverting the temperature and humidity profile is limited. Aiming at the problem of accuracy reduction of a model for simulating bright temperature training in practical inversion application of equipment, an effective solution is not provided at present.
Disclosure of Invention
The invention provides an atmospheric temperature and humidity profile inversion method of a foundation microwave radiometer based on non-deviation bright temperature, which is used for solving the problems that sounding data lack liquid water information and deviation exists between the observation bright temperature of the microwave radiometer and the simulated bright temperature of sounding.
The technical scheme adopted by the invention is as follows;
an atmospheric temperature and humidity profile inversion method of a foundation microwave radiometer based on non-deviation bright temperature comprises the following steps:
step one, data selection: selecting data of the nearest sounding site and ERA5 of the grid point according to the longitude and latitude where the microwave radiometer is installed, analyzing the data, and removing precipitation data;
step two, data processing: based on a MonoRTM radiation transmission mode, temperature and humidity information in sounding data and liquid water information of fusion ERA5 reanalysis data are utilized, and simulated brightness temperature of fusion liquid water information of each channel of the foundation microwave radiometer is calculated; comparing the simulated bright temperature with the observed bright temperature of the foundation microwave radiometer, eliminating abnormal data by using the Lauda criterion to obtain the bright temperature of the foundation microwave radiometer subjected to quality control, namely the non-deviation bright temperature, and simultaneously taking a plurality of groups of non-deviation bright temperatures at equal time intervals within half an hour before and after the observation of the sounding space to obtain stable sample data to form training sample data together with the temperature and humidity information of the sounding space data;
and step three, training a temperature and humidity profile inversion algorithm by using a neural network algorithm to construct an inversion model. The method comprises the following steps: a single hidden layer BP neural network of a three-layer network is used, namely an input layer, a hidden layer and an output layer are all fully connected layers; the node number of the input layer is the number of channels of the foundation microwave radiometer, the input data is the simulated bright temperature data of each channel, the node number of the output layer is the number of vertical layers of the atmospheric parameter profile, and the output and input are the atmospheric temperature, relative humidity and absolute humidity data of each height layer; 75% of data in the training sample data is used for training, 15% of data is used for verification, and 10% of data is used for testing the training precision of the inversion model; optimizing and adjusting the node number of the hidden layer according to the training precision of the model;
and fourthly, analyzing the application precision of the inversion of the atmospheric temperature and humidity profile, wherein the analysis comprises statistical analysis and case analysis.
Further, in the second step, the simulated brightness temperature of the fused liquid water information of each channel of the foundation microwave radiometer is calculated by utilizing the temperature and humidity information of the sounding data and the fused ERA5 reanalyzed data, and specifically:
interpolating liquid water information of the reanalysis data of the detected air temperature and humidity information and ERA5 to the same air pressure layer;
generating a TAPE5 file by using the air pressure, height and temperature and humidity information of the sounding data;
in a MonoRTM radiation transmission model, modifying channel information in an input file according to a TAPE5 file;
performing first simulation by using case3 user-defined downlink radiation;
compiling to obtain corresponding simulated brightness temperature and generating a TAPE7 file;
adding ERA5 into TAPE7 file generated by case3 and analyzing liquid water information of the data;
modifying the channel information in the input file according to the TAPE7 file;
a second simulation was performed using case 5;
generating simulated brightness temperature of the sounding data fused with the liquid water information.
Further, the statistical analysis of the precision in the third step and the fourth step is specifically quantitative evaluation through calculating deviation (Bias) and Root Mean Square Error (RMSE), and the expression is as follows:
in the above two formulae, xiFor the inverted atmospheric temperature and humidity profile, xobs，iTo probe the null profile value, n represents the number of test samples.
The invention has the beneficial effects that:
the invention relates to an atmospheric temperature and humidity profile inversion method of a foundation microwave radiometer based on non-deviation bright temperature, which effectively solves the problem that the deviation between the bright temperature and the actually observed bright temperature based on radiation transmission mode simulation calculation reduces the accuracy of inversion of temperature and humidity profiles. Compared with actual sounding observation data, the inversion accuracy of the atmospheric temperature profile of the non-deviation brightness temperature inversion is below 2km, the root mean square error RMSE is below 1K, the root mean square error RMSE in the height range of 2-10km is less than 1.5K, the root mean square error RMSE of relative humidity is less than 15% in the height range of 0-10km, and the root mean square error of absolute humidity in the height range of 0-10km is less than 1.2g/m3The accuracy and the application effect of the foundation microwave radiometer for inverting the atmospheric temperature and humidity profile are obviously improved.
Drawings
FIG. 1 is a schematic diagram of the training accuracy of an inverted atmospheric temperature profile model according to the present invention;
FIG. 2 is a schematic diagram of the training accuracy of the atmospheric relative humidity profile model inverted by the present invention;
FIG. 3 is a schematic diagram of the training accuracy of the atmosphere absolute humidity profile model inverted by the present invention;
FIG. 4 is a comparison graph of the inversion accuracy of the temperature profile and the RPG self-contained inversion algorithm;
FIG. 5 is a comparison graph of the inversion accuracy of the relative humidity profile and the RPG self-contained inversion algorithm inverted by the present invention;
FIG. 6 is a comparison graph of the inversion accuracy of the absolute humidity profile and the RPG self-contained inversion algorithm inverted by the present invention;
FIG. 7 is an analysis diagram of a comparative example of accuracy of an inversion model atmospheric temperature profile trained according to the present invention;
FIG. 8 is an analysis chart of a comparative example of the accuracy of the atmospheric relative humidity profile of an inversion model trained by the present invention;
FIG. 9 is an analysis diagram of a comparative example of the accuracy of the atmospheric absolute humidity profile of an inversion model trained according to the present invention;
FIG. 10 is a plot of the time-of-day sequence of the inverted atmospheric temperature profile of the present invention;
FIG. 11 is a plot of the time-of-day sequence of the atmospheric relative humidity profile inverted by the present invention;
FIG. 12 is a plot of the time-of-day sequence of the inverted atmospheric absolute humidity profile of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
According to the invention, by fusing the liquid water information of the corresponding station in the reanalysis data ERA5, the simulation bright temperature precision is effectively improved, and the inversion model training precision and the inversion of the temperature and humidity profile are improved; model training inversion is carried out by using non-deviation bright temperature, the inversion accuracy of the model is effectively improved, and the influence of deviation on the inversion accuracy of the temperature and humidity profile is avoided.
An atmospheric temperature and humidity profile inversion algorithm based on non-deviation brightness temperature comprises the following steps:
step one, data selection: selecting exploration data of a proper station and ERA5 of a grid point according to the longitude and latitude where the microwave radiometer is installed, analyzing the data, and removing precipitation data;
step two, data processing: by utilizing a MonoRTM radiation transmission mode, temperature and humidity information of sounding data and liquid water information of fusion ERA5 reanalysis data are utilized, and simulated brightness temperature of fusion liquid water information of each channel of the foundation microwave radiometer is obtained through calculation, and the method specifically comprises the following steps:
interpolating liquid water information of the reanalysis data of the detected air temperature and humidity information and ERA5 to the same air pressure layer;
generating a TAPE5 file by using the air pressure, height and temperature and humidity information of the sounding data;
in a MonoRTM radiation transmission model, modifying channel information in an input file according to a TAPE5 file;
performing first simulation by using case3 user-defined downlink radiation;
compiling to obtain corresponding simulated brightness temperature and generating a TAPE7 file;
adding ERA5 into TAPE7 file generated by case3 and analyzing liquid water information of the data;
modifying the channel information in the input file according to the TAPE7 file;
a second simulation was performed using case 5;
generating simulated brightness temperature of the sounding data fused with the liquid water information.
Comparing the simulated bright temperature with the observed bright temperature of the foundation microwave radiometer, eliminating abnormal data by using the Lauda criterion to obtain the bright temperature of the foundation microwave radiometer subjected to quality control, namely the non-deviation bright temperature, and simultaneously taking a plurality of groups of non-deviation bright temperatures at equal time intervals within half an hour before and after the observation of the sounding space to obtain stable sample data to form training sample data together with the temperature and humidity information of the sounding space data;
step three, training a temperature and humidity profile inversion algorithm by using a neural network algorithm, and constructing an inversion model, wherein the method specifically comprises the following steps: the method comprises the steps that a single hidden layer BP neural network of a three-layer network is used by fusing liquid water brightness temperature, namely an input layer, a hidden layer and an output layer are all fully connected layers, corresponding nodes are respectively 16, 50 and 83, 16 nodes of the input layer correspond to simulated brightness temperature data of 16 channels, 83 nodes of the output layer correspond to atmospheric temperature, relative humidity and absolute humidity of 83 height layers, 75% of data in training sample data are used for training, and 15% of data are used for verification and 10% of data are used for testing;
the neural network inversion model established by using the unbiased brightness temperature and sounding data has higher reliability, the temperature and humidity profile accuracy inverted by the method is quantitatively analyzed by using Root Mean Square Error (RMSE), and the RMSE expression is as follows:
x in the formula (1)iFor the inverted atmospheric temperature and humidity profile, xobs，iFor sounding profile values, n represents the number of test samples;
FIG. 1 is a training precision diagram of an atmospheric temperature and humidity profile inversion model, wherein the root mean square error RMSE of the atmospheric temperature profile inversion precision below 2km is below 1K, and the root mean square error RMSE of the height range of 2-10km is less than 1.5K.
FIG. 2 is a training accuracy graph of the inversion of the model of the relative humidity profile of the present invention, with the root mean square error RMSE of the relative humidity being less than 15% over the height range of 0-10 km.
FIG. 3 is a training accuracy diagram of an inverted absolute humidity profile model, with root mean square errors of less than 1.2g/m for absolute humidity in the height range of 0-10km3。
And step four, application and analysis of the atmospheric temperature and humidity profile inversion model. Specifically, the accuracy of a temperature and humidity profile inversion model for non-deviation bright temperature data training is compared with the accuracy of an RPG product with an inversion algorithm, the deviation Bias and the root mean square error RMSE are calculated based on sounding data, the dotted line in the graphs of FIG. 4, FIG. 5 and FIG. 6 is a model for non-deviation bright temperature training, and the solid line is an RPG temperature and humidity profile inversion model. The deviation Bias expression is as follows:
x in the formula (2)iFor the inverted atmospheric temperature and humidity profile, xobs，iTo probe the null profile value, n represents the number of test samples.
FIG. 4 is a comparison of the temperature profile inverted by the non-deviation bright temperature inversion method and the accuracy inverted by the RPG self-contained inversion algorithm, the deviation of the inversion method is about 0, the temperature root mean square error is less than 2km and within 1K, the height of 2-10km and within 1.8K, the accuracy of each layer is obviously superior to that of the RPG self-contained inversion algorithm, and the maximum root mean square error is improved by more than 1K.
FIG. 5 is a comparison between the relative humidity profile inverted by the non-deviation brightness temperature inversion method and the inversion accuracy of the RPG self-contained inversion algorithm, wherein the statistical deviation of the inversion method provided by the invention is basically near 0, and the maximum deviation of the RPG self-contained inversion algorithm is more than 20%; the relative humidity profile root mean square error of the non-deviation brightness temperature inversion is basically within 15% in the height range of 0-10km, the relative humidity profile root mean square error is obviously superior to an RPG (resilient packet Generator) self-contained inversion algorithm, and the maximum improvement reaches more than 20% near the height of 6 km.
FIG. 6 is a comparison between the absolute humidity profile inverted by the non-deviation brightness temperature inversion method of the present invention and the accuracy of the RPG self-contained inversion algorithm, in which the statistical deviation of the inversion method of the present invention is substantially around 0, and the maximum deviation of the RPG self-contained inversion algorithm reaches + -2K; the root mean square error of the absolute humidity profile of the non-deviation brightness temperature inversion is basically reduced along with the increase of the height and is maximum 1.2g/m3About, is obviously superior to the RPG self-contained inversion algorithm, and the maximum improvement reaches 1.5g/m3The above.
In conclusion, the non-deviation bright temperature inversion method provided by the invention obviously improves the accuracy of the profile of the foundation microwave radiometer for inverting the temperature, the relative humidity and the absolute humidity.
In order to more intuitively compare the actual application effect of the non-deviation bright temperature inversion method provided by the invention, the comparison between the time-reversal profile at 31/00 UTC in 2021 and the actual detection data of sounding is selected, specifically shown in fig. 7-9, wherein the time-reversal profile is realized as sounding data, the dotted line is the non-deviation bright temperature inversion method provided by the invention, and the dotted line superposed triangle is the inversion result of the RPG self-contained inversion method.
FIG. 7 is a comparison of temperature profiles, at 2-6km altitude, the unbiased bright temperature inversion method is closer to sounding observations than the RPG self-contained algorithm inverted profile. Fig. 8 is a comparison of relative humidity profiles, and fig. 9 is a comparison of absolute humidity profiles, and compared with an inversion profile of an RPG self-contained inversion algorithm, the non-deviation bright temperature inversion humidity profile is more consistent with actual sounding observation, which illustrates that the non-deviation bright temperature inversion display provided by the invention improves the actual application effect of foundation microwave radiometer inversion.
Fig. 10, 11 and 12 are daily time sequence diagrams of a temperature profile, a relative humidity profile and an absolute humidity profile inverted based on the unbiased bright temperature data inversion model, respectively, and it can be seen that the inversion model of the present invention can better invert the daily variation characteristics of temperature and humidity, and display the dynamic and thermal evolution characteristics of daily weather.
Claims (4)
1. A foundation microwave radiometer atmospheric temperature and humidity profile inversion method based on non-deviation bright temperature is characterized by comprising the following steps:
step one, data selection: selecting nearest point sounding data and ERA5 of a grid point where the longitude and latitude are located according to the longitude and latitude where the foundation microwave radiometer is located, analyzing the data, and removing precipitation data;
step two, data processing: based on a MonoRTM radiation transmission mode, temperature and humidity information in sounding data and liquid water information of fusion ERA5 reanalysis data are utilized, and simulated brightness temperature of fusion liquid water information of each channel of the foundation microwave radiometer is calculated; comparing the simulated brightness temperature with the brightness temperature observed by the foundation microwave radiometer, and eliminating abnormal data by using the Lauda criterion to obtain the brightness temperature of the foundation microwave radiometer subjected to quality control, namely the non-deviation brightness temperature; a plurality of groups of light temperatures with no deviation at equal time intervals within half an hour before and after the sounding observation are taken, and training sample data are formed together with the temperature and humidity information of sounding data;
training a temperature and humidity profile inversion algorithm by using a neural network algorithm to construct an inversion model; the method comprises the following steps: a single hidden layer BP neural network of a three-layer network is used, namely an input layer, a hidden layer and an output layer are all fully connected layers; the node number of the input layer is the number of channels of the foundation microwave radiometer, the input data is the simulated bright temperature data of each channel, the node number of the output layer is the number of vertical layers of the atmospheric parameter profile, and the output data is the atmospheric temperature, relative humidity and absolute humidity data of each height layer; 75% of data in the training sample data is used for training, 15% of data is used for verification, and 10% of data is used for testing the training precision of the inversion model; optimizing and adjusting the node number of the hidden layer according to the training precision of the model;
and step four, performing precision analysis and application of the atmospheric temperature and humidity profile inversion model, including statistical analysis and case analysis.
2. The method for inverting the atmospheric temperature and humidity profile of the foundation microwave radiometer based on the non-deviation bright temperature as claimed in claim 1, wherein in the second step, the simulated bright temperature of the fused liquid water information of each channel of the foundation microwave radiometer is calculated by using the temperature and humidity information of the sounding data and the liquid water information of the fusion ERA5 reanalysis data, specifically:
interpolating liquid water information of the reanalysis data of the detected air temperature and humidity information and ERA5 to the same air pressure layer;
generating a TAPE5 file by using the air pressure, height and temperature and humidity information of the sounding data;
in a MonoRTM radiation transmission model, modifying channel information in an input file according to a TAPE5 file;
performing first simulation by using case3 user-defined downlink radiation;
compiling to obtain corresponding simulated brightness temperature and generating a TAPE7 file;
adding ERA5 into TAPE7 file generated by case3 and analyzing liquid water information of the data;
modifying the channel information in the input file according to the TAPE7 file;
a second simulation was performed using case 5;
generating simulated brightness temperature of the sounding data fused with the liquid water information.
3. The method for inverting the atmospheric temperature and humidity profile of the ground-based microwave radiometer based on the unbiased bright temperature as claimed in claim 1, wherein the test inversion model training accuracy of step three is quantitatively evaluated by calculating a Root Mean Square Error (RMSE), and the expression is as follows:
in the formula, xiFor the inverted atmospheric temperature and humidity profile, xobs，iTo probe the null profile value, n represents the number of test samples.
4. The method for inverting the atmospheric temperature and humidity profile of the ground-based microwave radiometer based on the unbiased bright temperature as recited in claim 1, wherein the statistical analysis of the precision analysis in the fourth step is a quantitative evaluation by calculating a deviation (Bias) and a Root Mean Square Error (RMSE), and the expression is as follows:
in the formula, xiFor the inverted atmospheric temperature and humidity profile, xobs，iTo probe the null profile value, n represents the number of test samples.
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