CN111737913A - MWHTS clear sky observation bright temperature selection method based on cloud water content inversion - Google Patents
MWHTS clear sky observation bright temperature selection method based on cloud water content inversion Download PDFInfo
- Publication number
- CN111737913A CN111737913A CN202010544507.9A CN202010544507A CN111737913A CN 111737913 A CN111737913 A CN 111737913A CN 202010544507 A CN202010544507 A CN 202010544507A CN 111737913 A CN111737913 A CN 111737913A
- Authority
- CN
- China
- Prior art keywords
- mwhts
- clear sky
- water content
- bright temperature
- data set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
A method for selecting MWHTS bright temperature in clear sky observation based on cloud water content inversion comprises the steps of establishing matching data of the MWHTS bright temperature in time and space and a climatology data set, calculating corresponding MWHTS simulated bright temperature, and forming an analysis data set and a verification data set; taking the cloud water content in the analysis data set as 0 as a strict clear sky threshold, selecting MWHTS to observe the bright temperature in strict clear sky, and adjusting the strict clear sky threshold by taking the calculation accuracy of the MWHTS simulated bright temperature in the strict clear sky as a standard to form a corresponding clear sky threshold; establishing an optimal inversion model for MWHTS to observe the water content of the bright temperature inversion cloud based on the analysis data set and the BP neural network; and (3) inverting the cloud water content by using the MWHTS observation bright temperature in the verification data set, and selecting the MWHTS bright temperature observation in clear sky according to the cloud water content inversion value and the clear sky threshold value. The method can directly utilize the MWHTS to observe the bright temperature to realize the effective selection of the MWHTS bright temperature in clear sky, and has high accuracy and simple and easy operation.
Description
Technical Field
The invention belongs to the technical field of microwave remote sensing, and particularly relates to an MWHTS clear sky observation bright temperature selection method based on cloud water content inversion.
Background
The microwave wet temperature detector (MWHTS) is an important load on a wind cloud number three C star D star, is the international first microwave radiometer integrating a hygrometer and a thermometer, and has eight temperature detection channels (channels 2-9), five humidity detection channels (channels 11-15) and two window area channels (channel 1 and channel 10), so that the simultaneous detection of atmospheric temperature, water vapor parameters and surface parameters can be realized.
Compared with the clear air atmosphere, the radiation transmission of the microwaves in the cloud and rain atmosphere is influenced by the emission and scattering effects generated by cloud and rain, so that the nonlinearity of a radiation transmission equation is increased, and the calculation accuracy of a radiation transmission model is further reduced. At present, the simulation calculation precision of the satellite-borne microwave radiometer for observing the bright temperature in clear air atmosphere is far higher than that in cloud and rain atmosphere, and the calculation precision of the satellite-borne microwave radiometer for simulating the bright temperature has important influence on the data application of the microwave radiometer. The clear sky observation brightness temperature is extracted from the satellite-borne microwave radiometer observation brightness temperature, the inversion accuracy of the inversion atmospheric parameters is directly determined, and the method is closely related to the assimilation application effect of the satellite-borne radiometer observation brightness temperature. In addition, the bright temperature observed in clear sky has important theoretical guiding significance for the evaluation of the channel detection capability of the satellite-borne microwave radiometer and the hardware development.
When the forward problem of the satellite-borne microwave radiometer is processed, the setting of the atmospheric relative humidity threshold value and the utilization of the infrared cloud product are common methods for selecting clear sky observation bright temperature from clear sky observation bright temperature of the satellite-borne microwave radiometer, but before clear sky data is selected by utilizing the two methods, the satellite-borne microwave radiometer observation data needs to be matched with the atmospheric relative humidity or the infrared cloud product, so that the direct application of the data is seriously influenced, and therefore the two methods are not applicable to the inversion of the microwave radiometer observation data. In the inversion application of the brightness temperature observed by the satellite-borne microwave radiometer, a threshold value can be formed according to the statistical relationship between the brightness temperatures observed by the microwave radiometer channels to judge clear sky, but the accuracy of the judgment of clear sky data by the method is low, and in addition, the dependence of the establishment of the threshold value on the local atmospheric state is strong in the method, and the method is not suitable for selecting the brightness temperature observed in clear sky in a large range.
Disclosure of Invention
In order to solve the technical problems, according to the setting characteristics of a radiometer channel, MWHTS has high sensitivity to water vapor parameters, a BP neural network can be directly used for inverting the atmospheric cloud water content, the cloud water content is directly related to judgment of the atmospheric clear sky, and then MWHTS clear sky bright temperature data can be extracted according to the cloud water content inversion value.
In order to realize the technical purpose, the adopted technical scheme is as follows: an MWHTS clear sky observation bright temperature selection method based on cloud water content inversion comprises the following steps:
the method comprises the following steps: establishing matching data of the MWHTS observation brightness temperature and the climatology data set in time and space, inputting atmospheric parameters in the matching data into a radiation transmission model CRTM, calculating MWHTS simulation brightness temperature, establishing a matching data set, and forming an analysis data set and a verification data set;
step two: taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as the strict clear sky data in an analysis data set, calculating the calculation accuracy of the MWHTS simulated bright temperature, and establishing a corresponding clear sky threshold by increasing the strict clear sky threshold in the analysis data set by taking the calculation accuracy of the MWHTS simulated bright temperature in the strict clear sky data as reference;
step three: the MWHTS observed bright temperature in the analysis data set is used as input, the cloud water content in the analysis data set is used as output, the BP neural network is trained, and an optimal inversion model for the MWHTS observed bright temperature to invert the cloud water content is established by adjusting the number of neurons of a hidden layer of the BP neural network and taking the mean square error of a predicted value of the cloud water content as a minimum standard;
step four: and (4) inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for MWHTS observed bright temperature inversion cloud water content to obtain an inversion value of the cloud water content, and selecting the MWHTS clear air observed bright temperature according to the clear air threshold established in the step two.
The first step specifically comprises the following steps: first, the atmospheric parameters using the climatology data set were: matching the atmospheric parameters with MWHTS observed bright temperature according to a matching rule that longitude and latitude errors are less than 0.1 degree and time errors are less than 10 minutes by using a temperature profile, a humidity profile, a cloud water profile, a 2m temperature, a 2m humidity, surface pressure and a 10m wind speed to obtain matching data; then, the atmospheric parameters in the matched data are used as input parameters of a radiation transmission model CRTM, the simulated brightness temperature of the MWHTS is calculated, and a matched data set of the observed brightness temperature of the MWHTS, the simulated brightness temperature of the MWHTS and the atmospheric parameters is established; finally, 80% of the matched data in the matched data set is randomly selected to form an analysis data set, and the remaining 20% of the matched data forms a verification data set.
The second step specifically comprises: firstly, taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as strict clear sky data in an analysis data set formed in the step one, calculating a root mean square error between MWHTS observed bright temperature and MWHTS simulated bright temperature in the strict clear sky data, and recording the calculation precision of the MWHTS simulated bright temperature as the strict clear sky data as the strict clear sky precision; then, increasing a strict clear sky threshold by a step length of 0.01mm to serve as an adjustment clear sky threshold, selecting matching data smaller than the adjustment clear sky threshold in the analysis data set to serve as adjustment clear sky data, calculating a root mean square error between an MWHTS observed bright temperature and an MWHTS simulated bright temperature in the adjustment clear sky data, and recording the calculation accuracy as the adjustment clear sky accuracy, wherein the root mean square error is the calculation accuracy of the MWHTS simulated bright temperature of the adjustment clear sky data; and finally, calculating the difference value between the adjusted clear sky precision and the strict clear sky precision in the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15, and if the difference value of any one of the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15 is increased to 1K, adjusting the clear sky threshold to be the clear sky threshold.
The third step specifically comprises: firstly, establishing a training sample of a BP neural network by taking MWHTS observed bright temperature in an analysis data set as input and cloud water content matched with the MWHTS observed bright temperature in the analysis data set as output; then, selecting a BP neural network comprising an input layer, an output layer and a hidden layer, increasing the number of neurons of the hidden layer from 5 to 50 according to the step length 1, and training the BP neural network by using a training sample to obtain 46 BP neural network models; finally, the mean square deviations of the 46 BP neural network models on the cloud water content predicted value in the training process are compared, and the BP neural network model corresponding to the minimum mean square deviation value is selected as the optimal inversion model for MWHTS observation bright temperature inversion cloud water content.
The fourth step specifically comprises: firstly, inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for inverting the cloud water content by the MWHTS observed bright temperature established in the third step to obtain an inversion value of the cloud water content; then, comparing the clear sky threshold established in the step two with the cloud water content inversion value, and selecting data with the cloud water content inversion value smaller than the clear sky threshold as the clear sky cloud water content inversion value; and finally, acquiring MWHTS clear sky bright temperature data according to the one-to-one correspondence relationship between the clear sky cloud water content inversion value and the MWHTS observed bright temperature.
The invention has the beneficial effects that: according to the method, a strict clear sky threshold is set according to the cloud water content value of 0, the calculation accuracy of the MWHTS simulated bright temperature under the strict clear sky condition is taken as a standard, and the strict clear sky threshold is adjusted to establish the clear sky threshold; and directly inverting the cloud water content by using the MWHTS to observe the bright temperature, comparing the cloud water content inversion value with a clear sky threshold value, and selecting the MWHTS observed bright temperature corresponding to the cloud water content inversion value smaller than the clear sky threshold value, namely the MWHTS observed bright temperature in clear sky. The MWHTS selected by the method has higher accuracy in clear sky observation of bright temperature, and the operation is simple and easy to implement.
Drawings
FIG. 1 is a flow chart of a method for selecting a bright temperature for MWHTS clear sky observation based on cloud water content inversion according to the present invention;
FIG. 2 is a comparison graph of the calculation accuracy of the MWHTS simulated light temperature under all-weather conditions with strict clear sky data in example 1;
fig. 3 is a difference diagram between the fine adjustment clear sky precision and the strict fine sky precision when the fine adjustment clear sky threshold is 0.11mm in embodiment 1.
Detailed Description
An MWHTS clear sky observation bright temperature selection method based on cloud water content inversion is characterized by comprising the following steps:
the method comprises the following steps: establishing matching data of the MWHTS observation brightness temperature and the climatology data set in time and space, inputting atmospheric parameters in the matching data into a radiation transmission model CRTM, calculating MWHTS simulation brightness temperature, establishing a matching data set, and forming an analysis data set and a verification data set;
the first step specifically comprises the following steps:
first, the atmospheric parameters using the climatology data set were: matching the atmospheric parameters with MWHTS observed bright temperature according to a matching rule that longitude and latitude errors are less than 0.1 degree and time errors are less than 10 minutes by using a temperature profile, a humidity profile, a cloud water profile, a 2m temperature, a 2m humidity, surface pressure and a 10m wind speed to obtain matching data; then, the atmospheric parameters in the matched data are used as input parameters of a radiation transmission model CRTM, the simulated brightness temperature of the MWHTS is calculated, and a matched data set of the observed brightness temperature of the MWHTS, the simulated brightness temperature of the MWHTS and the atmospheric parameters is established; finally, 80% of the matched data in the matched data set is randomly selected to form an analysis data set, and the remaining 20% of the matched data forms a verification data set.
Step two: taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as the strict clear sky data in an analysis data set, calculating the calculation accuracy of the MWHTS simulated bright temperature, and establishing a corresponding clear sky threshold by increasing the strict clear sky threshold in the analysis data set by taking the calculation accuracy of the MWHTS simulated bright temperature in the strict clear sky data as reference;
the second step specifically comprises: firstly, taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as strict clear sky data in an analysis data set formed in the step one, calculating a root mean square error between MWHTS observed bright temperature and MWHTS simulated bright temperature in the strict clear sky data, and recording the calculation precision of the MWHTS simulated bright temperature as the strict clear sky data as the strict clear sky precision; then, increasing a strict clear sky threshold by a step length of 0.01mm to serve as an adjustment clear sky threshold, selecting matching data smaller than the adjustment clear sky threshold in the analysis data set to serve as adjustment clear sky data, calculating a root mean square error between an MWHTS observed bright temperature and an MWHTS simulated bright temperature in the adjustment clear sky data, and recording the calculation accuracy as the adjustment clear sky accuracy, wherein the root mean square error is the calculation accuracy of the MWHTS simulated bright temperature of the adjustment clear sky data; and finally, calculating the difference value between the adjusted clear sky precision and the strict clear sky precision in the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15, and if the difference value of any one of the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15 is increased to 1K, adjusting the clear sky threshold to be the clear sky threshold.
Step three: the MWHTS observed bright temperature in the analysis data set is used as input, the cloud water content in the analysis data set is used as output, the BP neural network is trained, and an optimal inversion model for the MWHTS observed bright temperature to invert the cloud water content is established by adjusting the number of neurons of a hidden layer of the BP neural network and taking the mean square error of a predicted value of the cloud water content as a minimum standard;
the third step specifically comprises: firstly, establishing a training sample of a BP neural network by taking MWHTS observed bright temperature in an analysis data set as input and cloud water content matched with the MWHTS observed bright temperature in the analysis data set as output; then, selecting a BP neural network comprising an input layer, an output layer and a hidden layer, increasing the number of neurons of the hidden layer from 5 to 50 according to the step length 1, and training the BP neural network by using a training sample to obtain 46 BP neural network models; finally, the mean square deviations of the 46 BP neural network models on the cloud water content predicted value in the training process are compared, and the BP neural network model corresponding to the minimum mean square deviation value is selected as the optimal inversion model for MWHTS observation bright temperature inversion cloud water content.
Step four: and (4) inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for MWHTS observed bright temperature inversion cloud water content to obtain an inversion value of the cloud water content, and selecting the MWHTS clear air observed bright temperature according to the clear air threshold established in the step two.
The fourth step specifically comprises: firstly, inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for inverting the cloud water content by the MWHTS observed bright temperature established in the third step to obtain an inversion value of the cloud water content; then, comparing the clear sky threshold established in the step two with the cloud water content inversion value, and selecting data with the cloud water content inversion value smaller than the clear sky threshold as the clear sky cloud water content inversion value; and finally, acquiring MWHTS clear sky bright temperature data according to the one-to-one correspondence relationship between the clear sky cloud water content inversion value and the MWHTS observed bright temperature.
The present invention is further described with reference to the following examples and the accompanying drawings, which are not intended to limit the scope of the invention as claimed.
Example 1
The climatology data set selects a reanalysis data set ERA-Interim of a European middle-term weather forecast center (ECMWF), selects a wind cloud No. three D star MWHTS observation brightness temperature to perform clear sky observation brightness temperature selection, wherein the time range is from 9 months in 2018 to 8 months in 2019, and the geographic range is (25-45 degrees N, 160-220 degrees E). Matching the MWHTS observed brightness temperature with the temperature profile, the humidity profile, the cloud water profile, the 2m temperature, the 2m humidity, the ground pressure and the 10m wind speed in the ERA Interim data set according to a matching rule that the time error is less than 10 minutes and the longitude and latitude error is less than 0.1 degrees, simultaneously inputting the matched atmospheric parameters in the ERA Interim data set into a radiation transmission model CRTM, calculating the MWHTS simulated brightness temperature to obtain a matching data set (1060162 group) of the MWHTS observed brightness temperature, the MWHTS simulated brightness temperature and the atmospheric parameters, randomly selecting 80% of matching data in the matching data set to establish an analysis data set (848129 group), and establishing a verification data set (212033 group) by the remaining 20% of matching data.
The cloud water content is 0 as a strict clear sky threshold, 5830 groups of matched data can be obtained by selecting strict clear sky data in an analysis data set, and the root mean square error between the MWHTS observed bright temperature and the MWHTS simulated bright temperature can be calculated in the strict clear sky data, so that the strict clear sky precision can be obtained; all MWHTS observed bright temperatures in the data set and MWHTS simulation bright temperature direct root mean square errors are calculated and analyzed, and all-weather precision can be obtained. The calculation accuracy of the MWHTS simulated light temperature is shown in FIG. 2 under strict clear sky conditions and all weather conditions, namely, the strict clear sky accuracy and all weather accuracy. As can be seen from fig. 2, the strict clear sky precision is higher than the all-weather precision in fifteen channels of MWHTS.
Gradually increasing the strict clear sky threshold by the step length of 0.01mm to form an adjusted clear sky threshold, selecting matching data smaller than the adjusted clear sky threshold from the cloud water content in the analysis data, calculating the root mean square error between the MWHTS observed bright temperature and the MWHTS simulated bright temperature, and recording as the adjusted clear sky precision; when the difference value between the fine air adjustment precision and the strict fine air precision of any one of the MWHTS temperature detection channels (channels 2-9) and the water vapor detection channels (channels 11-15) is increased to 1K, the fine air adjustment threshold at the moment is the fine air threshold. When the clear sky threshold is adjusted to be 0.11mm, the difference between the clear sky adjustment precision and the strict clear sky precision is shown in fig. 3. Where the difference in accuracy in the channel 8 is 1K, thus establishing a clear sky threshold of 0.11 mm. Based on the clear sky threshold, 306001 sets of clear sky data may be selected in the analysis dataset.
A BP neural network is used as an algorithm model for inverting the cloud water content by using MWHTS, wherein the BP neural network is structurally arranged into 3 layers (an input layer, an output layer and a hidden layer), 848129 groups of MWHTS observation brightness temperatures in an analysis data set are used as the input of the algorithm model, the corresponding cloud water content is used as the output of the algorithm model, the number of neurons in the hidden layer is changed from 5 to 50, and the neural network is trained respectively. Comparing the obtained 46 trained neural network models, and when the number of neurons in the hidden layer is 43, obtaining the minimum mean square deviation value of the predicted value of the cloud water content, which is 0.0091, and then recording the neural network model when the number of neurons in the hidden layer is 43 as the optimal inversion model for MWHTS observation of bright temperature inversion cloud water content.
And inputting the observed bright temperature of the 212033 groups of MWHTS in the verification data set into an optimal inversion model for observing the bright temperature inversion cloud water content of the MWHTS to obtain the cloud water content inversion values of the 212033 groups. And (4) taking the cloud water content in the verification data set as a true value, and obtaining the average deviation between the true value of the cloud water content and the inversion value of the cloud water content to be 0.0022 mm. In the cloud water content inversion value, comparing a clear sky threshold value of 0.11mm with the cloud water content inversion value, selecting an MWHTS observation bright temperature group corresponding to the cloud water content inversion value smaller than the clear sky threshold value as 76332 groups, and recording as MWHTS clear sky observation bright temperature selection data; in the cloud water content real values in the verification analysis data set, a clear sky threshold value of 0.11mm is compared with the cloud water content real values, and an MWHTS observation bright temperature corresponding to the cloud water content real value smaller than the clear sky threshold value is selected to be 79263 groups and recorded as MWHTS clear sky observation bright temperature real data. The same data of the MWHTS clear sky observation bright temperature selection data and the MWHTS clear sky observation bright temperature real data is 73590 groups, in other words, 73590 groups of data in the MWHTS clear sky observation bright temperature real data 79263 group can be selected by the method for selecting the MWHTS clear sky bright temperature data based on cloud water content inversion, namely 92.84% of the MWHTS clear sky observation bright temperature in the MWHTS observation bright temperature can be selected by the method.
Claims (5)
1. An MWHTS clear sky observation bright temperature selection method based on cloud water content inversion is characterized by comprising the following steps:
the method comprises the following steps: establishing matching data of the MWHTS observation brightness temperature and the climatology data set in time and space, inputting atmospheric parameters in the matching data into a radiation transmission model CRTM, calculating MWHTS simulation brightness temperature, establishing a matching data set, and forming an analysis data set and a verification data set;
step two: taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as the strict clear sky data in an analysis data set, calculating the calculation accuracy of the MWHTS simulated bright temperature, and establishing a corresponding clear sky threshold by increasing the strict clear sky threshold in the analysis data set by taking the calculation accuracy of the MWHTS simulated bright temperature in the strict clear sky data as reference;
step three: the MWHTS observed bright temperature in the analysis data set is used as input, the cloud water content in the analysis data set is used as output, the BP neural network is trained, and an optimal inversion model for the MWHTS observed bright temperature to invert the cloud water content is established by adjusting the number of neurons of a hidden layer of the BP neural network and taking the mean square error of a predicted value of the cloud water content as a minimum standard;
step four: and (4) inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for MWHTS observed bright temperature inversion cloud water content to obtain an inversion value of the cloud water content, and selecting the MWHTS clear air observed bright temperature according to the clear air threshold established in the step two.
2. The method according to claim 1, wherein the step one specifically comprises:
first, the atmospheric parameters using the climatology data set were: matching the atmospheric parameters with MWHTS observed bright temperature according to a matching rule that longitude and latitude errors are less than 0.1 degree and time errors are less than 10 minutes by using a temperature profile, a humidity profile, a cloud water profile, a 2m temperature, a 2m humidity, surface pressure and a 10m wind speed to obtain matching data; then, the atmospheric parameters in the matched data are used as input parameters of a radiation transmission model CRTM, the simulated brightness temperature of the MWHTS is calculated, and a matched data set of the observed brightness temperature of the MWHTS, the simulated brightness temperature of the MWHTS and the atmospheric parameters is established; finally, 80% of the matched data in the matched data set is randomly selected to form an analysis data set, and the remaining 20% of the matched data forms a verification data set.
3. The method according to claim 1, wherein the second step specifically comprises:
firstly, taking the cloud water content of 0 as a strict clear sky threshold, selecting matched data with the cloud water content of 0 as strict clear sky data in an analysis data set formed in the step one, calculating a root mean square error between MWHTS observed bright temperature and MWHTS simulated bright temperature in the strict clear sky data, and recording the calculation precision of the MWHTS simulated bright temperature as the strict clear sky data as the strict clear sky precision; then, increasing a strict clear sky threshold by a step length of 0.01mm to serve as an adjustment clear sky threshold, selecting matching data smaller than the adjustment clear sky threshold in the analysis data set to serve as adjustment clear sky data, calculating a root mean square error between an MWHTS observed bright temperature and an MWHTS simulated bright temperature in the adjustment clear sky data, and recording the calculation accuracy as the adjustment clear sky accuracy, wherein the root mean square error is the calculation accuracy of the MWHTS simulated bright temperature of the adjustment clear sky data; and finally, calculating the difference value between the adjusted clear sky precision and the strict clear sky precision in the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15, and if the difference value of any one of the MWHTS temperature detection channels 2-9 and the water vapor detection channels 11-15 is increased to 1K, adjusting the clear sky threshold to be the clear sky threshold.
4. The method according to claim 1, wherein the step three specifically comprises:
firstly, establishing a training sample of a BP neural network by taking MWHTS observed bright temperature in an analysis data set as input and cloud water content matched with the MWHTS observed bright temperature in the analysis data set as output; then, selecting a BP neural network comprising an input layer, an output layer and a hidden layer, increasing the number of neurons of the hidden layer from 5 to 50 according to the step length 1, and training the BP neural network by using a training sample to obtain 46 BP neural network models; finally, the mean square deviations of the 46 BP neural network models on the cloud water content predicted value in the training process are compared, and the BP neural network model corresponding to the minimum mean square deviation value is selected as the optimal inversion model for MWHTS observation bright temperature inversion cloud water content.
5. The method for selecting the bright temperature of the MWHTS clear sky observation based on cloud water content inversion as claimed in claim 1, wherein the fourth step specifically comprises:
firstly, inputting the MWHTS observed bright temperature in the verification data set into an optimal inversion model for inverting the cloud water content by the MWHTS observed bright temperature established in the third step to obtain an inversion value of the cloud water content; then, comparing the clear sky threshold established in the step two with the cloud water content inversion value, and selecting data with the cloud water content inversion value smaller than the clear sky threshold as the clear sky cloud water content inversion value; and finally, acquiring MWHTS clear sky bright temperature data according to the one-to-one correspondence relationship between the clear sky cloud water content inversion value and the MWHTS observed bright temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010544507.9A CN111737913B (en) | 2020-06-15 | 2020-06-15 | MWHTS clear sky observation bright temperature selection method based on cloud water content inversion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010544507.9A CN111737913B (en) | 2020-06-15 | 2020-06-15 | MWHTS clear sky observation bright temperature selection method based on cloud water content inversion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111737913A true CN111737913A (en) | 2020-10-02 |
| CN111737913B CN111737913B (en) | 2023-08-15 |
Family
ID=72649242
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010544507.9A Active CN111737913B (en) | 2020-06-15 | 2020-06-15 | MWHTS clear sky observation bright temperature selection method based on cloud water content inversion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111737913B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112329334A (en) * | 2020-10-15 | 2021-02-05 | 洛阳师范学院 | MWHTS and MWTS-II fusion inversion sea surface air pressure method based on simulated brightness temperature |
| CN112345151A (en) * | 2020-10-15 | 2021-02-09 | 洛阳师范学院 | Sensitivity test method of MWTS-II to sea surface air pressure based on natural atmosphere |
| CN112730313A (en) * | 2020-12-21 | 2021-04-30 | 国家卫星气象中心(国家空间天气监测预警中心) | Multi-frequency terahertz detector channel selection method and device for ice cloud detection |
| CN113311510A (en) * | 2021-05-11 | 2021-08-27 | 洛阳师范学院 | MWHTS bright temperature observation classification method based on simulated bright temperature |
| CN113340836A (en) * | 2021-05-18 | 2021-09-03 | 国家卫星气象中心(国家空间天气监测预警中心) | Atmospheric temperature and humidity profile inversion method for high-latitude complex underlying surface |
| CN113822345A (en) * | 2021-09-09 | 2021-12-21 | 南京中科逆熵科技有限公司 | Method and system for inverting cloud water content by fusing MWHTS and MWTS-II |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010032383A (en) * | 2008-07-29 | 2010-02-12 | Fujitsu Ltd | Apparatus and method for forecasting, and program |
| CN103901420A (en) * | 2014-04-18 | 2014-07-02 | 山东科技大学 | Method for dynamic threshold method remote sensing data cloud identification supported by prior surface reflectance |
| CN108051872A (en) * | 2017-12-13 | 2018-05-18 | 湖北省气象服务中心（湖北省专业气象服务台） | Method and apparatus based on steam phase transition process in Ground-Based Microwave Radiometer Retrieval of Cloud |
| CN108508442A (en) * | 2018-03-16 | 2018-09-07 | 哈尔滨工程大学 | A kind of Atmosphere and humidity profiles inversion method based on ground multi-channel microwave radiometer |
| US20190057171A1 (en) * | 2017-02-10 | 2019-02-21 | Guangxi Institute of Meteorlogical Disaster- Reducing Research | Novel nonlinear method for area-wide near surface air temperature precision retrieval |
| CN109782374A (en) * | 2019-01-15 | 2019-05-21 | 北京华云星地通科技有限公司 | Moisture content by assimilating inverting optimizes the method and device of numerical weather forecast |
| CN110969609A (en) * | 2019-11-29 | 2020-04-07 | 华中科技大学 | Microwave remote sensing bright temperature image cloud detection method |
| CN111060992A (en) * | 2019-12-27 | 2020-04-24 | 国家卫星气象中心(国家空间天气监测预警中心) | Equal-weight conjugate precipitation detection method and system for satellite-borne microwave dioxygen detection channel |
-
2020
- 2020-06-15 CN CN202010544507.9A patent/CN111737913B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010032383A (en) * | 2008-07-29 | 2010-02-12 | Fujitsu Ltd | Apparatus and method for forecasting, and program |
| CN103901420A (en) * | 2014-04-18 | 2014-07-02 | 山东科技大学 | Method for dynamic threshold method remote sensing data cloud identification supported by prior surface reflectance |
| US20190057171A1 (en) * | 2017-02-10 | 2019-02-21 | Guangxi Institute of Meteorlogical Disaster- Reducing Research | Novel nonlinear method for area-wide near surface air temperature precision retrieval |
| CN108051872A (en) * | 2017-12-13 | 2018-05-18 | 湖北省气象服务中心（湖北省专业气象服务台） | Method and apparatus based on steam phase transition process in Ground-Based Microwave Radiometer Retrieval of Cloud |
| CN108508442A (en) * | 2018-03-16 | 2018-09-07 | 哈尔滨工程大学 | A kind of Atmosphere and humidity profiles inversion method based on ground multi-channel microwave radiometer |
| CN109782374A (en) * | 2019-01-15 | 2019-05-21 | 北京华云星地通科技有限公司 | Moisture content by assimilating inverting optimizes the method and device of numerical weather forecast |
| CN110969609A (en) * | 2019-11-29 | 2020-04-07 | 华中科技大学 | Microwave remote sensing bright temperature image cloud detection method |
| CN111060992A (en) * | 2019-12-27 | 2020-04-24 | 国家卫星气象中心(国家空间天气监测预警中心) | Equal-weight conjugate precipitation detection method and system for satellite-borne microwave dioxygen detection channel |
Non-Patent Citations (2)
| Title |
|---|
| 张子瑾: "基于FY-3C/MWHTS观测资料反演中低纬度海面气压", 空间科学学报, vol. 40, no. 3, pages 364 - 375 * |
| 贺秋瑞;王振占;何杰颖;: "FY-3C/MWHTS资料反演陆地晴空大气温湿廓线", 遥感学报, no. 01, pages 27 - 38 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112329334A (en) * | 2020-10-15 | 2021-02-05 | 洛阳师范学院 | MWHTS and MWTS-II fusion inversion sea surface air pressure method based on simulated brightness temperature |
| CN112345151A (en) * | 2020-10-15 | 2021-02-09 | 洛阳师范学院 | Sensitivity test method of MWTS-II to sea surface air pressure based on natural atmosphere |
| CN112345151B (en) * | 2020-10-15 | 2022-01-07 | 洛阳师范学院 | Sensitivity test method of MWTS-II to sea surface air pressure based on natural atmosphere |
| CN112329334B (en) * | 2020-10-15 | 2024-01-23 | 洛阳师范学院 | MWHTS and MWTS-II fusion inversion sea surface air pressure method based on simulated bright temperature |
| CN112730313A (en) * | 2020-12-21 | 2021-04-30 | 国家卫星气象中心(国家空间天气监测预警中心) | Multi-frequency terahertz detector channel selection method and device for ice cloud detection |
| CN113311510A (en) * | 2021-05-11 | 2021-08-27 | 洛阳师范学院 | MWHTS bright temperature observation classification method based on simulated bright temperature |
| CN113340836A (en) * | 2021-05-18 | 2021-09-03 | 国家卫星气象中心(国家空间天气监测预警中心) | Atmospheric temperature and humidity profile inversion method for high-latitude complex underlying surface |
| CN113822345A (en) * | 2021-09-09 | 2021-12-21 | 南京中科逆熵科技有限公司 | Method and system for inverting cloud water content by fusing MWHTS and MWTS-II |
| CN113822345B (en) * | 2021-09-09 | 2024-03-29 | 南京中科逆熵科技有限公司 | Method and system for inverting cloud water content by fusing MWHTS and MWTS-II |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111737913B (en) | 2023-08-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111737913B (en) | MWHTS clear sky observation bright temperature selection method based on cloud water content inversion | |
| CN111737912B (en) | MWHTS (metal wrap through) simulated bright temperature calculation method based on deep neural network | |
| CN108875254B (en) | One-dimensional variation inversion method of atmospheric temperature wet profile | |
| CN101634711B (en) | Method for estimating temperature of near-surface air from MODIS data | |
| CN111060992B (en) | Equal-weight conjugate precipitation detection method and system for satellite-borne microwave dioxygen detection channel | |
| CN110826693B (en) | Three-dimensional atmospheric temperature profile inversion method and system based on DenseNet convolutional neural network | |
| CN110749942B (en) | Clear sky channel detection quality control method suitable for data assimilation of stationary satellite | |
| CN108020322B (en) | Airborne thermal infrared hyperspectral remote sensing quantitative detection method for coal field fire area | |
| CN111707376B (en) | Surface temperature inversion method for broadband thermal infrared sensor | |
| CN111257241A (en) | Atmospheric carbon dioxide concentration inversion algorithm based on DEEI (DeEI) | |
| CN113108918B (en) | Method for inverting air temperature by using thermal infrared remote sensing data of polar-orbit meteorological satellite | |
| CN111737641B (en) | MWHTS channel weight function calculation method based on neural network | |
| CN113552080B (en) | Real-time inversion algorithm for ultra-spectrum remote sensing earth surface atmosphere relative humidity | |
| CN111400658B (en) | Channel selection method for inversion gas profile of hyperspectral thermal infrared data | |
| CN112329334A (en) | MWHTS and MWTS-II fusion inversion sea surface air pressure method based on simulated brightness temperature | |
| CN113311509B (en) | Sensitivity test method of MWHTS (metal wrap through) to sea surface air pressure based on neural network | |
| CN114755189B (en) | Feature-optimized self-attention-based hyperspectral satellite LAI inversion method | |
| CN113108949B (en) | Model fusion-based sonde temperature sensor error prediction method | |
| CN112254866B (en) | Method for inverting sea surface air pressure by fusion of MWTS-II and MWHTS | |
| CN113311510B (en) | MWHTS (metal wrap through) observation bright temperature classification method based on simulated bright temperature | |
| CN114371519B (en) | Foundation microwave radiometer atmospheric temperature and humidity profile inversion method based on unbiased bright temperature | |
| CN111274535B (en) | Dynamic evaluation method for spatial representativeness of ground station | |
| Duan et al. | Estimating hourly solar near-infrared irradiance using meteorological data for sustainable building design and engineering | |
| CN113822345A (en) | Method and system for inverting cloud water content by fusing MWHTS and MWTS-II | |
| CN117077530A (en) | Atmospheric radiation transmission simulation method integrating deep learning algorithm, readable storage medium and computing device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |