CN112965144B - Method for improving accuracy of inversion of atmospheric temperature and humidity profile by one-dimensional variational algorithm - Google Patents
Method for improving accuracy of inversion of atmospheric temperature and humidity profile by one-dimensional variational algorithm Download PDFInfo
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Abstract
A method for improving accuracy of inversion of atmospheric temperature and humidity profile by one-dimensional variational algorithm includes selecting atmospheric data with long time span, establishing global representative data containing a large amount of data, carrying out sea-land classification on the global representative data according to earth surface difference, further classifying the global representative data according to latitude zones by considering difference of atmospheric features of different latitude zones, and generating different background covariance matrixes aiming at different latitude zones. And calling a corresponding background covariance matrix for inversion calculation by the one-dimensional variational algorithm according to the input sea-land classification and the geographical position of the observed brightness and temperature. The method can ensure that the one-dimensional variational algorithm has higher inversion accuracy when inverting the atmospheric parameters, and is simple and easy to operate.
Description
Technical Field
The invention relates to the technical field of microwave remote sensing, in particular to a method for improving the accuracy of inversion of atmospheric temperature and humidity profiles by a one-dimensional variational algorithm.
Background
The microwave radiometer can obtain the observed bright temperature by detecting the microwave radiation of the ground-gas system, and can extract atmospheric parameter information such as atmospheric temperature, atmospheric humidity, precipitation and the like from the observed bright temperature by using an inversion algorithm. The one-dimensional variational algorithm is used as an inversion algorithm widely applied, and essentially comprises the steps of inputting an initial value of an atmospheric parameter into a microwave radiation transmission model to calculate a simulated bright temperature, continuously adjusting the initial value through an iteration process to enable the simulated bright temperature generated by adjusting the initial value to be as close to an observed bright temperature of a microwave radiometer as possible, and taking the adjusted initial value after the iteration is finished as an inversion value of the atmospheric parameter to further obtain atmospheric parameter information. At present, a plurality of business inversion systems are used as core algorithms in the one-dimensional variational algorithm, such as an inversion software package AAPP operated in a business mode in Korea, a business inversion system MIRS of the National Oceanic and Atmospheric Administration (NOAA) in the United states, an inversion system 1D-Var developed by the European middle weather forecast center (ECMWF), and the like. The one-dimensional variational algorithm is a method for directly inverting the radiation transmission process of microwaves in the atmosphere from a physical angle and improving the parameter generation method influencing the inversion accuracy of the algorithm, and has important significance for inverting the atmospheric parameters with higher accuracy by the one-dimensional variational algorithm.
The one-dimensional variational algorithm is a typical physical inversion algorithm, and parameters influencing the inversion accuracy of the algorithm comprise: initial value, background covariance matrix, calculation precision of radiation transmission model, precision of microwave radiometer observation brightness temperature and the like. Among a plurality of parameters influencing the inversion accuracy of the one-dimensional variational algorithm, the background covariance matrix directly reflects the actual state of the atmosphere, and the initial adjustment value generated in the iteration process of the one-dimensional variational algorithm can be limited in the actual state of the atmosphere. Therefore, when the background covariance matrix is calculated and generated by using the atmospheric data, the atmospheric data must be selected by comprehensively considering the time, the place, the surface difference, the seasonal variation, the climate difference of each latitude zone and other factors related to the atmospheric state, so that the selected atmospheric data can represent the real atmospheric state better. At present, for the generation of the background covariance matrix, atmospheric data with a long time span is mostly adopted, and the data volume is large, so that the limitation of the calculation capability is limited, and clear sky data is usually selected for calculation, but the clear sky data is not enough to describe the real atmospheric state. In addition, the background covariance matrix is calculated by using a global representative data set with a small data volume, but the method uses a small data volume of atmospheric data which is not enough to describe a complex atmospheric state, and meanwhile, the difference of atmospheric characteristics of different latitude zones is ignored. Therefore, the existing traditional method for generating the background covariance matrix limits the improvement of the accuracy of the atmospheric parameter inversion by the one-dimensional variational algorithm to a certain extent.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for improving the accuracy of the inversion of the atmospheric temperature and humidity profile by the one-dimensional variational algorithm, which has higher inversion accuracy and is simple and easy to operate.
In order to realize the technical purpose, the adopted technical scheme is as follows: a method for improving accuracy of inversion of atmospheric temperature and humidity profile by a one-dimensional variational algorithm comprises the following steps:
the method comprises the following steps: establishing a climate data set comprising n based on a temperature profile T, a humidity profile H, a cloud water profile CLW, a total cloud water content TCW t An atmospheric data set of group atmospheric data;
step two: counting the distribution characteristics of the total content TCW of the cloud water in the atmospheric data set, and establishing a simplified atmospheric data set L based on the distribution characteristics of the total content TCW of the cloud water ′ ；
Step three: sea-LAND classification is carried out on the simplified atmospheric data set to form an OCEAN atmospheric data set OCEAN and a LAND atmospheric data set LAND, and a latitude BAND BAND is established j Reclassifying the OCEAN atmospheric data set OCEAN and the LAND atmospheric data set LAND according to the latitude zone, and respectively establishing corresponding OCEAN latitude zone atmospheric data sets OCEAN j And LAND latitude zone atmospheric data set LAND j ；
Step four: OCEANs latitude band atmospheric data set OCEAN is used respectively j And LAND latitude zone atmospheric data set LAND j And the one-dimensional variational algorithm calls the corresponding background covariance matrix to carry out inversion calculation on the temperature profile and the humidity profile according to the sea-land classification and the geographical position of the input observed brightness temperature.
The first step of the invention specifically comprises the following steps:
selecting data in a climatology data set by taking a temperature profile, a humidity profile, a cloud water profile and the total content of cloud water as a group of atmospheric data, wherein the geographic range is (90 degrees N-90 degrees S, 180 degrees S)W-180 degrees E), the time range is 10 years, the data resolution is 0.5 degrees multiplied by 0.5 degrees, the pressure intensity layer corresponding to the profile data is subjected to grid layering from 1000hPa on the ground to 1hPa on the high altitude, the data is divided into d layers, the quality control of the data is carried out by taking the content of the cloud water in the cloud water profile as a standard, and the quality control standard of the data is as follows: if the content of cloud water in the cloud water profile is less than 0, the group of atmospheric data is abnormal data, the group of atmospheric data is deleted, and the data quality control is carried out to establish the data containing n t An atmospheric data set of group atmospheric data.
The second step of the invention specifically comprises:
will contain n t The air data set of the group air data is reduced into a reduced air data set containing 10000000 group air data, firstly, n is added to the air data set t Classifying the group atmosphere data according to the total content TCW of the cloud water to obtain 20 atmosphere data sets L i Wherein i is 1,2,3 … 20; each atmospheric data set L i Containing n Li Group atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L 1 :(TCW＝0mm)
L 2 :(0mm<TCW≤0.02mm)
L 3 :(0.02mm<TCW≤0.04mm)
L 4 :(0.04mm<TCW≤0.06mm)
L 5 :(0.06mm<TCW≤0.08mm)
L 6 :(0.08mm<TCW≤0.10mm)
L 7 :(0.10mm<TCW≤0.20mm)
L 8 :(0.20mm<TCW≤0.30mm)
L 9 :(0.30mm<TCW≤0.40mm)
L 10 :(0.40mm<TCW≤0.50mm)
L 11 :(0.50mm<TCW≤0.60mm)
L 12 :(0.60mm<TCW≤0.70mm)
L 13 :(0.70mm<TCW≤0.80mm)
L 14 :(0.80mm<TCW≤0.90mm)
L 15 :(0.90mm<TCW≤1.00mm)
L 16 :(1.00mm<TCW≤1.50mm)
L 17 :(1.50mm<TCW≤2.00mm)
L 18 :(2.00mm<TCW≤2.50mm)
L 19 :(2.50mm<TCW≤3.00mm)
L 20 :(TCW>3.00mm)
then, the atmospheric data sets L are respectively aligned i The simplification is carried out, and the simplification rule is that the atmosphere data set L is used i In the random selection
Grouping the atmospheric data to form a reduced atmospheric data set L ′ i Wherein, in the step (A),
the calculation method comprises the following steps:
finally, 20 reduced atmospheric data sets L ′ i Combined together, a reduced atmosphere data set L is created containing 10000000 groups of atmosphere data ′ 。
The method for classifying the simplified atmospheric data set sea and land to form the ocean atmospheric data set and the land atmospheric data set comprises the following steps:
firstly, according to the simplified atmospheric data set L established in the step two ′ The geographic coordinates of the groups of data and the geographic coordinates of the coastline, and the reduced atmospheric data set L ′ The method is divided into two types, namely, the simplified air data set over the OCEAN is an OCEAN air data set OCEAN, and the simplified air data set over the LAND is a LAND air data set LAND.
The specific method for establishing the corresponding ocean latitude zone atmospheric data set and land latitude zone atmospheric data set comprises the following steps:
establishing a latitude BAND BAND j Wherein j is 1,2,3 …,18, each BAND j The specific value ranges are as follows:
90°N≤BAND 1 <80°N
80°N≤BAND 2 <70°N
70°N≤BAND 3 <60°N
60°N≤BAND 4 <50°N
50°N≤BAND 5 <40°N
40°N≤BAND 6 <30°N
30°N≤BAND 7 <20°N
20°N≤BAND 8 <10°N
10°N≤BAND 9 <0°
0°≤BAND 10 <10°S
10°S≤BAND 11 <20°S
20°S≤BAND 12 <30°S
30°S≤BAND 13 <40°S
40°S≤BAND 14 <50°S
50°S≤BAND 15 <60°S
60°S≤BAND 16 <70°S
70°S≤BAND 17 <80°S
80°S≤BAND 18 ≤90°S
and finally according to the latitude of each group of atmospheric data in the OCEAN atmospheric data set OCEAN, BAND is adopted according to the latitude zone j Classifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEAN j Total 18, each OCEAN latitude zone atmosphere data set OCEAN j Has a data amount of
According to the latitude of each group of atmospheric data in the LAND atmospheric data set LAND, according to the latitude BAND BAND j Classifying to obtain corresponding LAND latitude zone atmospheric data set LAND j 18 in total, each land latitude zone atmosphereData set LAND j Has a data amount of
The fourth step of the invention specifically comprises:
OCEAN latitude zone atmosphere data set OCEAN established from step three j Selecting temperature profile and humidity profile, and making up
Ocean latitude area temperature and humidity matrix
Wherein
The front d is an ocean latitude zone atmospheric data set temperature profile, and the rear d is an ocean latitude zone atmospheric data set humidity profile; atmospheric data set LAND from terrestrial latitude zone j Medium selection of temperature profile and humidity profile, composition
Land latitude area temperature and humidity matrix
Wherein
The front d is a temperature profile of the atmospheric data set in the land latitude zone, the back d is a humidity profile of the atmospheric data set in the land latitude zone, and the calculation method of the background covariance matrix comprises the following steps:
wherein the content of the first and second substances,
element representing the p-th row and q-th column in the background covariance matrix, COV (m) p ,m q ) M is obtained by expression p And m q Covariance of (2) when m p And m q Respectively representing temperature and humidity matrix of ocean latitude zone
When the first column and the second column are the p-th column and the q-th column, 18 ocean latitude zone background covariance matrixes can be obtained through a formula (2); when m is p And m q Matrix for respectively representing temperature and humidity of land latitude zone
When the data is in the p-th column and the q-th column, 18 land latitude area background covariance matrixes can be obtained through a formula (2); and according to the established 18 ocean latitude zone background covariance matrixes and 18 land latitude zone covariance matrixes, respectively calling the corresponding background covariance matrixes to perform inverse calculation of the temperature profile and the humidity profile according to the latitude and the sea-land classification of the input observed bright temperature by using a one-dimensional variational algorithm.
The beneficial effects of the invention are as follows: when the background covariance matrix of the one-dimensional variational algorithm is calculated, time, place, earth surface difference, seasonal variation and difference of atmospheric features of different latitude zones of atmospheric data are comprehensively considered, global representative data with large data volume is established, the global representative data are classified according to the latitude zones, and then different background covariance matrices are generated aiming at different latitude zones. And calling a corresponding background covariance matrix according to the input geographical position of the observed brightness temperature by using a one-dimensional variational algorithm to perform inversion calculation. Compared with the traditional method for calculating the background covariance matrix, the background covariance matrix generated by the method can enable the one-dimensional variational algorithm to have higher inversion precision when inverting the atmospheric parameters, and the operation is simple and easy.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph comparing the effects of the inversion of the accuracy of the atmospheric temperature profile and the humidity profile of the method of the present invention and the conventional method.
Detailed Description
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.
A method for improving accuracy of inversion of atmospheric temperature and humidity profile by a one-dimensional variational algorithm comprises the following steps:
the method comprises the following steps: establishing a set of climate data comprising n based on a set of climate data comprising a temperature profile T, a humidity profile H, a cloud water profile CLW, a total cloud water content TCW t An atmospheric data set of group atmospheric data.
Selecting data in a climatology data set by taking a temperature profile T, a humidity profile H, a cloud water profile CLW and a total cloud water content TCW as a group of atmospheric data, wherein the geographic range is (90 degrees N-90 degrees S, 180 degrees W-180 degrees E), the time range is 10 years, the data resolution is 0.5 degrees multiplied by 0.5 degrees, a pressure layer corresponding to profile data is subjected to grid layering from 1000hPa to 1hPa at high altitude on the ground and is divided into d layers, and the quality control standard of the data is as follows: if the content of cloud water in the cloud water profile is less than 0, the group of atmospheric data is abnormal data, the group of atmospheric data is deleted, and the data quality control is carried out to establish the data containing n t An atmospheric data set of group atmospheric data.
Step two: counting the distribution characteristics of the total content TCW of the cloud water in the atmospheric data set, and establishing a simplified atmospheric data set L based on the distribution characteristics of the total content TCW of the cloud water ′ 。
The specific implementation method comprises the following steps: will contain n t The air data set of the group air data is reduced into a reduced air data set containing 10000000 group air data, firstly, n is added to the air data set t Classifying the group atmosphere data according to the total content TCW of the cloud water to obtain 20 atmosphere data sets L i Wherein i is 1,2,3 … 20; each atmospheric data set L i Included
Group atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L 1 :(TCW＝0mm)
L 2 :(0mm<TCW≤0.02mm)
L 3 :(0.02mm<TCW≤0.04mm)
L 4 :(0.04mm<TCW≤0.06mm)
L 5 :(0.06mm<TCW≤0.08mm)
L 6 :(0.08mm<TCW≤0.10mm)
L 7 :(0.10mm<TCW≤0.20mm)
L 8 :(0.20mm<TCW≤0.30mm)
L 9 :(0.30mm<TCW≤0.40mm)
L 10 :(0.40mm<TCW≤0.50mm)
L 11 :(0.50mm<TCW≤0.60mm)
L 12 :(0.60mm<TCW≤0.70mm)
L 13 :(0.70mm<TCW≤0.80mm)
L 14 :(0.80mm<TCW≤0.90mm)
L 15 :(0.90mm<TCW≤1.00mm)
L 16 :(1.00mm<TCW≤1.50mm)
L 17 :(1.50mm<TCW≤2.00mm)
L 18 :(2.00mm<TCW≤2.50mm)
L 19 :(2.50mm<TCW≤3.00mm)
L 20 :(TCW>3.00mm)
then, the atmospheric data sets L are respectively aligned i The simplification is carried out, and the simplification rule is that the atmosphere data set L is used i In the random selection
Grouping the atmospheric data to form a reduced atmospheric data set L ′ i Wherein, in the process,
the calculating method comprises the following steps:
finally, 20 reduced atmospheric data sets L ′ i Combined together, a reduced atmosphere data set L is created containing 10000000 groups of atmosphere data ′ 。
Step three: carrying out sea-LAND classification on the simplified atmospheric data set to form an OCEAN atmospheric data set OCEAN and a LAND atmospheric data set LAND, and establishing a latitude BAND BAND j Reclassifying the OCEAN atmosphere data set OCEAN and the LAND atmosphere data set LAND according to the latitude zone, and respectively establishing corresponding OCEAN latitude zone atmosphere data sets OCEAN j And LAND latitude zone atmospheric data set LAND j 。
The specific implementation method comprises the following steps: firstly, establishing a simplified atmospheric data set L according to the step two ′ The geographic coordinates of the groups of data and the geographic coordinates of the coastline, and the reduced atmospheric data set L ′ The method is divided into two types, namely the simplified atmospheric data set over the OCEAN is an OCEAN atmospheric data set OCEAN, and the simplified atmospheric data set over the LAND is a LAND atmospheric data set LAND.
Establishing a latitude BAND BAND j Wherein j is 1,2,3 …,18, each BAND j The specific value ranges are as follows:
90°N≤BAND 1 <80°N
80°N≤BAND 2 <70°N
70°N≤BAND 3 <60°N
60°N≤BAND 4 <50°N
50°N≤BAND 5 <40°N
40°N≤BAND 6 <30°N
30°N≤BAND 7 <20°N
20°N≤BAND 8 <10°N
10°N≤BAND 9 <0°
0°≤BAND 10 <10°S
10°S≤BAND 11 <20°S
20°S≤BAND 12 <30°S
30°S≤BAND 13 <40°S
40°S≤BAND 14 <50°S
50°S≤BAND 15 <60°S
60°S≤BAND 16 <70°S
70°S≤BAND 17 <80°S
80°S≤BAND 18 ≤90°S
finally according to the latitude of each group of atmospheric data in the OCEAN atmospheric data set OCEAN, BAND is adopted according to the latitude j Classifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEAN j Total 18, each OCEAN latitude zone atmosphere data set OCEAN j Is a data amount of
According to the latitude of each group of atmospheric data in the LAND atmospheric data set LAND, according to the latitude BAND BAND j Classifying to obtain corresponding LAND latitude zone atmospheric data set LAND j 18 in total, each LAND latitude zone atmospheric data set LAND j Has a data amount of
Step four: OCEAN using OCEAN latitude band atmospheric data sets, respectively j And LAND latitude zone atmospheric data set LAND j And the one-dimensional variational algorithm calls the corresponding background covariance matrix to carry out inversion calculation on the temperature profile and the humidity profile according to the sea-land classification and the geographical position of the input observed brightness temperature.
The specific implementation method comprises the following steps: OCEAN latitude zone atmosphere data set OCEAN established from step three j Medium selection of temperature profile and humidity profile, composition
Ocean latitude area temperature and humidity matrix
Wherein
The front d is an ocean latitude zone atmospheric data set temperature profile, and the rear d is an ocean latitude zone atmospheric data set humidity profile; atmospheric data set LAND from terrestrial latitude zone j Medium selection of temperature profile and humidity profile, composition
Land latitude area temperature and humidity matrix
Wherein
The front d is a temperature profile of the atmospheric data set in the land latitude zone, the back d is a humidity profile of the atmospheric data set in the land latitude zone, and the calculation method of the background covariance matrix comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
element representing the p-th row and q-th column of the background covariance matrix, COV (m) p ,m q ) To obtain m p And m q Covariance of (2) when m p And m q Respectively representing temperature and humidity matrix of ocean latitude zone
When the first column and the second column are the p-th column and the q-th column, 18 ocean latitude zone background covariance matrixes can be obtained through a formula (2); when m is p And m q Matrix for respectively representing temperature and humidity of land latitude zone
In the case of the p-th and q-th columns, 18 numbers can be obtained by the formula (2)Land latitude zone background covariance matrix; and according to the established 18 ocean latitude zone background covariance matrixes and 18 land latitude zone covariance matrixes, respectively calling the corresponding background covariance matrixes to perform inverse calculation of the temperature profile and the humidity profile according to the latitude and the sea-land classification of the input observed bright temperature by using a one-dimensional variational algorithm.
Example 1
The selected climatological data set is an ERA Interim reanalysis data set of a European middle-term weather forecast center (ECMWF), the temperature profile, the humidity profile, the cloud water profile and the total content of cloud water are taken as a group of atmospheric data, the data are selected from the climatological data set, the geographical range is (90 degrees N-90 degrees S, 180 degrees W-180 degrees E), the time range is 2009, 1 month to 2018, 12 months, wherein the four moments including 00:00, 06:00, 12:00 and 18:00 are contained every day, the data resolution is 0.5 degrees multiplied by 0.5 degrees, and the pressure layers corresponding to the profile data are layered from the ground (1000hPa) to the grid of high altitude (1hPa) into 37 layers: 1000hPa, 975hPa, 950hPa, 925hPa, 900hPa, 875hPa, 850hPa, 825hPa, 800hPa, 775hPa, 750hPa, 700hPa, 650hPa, 600hPa, 550hPa, 500hPa, 450hPa, 400hPa, 350hPa, 300hPa, 250hPa, 225hPa, 200hPa, 175hPa, 150hPa, 125hPa, 100hPa, 70hPa, 50hPa, 30hPa, 20hPa, 10hPa, 7hPa, 5hPa, 3hPa, 2hPa and 1 hPa. The temperature profile, humidity profile, cloud water profile, and total cloud water content may be represented as T, H, CLW and TCW, respectively; and (3) performing quality control on the data by taking the cloud water content in the cloud water profile as a standard, wherein the quality control standard of the data is as follows: and if the cloud water content in the cloud water profile is less than 0, deleting the group of atmospheric data as abnormal data, and establishing an atmospheric data set containing 3564534841 groups of atmospheric data through quality control of the data.
In order to reduce the air data set containing 3564534841 groups of air data into a reduced air data set containing 10000000 groups of air data, 3564534841 groups of air data are firstly classified in the air data set according to the total cloud water content TCW, and 20 air data sets L are obtained i Wherein i is 1,2,3 … 20. Each atmospheric data set L i Included
And (4) group atmosphere data. The rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L 1 :(TCW＝0mm)；L 2 :(0mm<TCW≤0.02mm)；
L 3 :(0.02mm<TCW≤0.04mm)；L 4 :(0.04mm<TCW≤0.06mm)；
L 5 :(0.06mm<TCW≤0.08mm)；L 6 :(0.08mm<TCW≤0.10mm)；
L 7 :(0.10mm<TCW≤0.20mm)；L 8 :(0.20mm<TCW≤0.30mm)；
L 9 :(0.30mm<TCW≤0.40mm)；L 10 :(0.40mm<TCW≤0.50mm)；
L 11 :(0.50mm<TCW≤0.60mm)；L 12 :(0.60mm<TCW≤0.70mm)；
L 13 :(0.70mm<TCW≤0.80mm)；L 14 :(0.80mm<TCW≤0.90mm)；
L 15 :(0.90mm<TCW≤1.00mm)；L 16 :(1.00mm<TCW≤1.50mm)；
L 17 :(1.50mm<TCW≤2.00mm)；L 18 :(2.00mm<TCW≤2.50mm)；
L 19 :(2.50mm<TCW≤3.00mm)；L 20 :(TCW>3.00mm)。
wherein each atmospheric data set L i Amount of data contained
As shown in table 1.
TABLE 1 atmospheric data set L i Amount of data of
Then, the atmospheric data sets L are respectively aligned i The simplification is carried out, and the simplification rule is that the atmosphere data set L is used i In the random selection
Grouping the atmospheric data to form a reduced atmospheric data set L ′ i Wherein, in the process,
the calculation is performed according to equation (1):
each reduced atmospheric dataset L ′ i Amount of data contained
As shown in table 2.
TABLE 2 reduced atmospheric data set L ′ i Amount of data of
Finally, 20 simplified atmosphere group data L ′ i Combined together to form a reduced atmospheric data set L ′ For a total of 10000000 sets of atmospheric data.
For a reduced atmospheric dataset L ′ According to the geographic coordinates of each group of data and the coastline geographic coordinates provided in the MATLAB m _ map toolbox, the simplified atmospheric data set L is divided into ′ The method comprises the following steps of (1) classifying the data into two types, namely, an overhead reduced atmosphere data set on the OCEAN is an OCEAN atmosphere data set OCEAN, and an overhead reduced atmosphere data set on the LAND is a LAND atmosphere data set LAND; establishing a latitude BAND BAND j Wherein j is 1,2,3 …,18, each BAND j The specific value ranges of (A) are as follows:
90°N≤BAND 1 <80°N；80°N≤BAND 2 <70°N；70°N≤BAND 3 <60°N；
60°N≤BAND 4 <50°N；50°N≤BAND 5 <40°N；40°N≤BAND 6 <30°N；
30°N≤BAND 7 <20°N；20°N≤BAND 8 <10°N；10°N≤BAND 9 <0°；
0°≤BAND 10 <10°S；10°S≤BAND 11 <20°S；20°S≤BAND 12 <30°S；
30°S≤BAND 13 <40°S；40°S≤BAND 14 <50°S；50°S≤BAND 15 <60°S；
60°S≤BAND 16 <70°S；70°S≤BAND 17 <80°S；80°S≤BAND 18 ≤90°S。
in the OCEAN atmosphere data set OCEAN, according to the latitude where each group of atmosphere data is located, BAND is arranged according to the latitude zone j Classifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEAN j A total of 18 LAND atmospheric data sets LAND, according to the latitude of each group of atmospheric data, according to the latitude BAND BAND j Classifying to obtain corresponding LAND latitude zone atmospheric data set LAND j And the total number is 18. Wherein the OCEAN latitude zone atmosphere data set OCEAN j Amount of data of
And LAND latitude zone atmospheric data set LAND j Amount of data of
As shown in tables 3 and 4, respectively.
TABLE 3 Latitude with oceanic atmosphere data set OCEAN j Amount of data of
TABLE 4 Land Lap LAND atmospheric data set j Amount of data of
Atmosphere data set OCEAN from OCEAN latitude j Medium selection of temperature profile and humidity profile, composition
Ocean latitude area temperature and humidity matrix
Wherein
The front 37 is a temperature profile and the rear 37 is a humidity profile. Atmospheric data set LAND from LAND latitude zone j Selecting temperature profile and humidity profile, and making up
Land latitude area temperature and humidity matrix
Wherein
The front 37 columns of (a) are temperature profiles and the rear 37 columns are humidity profiles. The calculation method of the background covariance matrix comprises the following steps:
wherein the content of the first and second substances,
element representing the p-th row and q-th column in the background covariance matrix, COV (m) p ,m q ) M is obtained by expression p And m q Covariance of (2) when m p And m q Respectively representing temperature and humidity matrix of ocean latitude zone
In the p-th column and the q-th column of (1), 18 background covariance matrices of 74 × 74 ocean latitude zones can be obtained by the formula (2); when m is p And m q Matrix for respectively representing temperature and humidity of land latitude zone
In the case of the p-th and q-th columns, 18 background covariance matrices of 74 × 74 land latitude zones can be obtained by the formula (2). And according to the established 18 ocean latitude zone background covariance matrixes and 18 land latitude zone covariance matrixes, respectively calling the corresponding background covariance matrixes to perform inverse calculation of the temperature profile and the humidity profile according to the latitude and the sea-land classification of the input observed bright temperature by using a one-dimensional variational algorithm.
In this embodiment, in order to verify that the inversion accuracy of the one-dimensional variational algorithm of the background covariance matrix generated by the method of the present invention is improved compared with that of the background covariance matrix generated by the conventional calculation method, the bright temperature observed by the wind cloud three-star microwave wet temperature detector (MWHTS) is input into the one-dimensional variational algorithm, and the inversion experiment of the atmospheric temperature and humidity profile is performed. The time range of observed light temperature used was from No. 9/month 1 in 2019 to No. 9/month 30 in 2019, and the geographical range was (25 ° N-45 ° N, 160 ° E-220 ° E). In order to perform precision verification on a temperature profile inversion value and a humidity profile inversion value obtained by one-dimensional variational algorithm inversion, the MWHTS observed bright temperature needs to be matched with the atmospheric temperature profile and the humidity profile in an ECMWF ERA interval reanalysis data set, and a matching data set is established, wherein the matching rule is as follows: the time error is less than 10 minutes and the latitude and longitude error is less than 0.1 degree. Then the matching dataset totals 87610 sets of data, of which 80% were randomly selected to form the analysis dataset and the remaining 20% formed the verification dataset. And (3) inputting the MWHTS observed bright temperature in the analysis data set into a one-dimensional variational algorithm to perform inversion of the atmospheric temperature and humidity profile, verifying the atmospheric temperature profile and the humidity profile in the data set to perform precision verification of an inversion result, namely calculating a root mean square error between an inversion value of the one-dimensional variational algorithm and the atmospheric temperature profile and the humidity profile in the verification data set.
In this embodiment, an iterative solution method is used in the one-dimensional variational algorithm to obtain a final temperature profile inversion value and a final humidity profile inversion value, and an iterative formula is as follows:
wherein n represents the number of iterations, and when n is 1, S 1 Indicating an initial value of the temperature profile and an initial value of the humidity profile; s n Representing the temperature and humidity profile iteration values at the nth iteration, S n+1 Representing an inversion value of the atmospheric parameter after the iteration process is finished, and representing a temperature profile inversion value and a humidity profile inversion value in the embodiment; f (S) n ) Representing the temperature profile iteration value and the humidity profile iteration value S n Inputting the data into a radiation transmission model to calculate and simulate the brightness temperature K n Denotes f (S) n ) For atmospheric parameter S n The derivative of (a) is determined,
represents a pair K n Find a transposition in which f (S) n ) And K n All the data are obtained by RTTOV calculation of a radiation transmission model;
represents the observed light temperature of the microwave radiometer; c ΨΨ A measurement error covariance matrix is taken as a diagonal matrix, and diagonal elements consist of squares of differences between observed bright temperatures and simulated bright temperatures in each channel of the microwave radiometer; s a Representing a background value of the temperature profile and a background value of the humidity profile, and respectively taking values of the background values of the temperature profile and the humidity profile in the analysis data set; c SS Representing the background covariance matrix.
In this example, the MWHTS observed bright temperature in the validation data set is input into a one-dimensional variational algorithm to perform the inversion of the temperature profile and the humidity profile three timesAnd (5) carrying out experiments. Except for the background covariance matrix C in each inversion experiment SS Except for the difference, the settings of other parameters are the same. The first inversion experiment uses a background covariance matrix generated by the method, the second inversion experiment uses a background covariance matrix generated by a global representative data set TIGR, and the third inversion experiment uses a background covariance matrix calculated by matching a clear air temperature profile and a humidity profile in a data set, wherein the clear air temperature profile and the humidity profile are selected by using a selection standard with the total content of cloud water being 0. The root mean square error between the temperature profile inversion value obtained by the three times of inversion experiments and the temperature profile in the verification data set is calculated, the root mean square error between the humidity profile inversion value obtained by the three times of inversion experiments and the humidity profile in the verification data set is calculated, and the calculation result is shown in fig. 2. From fig. 2, it can be found that when the background covariance matrix generated by the method of the present invention is applied to the one-dimensional variational algorithm, higher inversion accuracy can be obtained than when the background covariance matrix generated by using clear sky data and the background covariance matrix generated by using a global representative data set TIGR are applied to the one-dimensional variational algorithm.
Claims (3)
1. A method for improving accuracy of inversion of atmospheric temperature and humidity profile by a one-dimensional variational algorithm is characterized by comprising the following steps:
the method comprises the following steps: establishing a climate data set comprising n based on a temperature profile T, a humidity profile H, a cloud water profile CLW, a total cloud water content TCW t An atmospheric data set of group atmospheric data;
step two: counting the distribution characteristics of the total content TCW of the cloud water in the atmosphere data set, and establishing a simplified atmosphere data set L' based on the distribution characteristics of the total content TCW of the cloud water;
the second step specifically comprises:
will contain n t The air data set of the group air data is reduced into a reduced air data set containing 10000000 group air data, firstly, n in the air data set t Classifying the group atmosphere data according to the total content TCW of the cloud water to obtain 20 atmosphere data sets L i Wherein i is 1,2,3 … 20; each atmospheric data set L i Included
Group atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L 1 :(TCW＝0mm)
L 2 :(0mm<TCW≤0.02mm)
L 3 :(0.02mm<TCW≤0.04mm)
L 4 :(0.04mm<TCW≤0.06mm)
L 5 :(0.06mm<TCW≤0.08mm)
L 6 :(0.08mm<TCW≤0.10mm)
L 7 :(0.10mm<TCW≤0.20mm)
L 8 :(0.20mm<TCW≤0.30mm)
L 9 :(0.30mm<TCW≤0.40mm)
L 10 :(0.40mm<TCW≤0.50mm)
L 11 :(0.50mm<TCW≤0.60mm)
L 12 :(0.60mm<TCW≤0.70mm)
L 13 :(0.70mm<TCW≤0.80mm)
L 14 :(0.80mm<TCW≤0.90mm)
L 15 :(0.90mm<TCW≤1.00mm)
L 16 :(1.00mm<TCW≤1.50mm)
L 17 :(1.50mm<TCW≤2.00mm)
L 18 :(2.00mm<TCW≤2.50mm)
L 19 :(2.50mm<TCW≤3.00mm)
L 20 :(TCW>3.00mm)
then, the atmospheric data sets L are respectively aligned i The simplification is carried out, and the simplification rule is that the atmosphere data set L is used i In the random selection
Group atmosphere data, form a reduced atmosphere data set L' i Wherein, in the step (A),
the calculation method comprises the following steps:
finally, 20 reduced atmospheric data sets L' i Combining the data to establish a simplified air data set L' containing 10000000 groups of air data;
step three: sea-LAND classification is carried out on the simplified atmospheric data set to form an OCEAN atmospheric data set OCEAN and a LAND atmospheric data set LAND, and a latitude BAND BAND is established j Reclassifying the OCEAN atmosphere data set OCEAN and the LAND atmosphere data set LAND according to the latitude zone, and respectively establishing corresponding OCEAN latitude zone atmosphere data sets OCEAN j And LAND latitude zone atmospheric data set LAND j ；
The specific method for establishing the corresponding ocean latitude zone atmospheric data set and land latitude zone atmospheric data set comprises the following steps:
establishing a latitude BAND BAND j Wherein j is 1,2,3 …,18, each BAND j The specific value ranges of (A) are as follows:
90°N≤BAND 1 <80°N
80°N≤BAND 2 <70°N
70°N≤BAND 3 <60°N
60°N≤BAND 4 <50°N
50°N≤BAND 5 <40°N
40°N≤BAND 6 <30°N
30°N≤BAND 7 <20°N
20°N≤BAND 8 <10°N
10°N≤BAND 9 <0°
0°≤BAND 10 <10°S
10°S≤BAND 11 <20°S
20°S≤BAND 12 <30°S
30°S≤BAND 13 <40°S
40°S≤BAND 14 <50°S
50°S≤BAND 15 <60°S
60°S≤BAND 16 <70°S
70°S≤BAND 17 <80°S
80°S≤BAND 18 ≤90°S
and finally according to the latitude of each group of atmospheric data in the OCEAN atmospheric data set OCEAN, BAND is adopted according to the latitude zone j Classifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEAN j Total 18, each OCEAN latitude zone atmosphere data set OCEAN j Has a data amount of
According to the latitude of each group of atmospheric data in the LAND atmospheric data set LAND, according to the latitude BAND BAND j Classifying to obtain corresponding LAND latitude zone atmospheric data set LAND j 18 in total, each LAND latitude zone atmospheric data set LAND j Has a data amount of
Step four: OCEAN using OCEAN latitude band atmospheric data sets, respectively j And LAND latitude zone atmospheric data set LAND j Calculating the temperature profile and the humidity profile to generate a corresponding background covariance matrix, and calling the corresponding background covariance matrix to perform inverse calculation of the temperature profile and the humidity profile according to the sea-land classification and the geographic position of the input observed bright temperature by a one-dimensional variational algorithm;
the fourth step specifically comprises:
OCEAN latitude zone atmosphere data set OCEAN established from step three j Selecting temperature profile and humidity profile, and making up
Ocean latitude area temperature and humidity matrix
Wherein
The front d is an ocean latitude zone atmospheric data set temperature profile, and the rear d is an ocean latitude zone atmospheric data set humidity profile; atmospheric data set LAND from terrestrial latitude zone j Selecting temperature profile and humidity profile, and making up
Land latitude area temperature and humidity matrix
Wherein
The front d is a temperature profile of the atmospheric data set in the land latitude zone, the back d is a humidity profile of the atmospheric data set in the land latitude zone, and the calculation method of the background covariance matrix comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
element representing the p-th row and q-th column in the background covariance matrix, COV (m) p ,m q ) To obtain m p And m q Covariance of (2) when m p And m q Respectively representing temperature and humidity matrix of ocean latitude zone
When the first column and the second column are the p-th column and the q-th column, 18 ocean latitude zone background covariance matrixes can be obtained through a formula (2); when m is p And m q Matrix for respectively representing temperature and humidity of land latitude zone
At the p-th column and the q-th column, 18 land latitude zone background covariance matrixes can be obtained through a formula (2); and according to the established 18 ocean latitude zone background covariance matrixes and 18 land latitude zone covariance matrixes, respectively calling the corresponding background covariance matrixes to perform inverse calculation of the temperature profile and the humidity profile according to the latitude and the sea-land classification of the input observed bright temperature by using a one-dimensional variational algorithm.
2. The method for improving the accuracy of the inversion of the atmospheric temperature and humidity profile by the one-dimensional variational algorithm according to claim 1, wherein the step one specifically comprises the following steps:
selecting data in a climatology data set by taking a temperature profile, a humidity profile, a cloud water profile and the total content of cloud water as a group of atmospheric data, wherein the geographic range is (90 degrees N-90 degrees S, 180 degrees W-180 degrees E), the time range is 10 years, the data resolution is 0.5 degrees multiplied by 0.5 degrees, the pressure layer corresponding to the profile data is subjected to grid layering from 1000hPa on the ground to 1hPa on the high altitude and is divided into d layers, the data quality control is carried out by taking the cloud water content in the cloud water profile as a standard, and the data quality control standard is as follows: if the content of cloud water in the cloud water profile is less than 0, the group of atmospheric data is abnormal data, the group of atmospheric data is deleted, and the data quality control is carried out to establish the data containing n t An atmospheric data set of group atmospheric data.
3. The method for improving the accuracy of the atmospheric temperature and humidity profile inversion based on the one-dimensional variational algorithm as claimed in claim 1, wherein the method for performing sea-land classification on the reduced atmospheric data set to form the ocean atmospheric data set and the land atmospheric data set comprises the following steps:
firstly, dividing the reduced atmosphere data set L 'into two types according to the geographical coordinates of each group of data in the reduced atmosphere data set L' established in the step two and the geographical coordinates of a coastline, namely, the reduced atmosphere data set over the OCEAN is an OCEAN atmosphere data set OCEAN, and the reduced atmosphere data set over the LAND is a LAND atmosphere data set LAND.
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