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CN112965144A - 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 PDF

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CN112965144A
CN112965144A CN202110178470.7A CN202110178470A CN112965144A CN 112965144 A CN112965144 A CN 112965144A CN 202110178470 A CN202110178470 A CN 202110178470A CN 112965144 A CN112965144 A CN 112965144A
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贺秋瑞
张永新
周莉
姬孟洛
陈苇航
张航
潘彤安
罗震
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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 enable the one-dimensional variational algorithm to have higher inversion accuracy when inverting the atmospheric parameters, and is simple and easy to operate.

Description

Method for improving accuracy of inversion of atmospheric temperature and humidity profile by one-dimensional variational algorithm
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 profile 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 parameter generation method for directly inverting the radiation transmission process of microwaves in the atmosphere from a physical angle and improving 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 TCWtAn 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 establishedjReclassifying 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 OCEANjAnd LAND latitude zone atmospheric data set LANDj；
Step four: OCEAN using OCEAN latitude band atmospheric data sets, respectivelyjAnd LAND latitude zone atmospheric data set LANDjAnd 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 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 ntAn atmospheric data set of group atmospheric data.
The second step of the invention specifically comprises:
will contain ntThe 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 settClassifying the group atmosphere data according to the total content TCW of the cloud water to obtain 20 atmosphere data sets LiWherein i is 1,2,3 … 20; each atmospheric data set LiContaining nLiGroup atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L1:(TCW＝0mm)
L2:(0mm<TCW≤0.02mm)
L3:(0.02mm<TCW≤0.04mm)
L4:(0.04mm<TCW≤0.06mm)
L5:(0.06mm<TCW≤0.08mm)
L6:(0.08mm<TCW≤0.10mm)
L7:(0.10mm<TCW≤0.20mm)
L8:(0.20mm<TCW≤0.30mm)
L9:(0.30mm<TCW≤0.40mm)
L10:(0.40mm<TCW≤0.50mm)
L11:(0.50mm<TCW≤0.60mm)
L12:(0.60mm<TCW≤0.70mm)
L13:(0.70mm<TCW≤0.80mm)
L14:(0.80mm<TCW≤0.90mm)
L15:(0.90mm<TCW≤1.00mm)
L16:(1.00mm<TCW≤1.50mm)
L17:(1.50mm<TCW≤2.00mm)
L18:(2.00mm<TCW≤2.50mm)
L19:(2.50mm<TCW≤3.00mm)
L20:(TCW>3.00mm)
then, the atmospheric data sets L are respectively alignediThe simplification is carried out, and the simplification rule is that the atmosphere data set L is usediIn the random selection
Figure BDA0002940742430000031
Grouping the atmospheric data to form a reduced atmospheric data set L′ iWherein
Figure BDA0002940742430000032
the calculation method comprises the following steps:
Figure BDA0002940742430000033
finally, 20 reduced atmospheric data sets L′ iCombined together, a reduced atmosphere data set L is created containing 10000000 groups of atmosphere data′。
The method for carrying out sea-land classification on the simplified atmospheric data set to form the ocean atmospheric data set and the land atmospheric data set 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.
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 BANDjWherein j is 1,2,3 …,18, each BANDjThe specific value ranges are as follows:
90°N≤BAND1<80°N
80°N≤BAND2<70°N
70°N≤BAND3<60°N
60°N≤BAND4<50°N
50°N≤BAND5<40°N
40°N≤BAND6<30°N
30°N≤BAND7<20°N
20°N≤BAND8<10°N
10°N≤BAND9<0°
0°≤BAND10<10°S
10°S≤BAND11<20°S
20°S≤BAND12<30°S
30°S≤BAND13<40°S
40°S≤BAND14<50°S
50°S≤BAND15<60°S
60°S≤BAND16<70°S
70°S≤BAND17<80°S
80°S≤BAND18≤90°S
and finally, according to the latitude of each group of atmospheric data in the OCEAN atmospheric data set OCEANAccording to latitude BAND BANDjClassifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEANjTotal 18, each OCEAN latitude zone atmosphere data set OCEANjHas a data amount of
Figure BDA0002940742430000041
According to the latitude of each group of atmospheric data in the LAND atmospheric data set LAND, according to the latitude BAND BANDjClassifying to obtain corresponding LAND latitude zone atmospheric data set LANDj18 in total, each LAND latitude zone atmospheric data set LANDjHas a data amount of
Figure BDA0002940742430000051
The fourth step of the invention specifically comprises:
OCEAN latitude zone atmosphere data set OCEAN established from step threejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000052
Ocean latitude area temperature and humidity matrix
Figure BDA0002940742430000053
Wherein
Figure BDA0002940742430000054
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 zonejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000055
Land latitude area temperature and humidity matrix
Figure BDA0002940742430000056
Wherein
Figure BDA0002940742430000057
Front d of (2) is the number of land latitude with atmosphereAccording to the temperature profile of the set, the rear d is the humidity profile of the atmospheric data set in the terrestrial latitude zone, and the calculation method of the background covariance matrix comprises the following steps:
Figure BDA0002940742430000058
wherein,
Figure BDA0002940742430000059
element representing the p-th row and q-th column in the background covariance matrix, COV (m)p,mq) To obtain mpAnd mqCovariance of (2) when mpAnd mqRespectively representing temperature and humidity matrix of ocean latitude zone
Figure BDA00029407424300000510
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 ispAnd mqMatrix for respectively representing temperature and humidity of land latitude zone
Figure BDA00029407424300000511
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.
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 the 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 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 TCWtAn 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 ntAn 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 ntReduction of an atmospheric dataset of group atmospheric data to contain 10000000 reduced air data set of groups of air data, first pair n in the air data settClassifying the group atmosphere data according to the total content TCW of the cloud water to obtain 20 atmosphere data sets LiWherein i is 1,2,3 … 20; each atmospheric data set LiIncluded
Figure BDA0002940742430000061
Group atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
L1:(TCW＝0mm)
L2:(0mm<TCW≤0.02mm)
L3:(0.02mm<TCW≤0.04mm)
L4:(0.04mm<TCW≤0.06mm)
L5:(0.06mm<TCW≤0.08mm)
L6:(0.08mm<TCW≤0.10mm)
L7:(0.10mm<TCW≤0.20mm)
L8:(0.20mm<TCW≤0.30mm)
L9:(0.30mm<TCW≤0.40mm)
L10:(0.40mm<TCW≤0.50mm)
L11:(0.50mm<TCW≤0.60mm)
L12:(0.60mm<TCW≤0.70mm)
L13:(0.70mm<TCW≤0.80mm)
L14:(0.80mm<TCW≤0.90mm)
L15:(0.90mm<TCW≤1.00mm)
L16:(1.00mm<TCW≤1.50mm)
L17:(1.50mm<TCW≤2.00mm)
L18:(2.00mm<TCW≤2.50mm)
L19:(2.50mm<TCW≤3.00mm)
L20:(TCW>3.00mm)
then, the atmospheric data sets L are respectively alignediThe simplification is carried out, and the simplification rule is that the atmosphere data set L is usediIn the random selection
Figure BDA0002940742430000071
Grouping the atmospheric data to form a reduced atmospheric data set L′ iWherein
Figure BDA0002940742430000072
the calculation method comprises the following steps:
Figure BDA0002940742430000073
finally, 20 reduced atmospheric data sets L′ iCombined together, a reduced atmosphere data set L is created containing 10000000 groups of atmosphere 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 establishedjReclassifying 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 OCEANjAnd LAND latitude zone atmospheric data set LANDj。
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 BANDjWherein j is 1,2,3 …,18, each BANDjThe specific value ranges are as follows:
90°N≤BAND1<80°N
80°N≤BAND2<70°N
70°N≤BAND3<60°N
60°N≤BAND4<50°N
50°N≤BAND5<40°N
40°N≤BAND6<30°N
30°N≤BAND7<20°N
20°N≤BAND8<10°N
10°N≤BAND9<0°
0°≤BAND10<10°S
10°S≤BAND11<20°S
20°S≤BAND12<30°S
30°S≤BAND13<40°S
40°S≤BAND14<50°S
50°S≤BAND15<60°S
60°S≤BAND16<70°S
70°S≤BAND17<80°S
80°S≤BAND18≤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 zonejClassifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEANjTotal 18, each OCEAN latitude zone atmosphere data set OCEANjHas a data amount of
Figure BDA0002940742430000081
According to the latitude of each group of atmospheric data in the LAND atmospheric data set LAND, according to the latitude BAND BANDjClassifying to obtain corresponding LAND latitude zone atmospheric data set LANDj18 in total, each LAND latitude zone atmospheric data set LANDjHas a data amount of
Figure BDA0002940742430000082
Step four: OCEAN using OCEAN latitude band atmospheric data sets, respectivelyjAnd LAND latitude zone atmospheric data set LANDjAnd 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.
Concrete implementation methodComprises the following steps: OCEAN latitude zone atmosphere data set OCEAN established from step threejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000091
Ocean latitude area temperature and humidity matrix
Figure BDA0002940742430000092
Wherein
Figure BDA0002940742430000093
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 zonejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000094
Land latitude area temperature and humidity matrix
Figure BDA0002940742430000095
Wherein
Figure BDA0002940742430000096
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:
Figure BDA0002940742430000097
wherein,
Figure BDA0002940742430000098
element representing the p-th row and q-th column in the background covariance matrix, COV (m)p,mq) To obtain mpAnd mqCovariance of (2) when mpAnd mqRespectively representing temperature and humidity matrix of ocean latitude zone
Figure BDA0002940742430000099
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 ispAnd mqMatrix for respectively representing temperature and humidity of land latitude zone
Figure BDA00029407424300000910
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.
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, the group of atmospheric data is abnormal data, the group of atmospheric data is deleted, and an atmospheric data set is established through quality control of the data, wherein the atmospheric data set comprises 3564534841 groups of atmospheric data.
To contain3564534841 sets of atmospheric data are reduced into a reduced atmospheric data set containing 10000000 sets of atmospheric data, 3564534841 sets of atmospheric data are classified in the atmospheric data set according to total cloud water content TCW, and 20 atmospheric data sets L are obtainediWherein i is 1,2,3 … 20. Each atmospheric data set LiIncluded
Figure BDA0002940742430000101
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:
L1:(TCW＝0mm)；L2:(0mm<TCW≤0.02mm)；
L3:(0.02mm<TCW≤0.04mm)；L4:(0.04mm<TCW≤0.06mm)；
L5:(0.06mm<TCW≤0.08mm)；L6:(0.08mm<TCW≤0.10mm)；
L7:(0.10mm<TCW≤0.20mm)；L8:(0.20mm<TCW≤0.30mm)；
L9:(0.30mm<TCW≤0.40mm)；L10:(0.40mm<TCW≤0.50mm)；
L11:(0.50mm<TCW≤0.60mm)；L12:(0.60mm<TCW≤0.70mm)；
L13:(0.70mm<TCW≤0.80mm)；L14:(0.80mm<TCW≤0.90mm)；
L15:(0.90mm<TCW≤1.00mm)；L16:(1.00mm<TCW≤1.50mm)；
L17:(1.50mm<TCW≤2.00mm)；L18:(2.00mm<TCW≤2.50mm)；
L19:(2.50mm<TCW≤3.00mm)；L20:(TCW>3.00mm)。
wherein each atmospheric data set LiAmount of data contained
Figure BDA0002940742430000102
As shown in table 1.
TABLE 1 atmospheric data set LiAmount of data of
Figure BDA0002940742430000103
Figure BDA0002940742430000111
Then, the atmospheric data sets L are respectively alignediThe simplification is carried out, and the simplification rule is that the atmosphere data set L is usediIn the random selection
Figure BDA0002940742430000112
Grouping the atmospheric data to form a reduced atmospheric data set L′ iWherein
Figure BDA0002940742430000113
the calculation is performed according to equation (1):
Figure BDA0002940742430000114
each reduced atmospheric dataset L′ iAmount of data contained
Figure BDA0002940742430000115
As shown in table 2.
TABLE 2 reduced atmospheric data set L′ iAmount of data of
Figure BDA0002940742430000116
Finally, 20 pieces of simplified atmosphere group data L are combined′ iCombined 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 is divided into two types, namely the simplified air data set over the ocean is an ocean air data set OCEAN, wherein the LAND overhead simplified atmospheric data set is a LAND atmospheric data set LAND; establishing a latitude BAND BANDjWherein j is 1,2,3 …,18, each BANDjThe specific value ranges are as follows:
90°N≤BAND1<80°N；80°N≤BAND2<70°N；70°N≤BAND3<60°N；
60°N≤BAND4<50°N；50°N≤BAND5<40°N；40°N≤BAND6<30°N；
30°N≤BAND7<20°N；20°N≤BAND8<10°N；10°N≤BAND9<0°；
0°≤BAND10<10°S；10°S≤BAND11<20°S；20°S≤BAND12<30°S；
30°S≤BAND13<40°S；40°S≤BAND14<50°S；50°S≤BAND15<60°S；
60°S≤BAND16<70°S；70°S≤BAND17<80°S；80°S≤BAND18≤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 zonejClassifying to obtain corresponding OCEAN latitude zone atmosphere data set OCEANjA total of 18 LAND atmospheric data sets LAND, according to the latitude of each group of atmospheric data, according to the latitude BAND BANDjClassifying to obtain corresponding LAND latitude zone atmospheric data set LANDjAnd the total number is 18. Wherein the OCEAN latitude zone atmosphere data set OCEANjAmount of data of
Figure BDA0002940742430000121
And LAND latitude zone atmospheric data set LANDjAmount of data of
Figure BDA0002940742430000122
As shown in tables 3 and 4, respectively.
TABLE 3 Latitude with oceanic atmosphere data set OCEANjAmount of data of
Figure BDA0002940742430000123
TABLE 4 Land Lap LAND atmospheric data setjAmount of data of
Figure BDA0002940742430000124
Figure BDA0002940742430000131
Atmosphere data set OCEAN from OCEAN latitudejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000132
Ocean latitude area temperature and humidity matrix
Figure BDA0002940742430000133
Wherein
Figure BDA0002940742430000134
The front 37 is a temperature profile and the rear 37 is a humidity profile. Atmospheric data set LAND from terrestrial latitude zonejMedium selection of temperature profile and humidity profile, composition
Figure BDA0002940742430000135
Land latitude area temperature and humidity matrix
Figure BDA0002940742430000136
Wherein
Figure BDA0002940742430000137
Is a temperature profile and the rear 37 is a humidity profile. The calculation method of the background covariance matrix comprises the following steps:
Figure BDA0002940742430000138
wherein，
Figure BDA0002940742430000139
Element representing the p-th row and q-th column in the background covariance matrix, COV (m)p,mq) To obtain mpAnd mqCovariance of (2) when mpAnd mqRespectively representing temperature and humidity matrix of ocean latitude zone
Figure BDA00029407424300001310
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 ispAnd mqMatrix for respectively representing temperature and humidity of land latitude zone
Figure BDA00029407424300001311
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 longitude and latitude 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:
Figure BDA0002940742430000141
wherein n represents the number of iterations, and when n is 1, S1Indicating an initial value of the temperature profile and an initial value of the humidity profile; snRepresenting the temperature and humidity profile iteration values at the nth iteration, Sn+1Representing 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 SnInputting the data into a radiation transmission model to calculate and simulate the brightness temperature KnDenotes f (S)n) For atmospheric parameter SnThe derivative of (a) of (b),
Figure BDA0002940742430000142
represents a pair KnFind a transposition in which f (S)n) And KnAll the data are obtained by RTTOV calculation of a radiation transmission model;
Figure BDA0002940742430000143
represents the observed light temperature of the microwave radiometer; cΨΨA 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; saRepresenting 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; cSSIndicating the backA scene covariance matrix.
In this example, the MWHTS observed bright temperature in the validation data set is input to a one-dimensional variational algorithm to perform three inversion experiments of the temperature profile and the humidity profile. Except for the background covariance matrix C in each inversion experimentSSExcept 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 a clear air temperature profile and a humidity profile in a matched data set, wherein the clear air temperature profile and the humidity profile are selected by using a selection standard that the total content of cloud water is 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 (6)

1. A method for improving the 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 an atmospheric data set containing group atmospheric data based on a climatology data set comprising a temperature profile, a humidity profile, a cloud water profile and a total content of cloud water;
step two: counting the distribution characteristics of the total cloud water content in the atmospheric data set, and establishing a simplified atmospheric data set based on the distribution characteristics of the total cloud water content;
step three: carrying out sea-land classification on the simplified atmospheric data set to form an ocean atmospheric data set and a land atmospheric data set, establishing a latitude zone, carrying out reclassification on the ocean atmospheric data set and the land atmospheric data set according to the latitude zone, and respectively establishing a corresponding ocean latitude zone atmospheric data set and a land latitude zone atmospheric data set;
step four: and respectively using the temperature profile and the humidity profile in the ocean latitude zone atmospheric data set and the land latitude zone atmospheric data set to calculate and generate corresponding background covariance matrixes, and calling the corresponding background covariance matrixes to perform inverse calculation on the temperature profiles and the humidity profiles according to the sea and land classification and the geographical position 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:
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 layers, the data quality control is carried out by taking the content of cloud water in the cloud water profile as a standard, and the data quality control standard is as follows: and if the cloud water content 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 an atmospheric data set containing the group of atmospheric data is established through quality control of the data.
3. 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 second step specifically comprises:
the method comprises the steps of reducing an atmosphere data set containing group atmosphere data into a reduced atmosphere data set containing 10000000 groups of atmosphere data, and classifying the group atmosphere data in the atmosphere data set according to the total content of cloud water to obtain 20 atmosphere data sets, wherein the group atmosphere data set comprises 10000000 groups of atmosphere data; each atmospheric data set contains group atmospheric data; the rules for classifying the atmospheric data set according to the total content of the cloud water are as follows:
then, respectively simplifying the atmosphere data sets, wherein the simplifying rule is to randomly select a group of atmosphere data in the atmosphere data sets to form a simplified atmosphere data set, and the calculation method comprises the following steps:
(1)
finally, 20 reduced atmosphere data sets are combined together to create a reduced atmosphere data set containing 10000000 sets of atmosphere data.
4. 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 simplified atmospheric data set into two types according to the geographical coordinates of each group of data in the simplified atmospheric data set established in the step two and the geographical coordinates of a coastline, namely, the simplified atmospheric data set over the ocean is an ocean atmospheric data set, and the simplified atmospheric data set over the land is a land atmospheric data set.
5. 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 method comprises the following steps: 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 zone, wherein the specific value ranges are as follows:
and finally, classifying according to latitude zones according to the latitude where each group of atmospheric data in the ocean atmospheric data set is located to obtain corresponding ocean latitude zone atmospheric data sets, wherein the number of the ocean latitude zone atmospheric data sets is 18, the data volume of each ocean latitude zone atmospheric data set is, classifying according to latitude zones according to the latitude where each group of atmospheric data in the land atmospheric data set is located to obtain corresponding land latitude zone atmospheric data sets, the number of the land latitude zone atmospheric data sets is 18, and the data volume of each land latitude zone atmospheric data set is.
6. 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 fourth step specifically comprises:
selecting a temperature profile and a humidity profile from the ocean latitude zone atmospheric data set established in the third step to form an ocean latitude zone temperature and humidity matrix, wherein the front part is an ocean latitude zone atmospheric data set temperature profile, and the rear part is an ocean latitude zone atmospheric data set humidity profile; selecting a temperature profile and a humidity profile from a land latitude zone atmospheric data set to form a multiplied land latitude zone temperature and humidity matrix, wherein the front row is the land latitude zone atmospheric data set temperature profile, the rear row is the land latitude zone atmospheric data set humidity profile, and the calculation method of the background covariance matrix comprises the following steps: wherein, the elements of the first row and the first column in the background covariance matrix represent the summed covariance, and when the sum respectively represents the first column and the second column of the ocean latitude zone temperature and humidity matrix, 18 ocean latitude zone background covariance matrices can be obtained through the formula (2); when the sum respectively represents the first column and the second column of the terrestrial latitude zone temperature and humidity matrix, 18 terrestrial 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.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113253365A (en) * 2021-07-01 2021-08-13 江苏铨铨信息科技有限公司 Satellite remote sensing cloud wind guide inversion forecasting method based on dynamics constraint and variation method
CN118364207A (en) * 2024-06-18 2024-07-19 国家卫星海洋应用中心 Method, device, equipment and medium for determining background field

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001066377A (en) * 1999-08-26 2001-03-16 Mitsubishi Electric Corp Weather system
CN101799561A (en) * 2010-02-05 2010-08-11 民政部国家减灾中心 Snow disaster remote sensing monitoring simulation evaluation method based on disaster reduction small satellite
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
CN109507756A (en) * 2018-12-20 2019-03-22 中国气象局气象探测中心 A kind of station air pressure of GNSS steam and temperature are without instrument acquisition methods and system
CN109709558A (en) * 2019-03-06 2019-05-03 成都信息工程大学 A kind of physics inversion algorithm of satellite-borne microwave remote sensing Over-land PWV
KR102030626B1 (en) * 2018-09-28 2019-10-10 재단법인 국가농림기상센터 Estimation system of temporal surface air temperature under nocturnal inversion conditions and estimation method using the same
CN111610524A (en) * 2020-05-09 2020-09-01 华中科技大学 Ice cloud profile inversion method and system based on one-dimensional variational algorithm

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001066377A (en) * 1999-08-26 2001-03-16 Mitsubishi Electric Corp Weather system
CN101799561A (en) * 2010-02-05 2010-08-11 民政部国家减灾中心 Snow disaster remote sensing monitoring simulation evaluation method based on disaster reduction small satellite
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
KR102030626B1 (en) * 2018-09-28 2019-10-10 재단법인 국가농림기상센터 Estimation system of temporal surface air temperature under nocturnal inversion conditions and estimation method using the same
CN109507756A (en) * 2018-12-20 2019-03-22 中国气象局气象探测中心 A kind of station air pressure of GNSS steam and temperature are without instrument acquisition methods and system
CN109709558A (en) * 2019-03-06 2019-05-03 成都信息工程大学 A kind of physics inversion algorithm of satellite-borne microwave remote sensing Over-land PWV
CN111610524A (en) * 2020-05-09 2020-09-01 华中科技大学 Ice cloud profile inversion method and system based on one-dimensional variational algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘泽霖: "综合孔径微波辐射计大气温度廓线直接反演算法", 《中国优秀硕士学位论文全文数据库》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113253365A (en) * 2021-07-01 2021-08-13 江苏铨铨信息科技有限公司 Satellite remote sensing cloud wind guide inversion forecasting method based on dynamics constraint and variation method
CN113253365B (en) * 2021-07-01 2021-09-21 江苏铨铨信息科技有限公司 Satellite remote sensing cloud wind guide inversion forecasting method based on dynamics constraint and variation method
CN118364207A (en) * 2024-06-18 2024-07-19 国家卫星海洋应用中心 Method, device, equipment and medium for determining background field
CN118364207B (en) * 2024-06-18 2024-08-16 国家卫星海洋应用中心 Method, device, equipment and medium for determining background field

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