CN108874734B - Global land rainfall inversion method - Google Patents
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Abstract
The invention discloses a global land precipitation inversion method, which comprises the following steps: step 1) land level 1 data and TMPA 3B42 land data are matched to obtain a matching data set; calculating three bright temperature differences, judging the type of convection intensity of the cloud system, and obtaining land rainfall by adopting different precipitation inversion models for different types; and 2) judging whether precipitation occurs or not according to the convection intensity, and combining the precipitation rate in the step 1) to obtain the final land rainfall rate. The global land rainfall inversion method can effectively judge whether a rainfall event occurs or not and invert the rainfall rate, realizes global land rainfall inversion, effectively improves the data utilization rate of FY-3C/MWHS-II, has high accuracy of land rainfall inversion results, and can be used as a reference of a land rainfall inversion business algorithm of FY-3C/MWHS-II, thereby laying a foundation for subsequent researches on numerical weather forecast, data assimilation and the like.
Description
Technical Field
The invention relates to the field of microwave remote sensing precipitation, in particular to a global land precipitation inversion method which can be used for a 03-star novel wind-cloud microwave hygrometer (FY-3C/MWHS-II) and is suitable for a global land area with a latitude within a range of 50 degrees S-50 degrees N.
Background
Precipitation is an important parameter for weather analysis, weather forecast, and climate change research. It plays an important role in the global energy and water circulation process and has particularly important significance for the research of climate change. Precipitation varies greatly in time and space and is one of the most difficult climate factors to detect. Currently, there are three main methods for measuring land precipitation: ground rain gauge measurement, ground radar remote sensing and satellite remote sensing. Although the measurement of the ground rain gauge is the reference of other detection means, the time and space resolution of the ground rain gauge can not meet the requirement of atmospheric scientific research, the remote sensing of the ground radar also has the problem of the space resolution, and meanwhile, the radar detection precipitation is shielded by the terrain, the radar ray lifting and the uncertainty of the Z-R relation are influenced, and the adverse factors of fixed position, high manufacturing cost and the like are difficult to implement in a complex area. The satellite-borne remote sensing has the advantages of wide space coverage, high time and space resolution and the like, so the development is rapid. Compared with infrared, the microwave can penetrate cloud rain and the like, so the satellite-borne microwave remote sensing has unique advantages.
A03 star (FY-3C) of a second generation polar orbit meteorological satellite, namely a Fengyun three-number meteorological satellite in China is successfully launched in 2013, 9 and 23 days. The wind cloud No. three ground observation flies around the south and north poles of the earth for 14 circles each day, the time of flying one circle is 102 minutes, and the height of a satellite is 836 km. An important load novel microwave hygrometer (MWHS-II) on the device is used as a renewal product on an FY-3A meteorological satellite and an FY-3B meteorological satellite, an original main atmospheric water vapor detection frequency point is kept at 183.31-GHz, the frequency point is expanded into 5 detection channels which have different responses to the vertical characteristics of water vapor of layers with different heights in the atmosphere, the channel positioned in the center of a water vapor absorption band can detect the water vapor distribution information of about 300 hectopascal on the upper atmosphere, the channel gradually moves away from the center of an absorption line to a wing area, the penetration depth is gradually enhanced, and the water vapor distribution information of 400, 500 and 700 hectopascal on the middle atmosphere and 850 hectopascal on the bottom layer can be detected; meanwhile, the window area channels are set to be 89-GHz and 150-GHz and are used for detecting the microwave radiation information on the earth surface; 118-GHz that is used for polar orbit meteorological satellite for the first time internationally sets up to 8 passageways as oxygen absorption frequency point, can be used to the atmospheric temperature of vertical height to survey, and 4 passageways (being passageway 6-9) can receive the precipitation information of lower floor in the troposphere thereafter, and 5 steam detection passageways of 183.31-GHz combine together, have realized surveying simultaneously of humidity and temperature. The 15 channel setup parameter case for FY-3C/MWHS-II is shown in Table 1. Novel microwave hygrometer is as a passive microwave remote sensor, not only can pierce through cloud layer and rain zone, and can pierce through the earth's surface or the vegetation of certain degree of depth, a weather information such as humidity and temperature, steam content, precipitation amount for all-weather detection global earth's surface and different altitude layer, the ability of surveying precipitation has, provide timely accurate atmospheric humidity initial field information for numerical weather forecast, promote the monitoring early warning ability to disastrous strong convection weather such as typhoon rainstorm, have important effect in atmospheric survey and land observation.
TABLE 1 FY-3C/MWHS-II channel setup parameters
From the perspective of FY-3C/MWHS-II hardware design, the level reaches the international advanced level, but from the perspective of data application, compared with foreign similar loads AMSU, ATMS and the like, the data utilization rate is low, and the global land precipitation inversion research based on FY-3C/MWHS-II has important significance. The FY-3C/MWHS-II level 1 data mainly comprises sea and land identification codes, brightness temperature, instrument observation zenith angle, geographical position, time and other information. The FY-3C/MWHS-II has higher data quality, is assimilated by the European ECMWF into a service assimilation system in 2016 and 4 months, and has a positive effect on global numerical weather forecast.
The traditional land rainfall inversion method adopts a low-frequency wave band to detect land surface information, but the land surface emissivity is high, microwave signals are easily submerged in a strong radiation background, so that the precipitation inversion algorithm is not accurately established, and the inversion precision is poor.
118-GHz and 183-GHz frequency points arranged on the FY-3C/MWHS-II are used for absorption channels for atmospheric detection, compared with channels in a window area, the absorption channels have two advantages in inverting precipitation, one is that the absorption channels are not as transparent as the channels in the window area, so that the absorption channels are relatively insensitive to underlying surfaces, a new idea is brought for precipitation inversion in a land area, and the atmospheric chromatography detection capability of the absorption channels also brings hopes for water condensate profile detection; compared with the window channel inversion precipitation, the absorption channel inversion precipitation result for atmospheric detection is more stable and higher in precision, and particularly in a land area, the absorption channel has potential detection capability on weak land precipitation and snowfall. The window area channel and the absorption channel are combined to realize the comprehensive inversion of precipitation, and the method becomes a development direction of microwave precipitation remote sensing. Trmm (pharmaceutical Rainfall measurement mission) multi-satellite Precipitation data tmpa (trmm Precipitation analysis) undergoes multiple upgrades in the last 10 years (V5-V7), and because a plurality of satellite data are fused and the data quality is high, the trmm (pharmaceutical Rainfall measurement simulation) is successfully applied to the research fields of Precipitation, hydrology, climate and the like. TMPA 3B42(V7) data is an important research precipitation product widely used internationally due to its high accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the existing land precipitation method, all channels based on FY-3C/MWHS-II are distributed in a high-frequency band, solid water condensate and large water drops in cloud have strong scattering effect on high-frequency microwaves, and land precipitation can be well inverted by utilizing the principle. Therefore, the method for inverting the global land precipitation by combining the FY-3C/MWHS-II data and the TMPA 3B42 data is provided, and the precipitation inversion method is suitable for the global land area with the latitude in the range of 50 degrees S-50 degrees N.
In order to achieve the above object, the present invention provides a global land precipitation inversion method, including:
step 1) matching land level 1 data with TMPA 3B42 data to obtain a matching data set; calculating three bright temperature differences, judging the type of convection intensity of the cloud system, and obtaining land rainfall by adopting different precipitation inversion models for different types;
and 2) judging whether the rainfall falls or not according to the convection intensity, and combining the rainfall rate in the step 1) to obtain the final land rainfall rate.
As an improvement of the above method, the step 1) specifically includes:
step 1-1) selecting and reading land level 1 data of a satellite load according to a sea-land identification code, and extracting brightness temperature, geographical position and time information of 15 channels;
step 1-2) extracting precipitation, geographic position and time information in TMPA 3B42 data;
step 1-3) carrying out data matching on the data in the step 1-1) and the data in the step 1-2) according to a land matching rule to obtain a matching data set meeting the requirement;
step 1-4) calculating three bright temperature differences according to the channel bright temperatures in the matching data set obtained in step 1-3):
Δ1＝TB183±1-TB183±7 (3)
Δ2＝TB183±3-TB183±7 (4)
Δ3＝TB183±1-TB183±3 (5)
wherein, Delta1Is the first bright temperature difference, Δ2Is the second bright temperature difference, Δ3Is the third quantity of temperature difference, TB183±1、TB183±3And TB183±7The brightness temperature of 183 +/-1-GHz, 183 +/-3-GHz and 183 +/-7-GHz channels respectively;
step 1-5) judging the type of the convection intensity of the cloud system:
CI＝1 Δ2>0,Δ2>Δ1,Δ2>Δ3 (6)
CI＝2 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2>Δ3 (7)
CI＝3 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2＜Δ3 (8)
wherein, CI is 1 to judge as weak convection, CI is 2 to judge as medium convection, and CI is 3 to judge as strong convection;
step 1-6) performing precipitation inversion on the weather conditions of CI (1), CI (2) and CI (3) determined in the step 1-5) by using a BP neural network model 1, a BP neural network model 2 and a linear regression model 3 respectively; meanwhile, carrying out precipitation inversion on other unclassified weather conditions by using a BP neural network model 4; and obtaining the land rainfall rate.
As an improvement of the above method, the land matching rule of step 1-3) is as follows:
(a) unreasonable data are removed by adopting a brightness temperature extreme value method, and a brightness temperature between 50K and 400K is selected;
(b) removing data which do not meet the requirements by adopting a precipitation extreme value method, and selecting precipitation between 0mm/hr and 100 mm/hr;
(c) selecting data with the time difference not more than 30min before and after matching the data, wherein the time resolution is set to be 30 min;
(d) selecting data with the latitude in the range of 50 DEG S-50 DEG N;
(e) data having a difference in longitude and latitude not exceeding 0.25 before and after matching data is selected.
As an improvement of the above method, the BP neural network model 1 uses a BP neural network precipitation inversion algorithm under a weather condition that CI is 1 and is established by 15 full-channel bright temperatures of FY-3C/MWHS-II; the BP neural network model 2 uses a BP neural network precipitation inversion algorithm under the condition that CI (common index) is 2 and is established by 15 full-channel bright temperatures of FY-3C/MWHS-II; the BP neural network model 4 uses a BP neural network precipitation inversion algorithm under all-weather conditions established by 15 full-channel bright temperatures of FY-3C/MWHS-II, and the linear regression model 3 uses a linear regression precipitation inversion algorithm under 3 weather conditions established by CI (common interface) established by 15 full-channel bright temperatures of FY-3C/MWHS-II.
As an improvement of the above method, the step 2) specifically includes:
step 2-1) judging weather conditions of CI-2 and CI-3 as the conditions that the precipitation event occurrence probability is large, and judging the rest conditions as the conditions that the precipitation event occurrence probability is small;
step 2-2) setting the precipitation rate obtained by inversion in the step 1-6) to 0mm/hr according to precipitation events smaller than 0.25mm/hr in the case that the probability of precipitation events is smaller in the step 2-1);
and 2-3) setting the rainfall rate less than 0mm/hr obtained by inversion in the steps 1-6) as 0 mm/hr.
The invention has the advantages that:
1. the global land rainfall inversion method can effectively judge whether a rainfall event occurs or not and invert the rainfall rate, realizes global land rainfall inversion, effectively improves the data utilization rate of FY-3C/MWHS-II, has high accuracy of land rainfall inversion results obtained by the method, can be used as a reference of a land rainfall inversion business algorithm of FY-3C/MWHS-II, and lays a foundation for subsequent researches on numerical weather forecast, data assimilation and the like;
2. the global land precipitation inversion method provided by the invention improves the utilization rate of FY-3C/MWHS-II data, the correlation of land precipitation inversion results reaches 0.74, and the method has higher application value.
Drawings
FIG. 1 is a schematic overall flow chart of the global land precipitation inversion method of the present invention;
FIG. 2 is a schematic flow chart of an inversion algorithm in the global land precipitation inversion method of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and embodiments for a clear and complete description of a global land precipitation inversion method.
FIG. 1 is a schematic overall flow chart of the global land precipitation inversion method. As shown in FIG. 1, the inversion method first matches the land data extracted from FY-3C/MWHS-II and TMPA 3B42(V7) data in time and space according to the matching rule of land; then, according to the detection characteristics of the FY-3C/MWHS-II water vapor absorption channel 183-GHz, a calculation method of the Convection Intensity Convention Intensity (CI) of the cloud system is provided, the possibility of occurrence of a precipitation event is judged according to the Convection Intensity (CI), and different inversion model algorithms are selected according to different types of the Convection Intensity (CI) for precipitation inversion; and finally, perfecting the precipitation inversion result again by combining the possibility of precipitation events and the results of precipitation inversion by selecting different inversion model algorithms to obtain the final global land rainfall (mm/hr), and completing the global land rainfall inversion based on FY-3C/MWHS-II.
FIG. 2 is a schematic flow chart of an inversion algorithm in the novel global land precipitation inversion method of the present invention. As shown in fig. 2, the detailed steps are as follows:
step 1) selecting and reading land level 1 data of satellite load according to sea and land identification codes in FY-3C/MWHS-II data, and extracting information such as brightness temperature, geographical position, time and the like of 15 channels.
And step 2) extracting information such as precipitation, geographic position, time and the like in TMPA 3B42 data.
Step 3) carrying out data matching according to the land matching rules of FY-3C/MWHS-II and TMPA 3B42 to obtain a matching data set meeting the requirements, wherein the specific matching rules are as follows:
(a) unreasonable data are removed by adopting a brightness temperature extreme value method, and a brightness temperature between 50K and 400K is selected;
(b) removing data which do not meet the requirements by adopting a precipitation extreme value method, and selecting precipitation between 0mm/hr and 100 mm/hr;
(c) selecting data with the time difference not more than 30min before and after matching the data, wherein the time resolution is set to be 30 min;
(d) selecting data with the latitude in the range of 50 DEG S-50 DEG N;
(e) data having a difference in longitude and latitude not exceeding 0.25 ° before and after matching data is selected, i.e., the spatial resolution is set to 0.25 °.
Step 4) calculating the channel brightness temperature in the matching data set obtained in the step 3) according to the formulas (3) to (5) as follows:
Δ1＝TB183±1-TB183±7 (3)
Δ2＝TB183±3-TB183±7 (4)
Δ3＝TB183±1-TB183±3 (5)
wherein, Delta1Is the first bright temperature difference, Δ2Is the second bright temperature difference, Δ3Is the third quantity of temperature difference, TB183±1、TB183±3And TB183±7The brightness temperatures of three channels of FY-3C/MWHS-II 183 plus or minus 1-GHz, 183 plus or minus 3-GHz and 183 plus or minus 7-GHz respectively;
and step 5) determining the type of the convection intensity of the cloud system according to the formulas (6) to (8), wherein CI 1 is determined as weak convection, and CI 2 and CI 3 are determined as medium convection and strong convection, respectively.
CI＝1 Δ2>0,Δ2>Δ1,Δ2>Δ3 (6)
CI＝2 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2>Δ3 (7)
CI＝3 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2＜Δ3 (8)
And 6) judging that the weather conditions of CI-2 and CI-3 (medium convection and strong convection) judged in the step 5) are the conditions that the precipitation event occurrence probability is large, and judging that the rest conditions are the conditions that the precipitation event occurrence probability is small.
Step 7), performing precipitation inversion on the weather conditions of CI (1) (weak convection), CI (2) (medium convection) and CI (3) (strong convection) determined in the step 5) by using the BP neural network model 1, the BP neural network model 2 and the linear regression model 3 respectively; meanwhile, the other unclassified weather conditions are subjected to precipitation inversion by using the BP neural network model 4, so that the precipitation rate is obtained.
The BP neural network model 4 is the BP neural network precipitation inversion algorithm under the all-weather condition established by the 15 full-channel bright temperatures of FY-3C/MWHS-II, and the linear regression model 3 is the linear regression precipitation inversion algorithm under the condition of CI (weak convection) established by the 15 full-channel bright temperatures of FY-3C/MWHS-II, and the linear regression model 3 is the linear regression precipitation inversion algorithm under the condition of the 15 full-channel bright temperatures of FY-3C/MWHS-II.
And 8) setting the rainfall rate inverted in the step 7) to be 0mm/hr according to the rainfall events smaller than 0.25mm/hr in the case that the probability of the rainfall events is judged to be smaller in the step 6), and perfecting the rainfall inversion result.
And 9) setting the rainfall rate less than 0mm/hr obtained in the previous step, namely step 7), to be 0mm/hr, and perfecting the rainfall inversion result again to obtain the final global land rainfall rate (mm/hr).
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (2)
1. A method of global land precipitation inversion, the method comprising:
step 1) land level 1 data and TMPA 3B42 land data are matched to obtain a matching data set; calculating three bright temperature differences, judging the type of convection intensity of the cloud system, and obtaining land rainfall by adopting different precipitation inversion models for different types;
step 2) judging whether the rainfall falls or not according to the convection intensity, and combining the rainfall rate in the step 1) to obtain a final land rainfall rate;
the step 1) specifically comprises the following steps:
step 1-1) selecting and reading land level 1 data of a satellite load according to a sea-land identification code, and extracting brightness temperature, geographical position and time information of 15 channels;
step 1-2) extracting precipitation, geographic position and time information in TMPA 3B42 data;
step 1-3) carrying out data matching on the data in the step 1-1) and the data in the step 1-2) according to a land matching rule to obtain a matching data set meeting the requirement;
step 1-4) calculating three bright temperature differences according to the channel bright temperatures in the matching data set obtained in step 1-3):
Δ1＝TB183±1-TB183±7 (3)
Δ2＝TB183±3-TB183±7 (4)
Δ3＝TB183±1-TB183±3 (5)
wherein, Delta1Is the first bright temperature difference, Δ2Is the second bright temperature difference, Δ3Is the third bright temperature difference, TB183±1、TB183±3And TB183±7The brightness temperature of 183 +/-1-GHz, 183 +/-3-GHz and 183 +/-7-GHz channels respectively;
step 1-5) judging the type of the convection intensity of the cloud system:
CI＝1 Δ2>0,Δ2>Δ1,Δ2>Δ3 (6)
CI＝2 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2>Δ3 (7)
CI＝3 Δ1>0,Δ2>0,Δ3>0,Δ1>Δ2,Δ1>Δ3,Δ2＜Δ3 (8)
wherein, CI is 1 to judge as weak convection, CI is 2 to judge as medium convection, and CI is 3 to judge as strong convection;
step 1-6) performing precipitation inversion on the weather conditions of CI (1), CI (2) and CI (3) determined in the step 1-5) by using a BP neural network model 1, a BP neural network model 2 and a linear regression model 3 respectively; meanwhile, carrying out precipitation inversion on other unclassified weather conditions by using a BP neural network model 4; obtaining the land rainfall rate;
the step 2) specifically comprises the following steps:
step 2-1) judging weather conditions of CI-2 and CI-3 as the conditions that the precipitation event occurrence probability is large, and judging the rest conditions as the conditions that the precipitation event occurrence probability is small;
step 2-2) setting the precipitation rate obtained by inversion in the step 1-6) to 0mm/hr according to precipitation events smaller than 0.25mm/hr in the case that the probability of precipitation events is smaller in the step 2-1);
step 2-3) setting the rainfall rate less than 0mm/hr obtained by inversion in the step 1-6) as 0 mm/hr;
the land matching rule of the step 1-3) is as follows:
(a) unreasonable data are removed by adopting a brightness temperature extreme value method, and a brightness temperature between 50K and 400K is selected;
(b) removing data which do not meet the requirements by adopting a precipitation extreme value method, and selecting precipitation between 0mm/hr and 100 mm/hr;
(c) selecting data with the time difference not more than 30min before and after matching the data, wherein the time resolution is set to be 30 min;
(d) selecting data with the latitude in the range of 50 DEG S-50 DEG N;
(e) data having a difference in longitude and latitude not exceeding 0.25 before and after matching data is selected.
2. The global land precipitation inversion method of claim 1, wherein the BP neural network model 1 uses a BP neural network precipitation inversion algorithm under a weather condition of CI ═ 1 established by 15 full channel light temperatures of FY-3C/MWHS-II; the BP neural network model 2 uses a BP neural network precipitation inversion algorithm under the condition that CI (common index) is 2 and is established by 15 full-channel bright temperatures of FY-3C/MWHS-II; the BP neural network model 4 uses a BP neural network precipitation inversion algorithm under all-weather conditions established by 15 full-channel bright temperatures of FY-3C/MWHS-II, and the linear regression model 3 uses a linear regression precipitation inversion algorithm under 3 weather conditions established by CI (common interface) established by 15 full-channel bright temperatures of FY-3C/MWHS-II.
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