CN108874734A - A kind of Global Land Surface Precipitation inversion method - Google Patents
A kind of Global Land Surface Precipitation inversion method Download PDFInfo
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Abstract
The invention discloses a kind of Global Land Surface Precipitation inversion method, the method includes：Step 1) matches 1 grade of land data and the land TMPA 3B42 data, obtains matched data collection；Three bright temperature difference are calculated, thus determine the type of the convection intensity of cloud system, different precipitation inverse models is used to different types, obtains land rate of rainall；Step 2) determines whether precipitation according to convection intensity, obtains final land rate of rainall in conjunction with the precipitation rate of step 1).Whether Global Land Surface Precipitation inversion method of the invention can effectively differentiate the generation of precipitation event and inverting rate of rainall, realize Global Land Surface Precipitation inverting, effectively increase FY-3C/MWHS-II data user rate, the land precipitation inversion result accuracy rate that the method obtains is higher, it can be as a kind of reference of the land precipitation inverting business algorithm of FY-3C/MWHS-II, to lay a good foundation for researchs such as subsequent numerical weather forecast, Data Assimilations.
Description
Technical field
The present invention relates to microwave remote sensing precipitation fields, in particular to a kind of Global Land Surface Precipitation inversion method, this hair
Bright method can be used for No. three 03 star novel microwave hygrometers (FY-3C/MWHS-II) of wind and cloud, be suitable for latitude in 50 ° of S-50 ° of N models
Enclose interior Global land region.
Background technique
Precipitation is the important parameter of synoptic analysis, weather forecast and climate change research etc..It is in global energy and water
It is played an important role in cyclic process, there is especially important meaning to climate change research.But the time of precipitation and sky
Between change very big, be one of the climatic factor for being most difficult to survey.Currently, there are mainly three types of the means of measurement land precipitation：Ground areal rainfall
Meter measurement, ground-based radar remote sensing and satellite remote sensing.Although ground rainfall gauge measurement is the benchmark of other detection means, at that time
Between and the resolution ratio in space be far from satisfying the demand of atmospheric science research, and ground-based radar remote sensing is equally faced with space and divides
The problem of resolution, while radar-derived precipitation is by terrain shading, the probabilistic influence of radar ray lifting and Z-R relationship, position
The fixed and very high unfavorable factor of cost is set, is difficult to carry out in complex region.Space remote sensing is due to wide, the time with space covering
With the advantages such as spatial resolution height, quickly grow.Compared with infrared, microwave can penetrate sexual intercourse etc., therefore satellite-borne microwave remote sensing
With unique advantage.
China's second generation polar orbiting meteorological satellite --- 03 star (FY-3C) of " wind and cloud three " meteorological satellite was in September 23 in 2013
Day successful launch.Daily around 14 circle of earth south poles flight, the time of one circle of flight is 102 points for " wind and cloud three " earth observation
Clock, satellite altitude 836km.The important load novel microwave hygrometer (MWHS-II) of one thereon is used as FY-3A and FY-3B gas
As the renewal product on satellite, the main detection frequency point 183.31-GHz of original atmosphere vapour is remained, which is extended to 5
A detection channels have different responses to atmosphere different height layer steam vertical features, the channel energy positioned at absorption band of water vapor center
The Water Vapor Distribution information of about 30,000 pa of atmospheric sounding upper layer, is gradually distance from the channel that pterion is shifted at Absorption Line center, penetration depth
Gradually reinforce, it can be with the Water Vapor Distribution information of atmospheric sounding middle layer 400,500 and 70,000 pas and 85,000 pa of bottom；Simultaneously
Window area channel is set as 89-GHz and 150-GHz, for detecting surface microwave radiation information；It is meteorological for SSO (Sun Synchronous Orbit) for the first time in the world
The 118-GHz of satellite is 8 channels as oxygen absorption frequency point setting, can be used for the atmospheric temperature detecting of vertical height, thereafter 4
A channel (i.e. channel 6-9) can receive the precipitation information of lower layer in troposphere, logical with 5 water vapor detectings of 183.31-GHz
Road combines, detection while realizing humidity and temperature.Parameter situation such as table 1 is arranged in 15 channels of FY-3C/MWHS-II.
Novel microwave hygrometer can not only penetrate cloud layer and rain belt, and can penetrate one as a kind of passively microwave remote sensor
The earth's surface of depthkeeping degree or vegetation, for round-the-clock, round-the-clock detection global seismic and the humidity and temperature of different height layer, steam
The weather informations such as content, precipitation have the ability of detection precipitation, provide atmospheric humidity promptly and accurately for numerical weather forecast
Initial fields information promotes the monitoring and warning ability to the disastrous strong convective weather such as Heavy Rain of Typhoon, sees in Atmospheric Survey and land
Play a significant role in survey.
Parameter is arranged in 1 channel FY-3C/MWHS-II of table
From the perspective of FY-3C/MWHS-II hardware design, level has reached international most advanced level, but from data application
From the perspective of, compared with external similar load AMSU, ATMS etc., data user rate is lower, based on the complete of FY-3C/MWHS-II
Precipitation inverting research in ball land is of great significance.1 grade of data of FY-3C/MWHS-II mainly have extra large land identification code, bright temperature, instrument
The information such as device view zenith angle, geographical location and time.The FY-3C/MWHS-II quality of data is higher, in April, 2016 by Europe
ECMWF assimilation in continent enters business assimilation system, plays positive-effect to global numerical weather forecast.
Traditional land precipitation inversion method detects earth's surface information using low-frequency band, but the earth's surface emissivity on land
Higher, microwave signal is easy to be submerged in intense radiation background, so that the foundation of precipitation inversion algorithm is not accurate enough, leads to inverting
Precision is poor.
118-GHz the and 183-GHz frequency point being arranged on FY-3C/MWHS-II is used for the absorbing path of Atmospheric Survey, with window
Area compares in channel, and absorbing path retrieving precipitation has a two o'clock advantage, and one is absorbing path unlike window area channel is so transparent, therefore
To underlying surface relative insensitivity, this brings new thinking for the precipitation inverting of land area, and the Computed Tomography for Air of absorbing path is visited
Survey ability is also water-setting object profile surveying tape to wish；Compared with window area channel retrieving precipitation, the absorption for Atmospheric Survey is logical
The result of road retrieving precipitation is more stable, and precision is higher, and especially in land area, absorbing path has the weak precipitation in land and snowfall latent
Detectivity.Window area channel is combined with absorbing path, the comprehensive inversion of precipitation is realized, becomes the one of microwave precipitation remote sensing
A developing direction.TRMM (Tropical Rainfall Measuring Mission) more satellite precipitation data TMPA (TRMM
Precipitation Analysis) it experienced repeatedly upgrading (V5-V7) at nearly 10 years, due to having merged multiple satellite datas,
And the quality of data is higher, by Successful utilization in research fields such as precipitation, the hydrology, weathers.TMPA 3B42 (V7) data are due to it
Higher precision becomes the research Precipitation Products that widely applied one kind is important in the world.
Summary of the invention
It is an object of the invention to overcome drawbacks described above existing for current land precipitation method, it is based on FY-3C/MWHS-II
All channels be all distributed in high frequency band, solid water condensate and big water droplet there is strong scattering to make high-frequency microwave in cloud
With can preferably inverting land precipitation using this principle.Thus provide a kind of FY-3C/MWHS-II data and TMPA
The method that 3B42 data combine inverting Global Land Surface Precipitation, the precipitation inversion method are suitable for latitude in 50 ° of S-50 ° of N ranges
Interior Global land region.
In order to achieve the above-mentioned object of the invention, the present invention provides a kind of Global Land Surface Precipitation inversion method, the method packets
It includes：
Step 1) matches 1 grade of land data and TMPA 3B42 data, obtains matched data collection；Calculating three is bright
Thus the temperature difference determines the type of the convection intensity of cloud system, use different precipitation inverse models to different types, obtain land
Ground rate of rainall；
Step 2) determines whether precipitation according to convection intensity, obtains final land rainfall in conjunction with the rate of rainall of step 1)
Rate.
As a kind of improvement of the above method, the step 1) is specifically included：
Step 1-1) 1 grade of the land data of satellite load are selected and read according to extra large land identification code, extract 15 therein
Channels Brightness Temperature, geographical location and temporal information；
Step 1-2) extract TMPA 3B42 data in precipitation, geographical location and temporal information；
Step 1-3) by the data and step 1-2 of step 1-1)) data of data is carried out according to land matching rule
The matched data collection matched and met the requirements；
Step 1-4) according to Channels Brightness Temperature three bright temperature difference of calculating of the obtained matched data concentration of step 1-3)：
Δ1=TB183±1-TB183±7 (3)
Δ2=TB183±3-TB183±7 (4)
Δ3=TB183±1-TB183±3 (5)
Wherein, Δ1For the first bright temperature difference, Δ2For the second bright temperature difference, Δ3For the third amount temperature difference, TB183±1、TB183±3With
TB183±7The brightness temperature of respectively 183 ± 1-GHz, 183 ± 3-GHz and tri- channels 183 ± 7-GHz；
Step 1-5) determine cloud system convection intensity type：
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=1 is determined as weak convection current, and CI=2 is determined as medium-sized convection current, and CI=3 is determined as strong convection；
Step 1-6) CI=1, CI=2 and CI=3 weather condition determined in step 1-5) used BP neural network respectively
Model 1, BP neural network model 2 and linear regression model (LRM) 3 carry out the inverting of precipitation；Meanwhile other non-classified weather feelings
Condition carries out the inverting of precipitation with BP neural network model 4；Obtain land rate of rainall.
As a kind of improvement of the above method, the step 1-3) land matching rule it is as follows：
(a) unreasonable data are removed using bright temperature extremum method, chooses the bright temperature between 50K~400K；
(b) undesirable data are removed using precipitation extremum method, chooses the drop between 0mm/hr~100mm/hr
Water；
(c) selection matched data surrounding time difference is no more than the data of 30min, i.e. temporal resolution is set as 30min；
(d) data of the selection latitude within the scope of 50 ° of S~50 ° N；
(e) selection matched data front and back longitude and difference of latitude are no more than 0.25 ° of data.
As a kind of improvement of the above method, the BP neural network model 1 uses 15 of FY-3C/MWHS-II
BP neural network precipitation inversion algorithm under the CI=1 weather condition that the bright temperature of full tunnel is established；The BP neural network model 2 makes
Be FY-3C/MWHS-II the CI=2 weather condition established of the bright temperature of 15 full tunnels under BP neural network precipitation inverting
Algorithm；The all weather conditions that the BP neural network model 4 uses the bright temperature of 15 full tunnels of FY-3C/MWHS-II to establish
Under BP neural network precipitation inversion algorithm, the linear regression model (LRM) 3 uses 15 full tunnels of FY-3C/MWHS-II
Linear regression precipitation inversion algorithm under the CI=3 weather condition that bright temperature is established.
As a kind of improvement of the above method, the step 2) is specifically included：
Step 2-1) weather condition of CI=2 and CI=3 is determined as the larger situation of precipitation event probability of happening, it is remaining
Situation is determined as the lesser situation of precipitation event probability of happening；
Step 2-2) precipitation rate that inverting in step 1-6) is obtained is general according to determining that precipitation event occurs in step 2-1)
The precipitation event less than 0.25mm/hr in the smaller situation of rate is set as 0mm/hr；
Step 2-3) rate of rainall less than 0mm/hr that inverting in step 1-6) is obtained is set as 0mm/hr.
The advantage of the invention is that：
1, Global Land Surface Precipitation inversion method of the invention can effectively differentiate and drop whether the generation of precipitation event with inverting
Rain rate realizes Global Land Surface Precipitation inverting, effectively increases FY-3C/MWHS-II data user rate, the land that the method obtains
Ground precipitation inversion result accuracy rate is higher, can be as a seed ginseng of the land precipitation inverting business algorithm of FY-3C/MWHS-II
It examines, to lay a good foundation for researchs such as subsequent numerical weather forecast, Data Assimilations；
2, a kind of Global Land Surface Precipitation inversion method provided by the invention, improves the utilization of FY-3C/MWHS-II data
Rate, land precipitation inversion result correlation reach 0.74, demonstrate method of the invention application value with higher.
Detailed description of the invention
Fig. 1 is Global Land Surface Precipitation inversion method overall flow schematic diagram of the invention；
Fig. 2 is the flow diagram of inversion algorithm in Global Land Surface Precipitation inversion method of the invention.
Specific embodiment
With reference to the accompanying drawing with specific embodiment to a kind of Global Land Surface Precipitation inversion method of the invention carry out it is clear,
Complete description, is described in further detail.
Fig. 1 is Global Land Surface Precipitation inversion method overall flow schematic diagram of the invention.As shown in Figure 1, the inversion method
First the land data extracted from FY-3C/MWHS-II and TMPA 3B42 (V7) data according to land matching rule into
Row time and matching spatially；Then it according to the detection feature of FY-3C/MWHS-II water vapor absorption channel 183-GHz, proposes
The calculation method of the convection intensity Convection Intensity (CI) of cloud system judges precipitation thing according to convection intensity (CI)
The possibility that part occurs, and select different inverse model algorithms to carry out precipitation inverting according to the different type of convection intensity (CI)；
Finally, perfect again in conjunction with the result that the possibility that precipitation event the occurs inverse model algorithm different with selection carries out precipitation inverting
Precipitation inversion result obtains final Global land rate of rainall (mm/hr), completes the Global land based on FY-3C/MWHS-II
Rate of rainall inverting.
Fig. 2 is the flow diagram of inversion algorithm in novel Global Land Surface Precipitation inversion method of the invention.Such as Fig. 2 institute
Show, detailed step is as follows：
Step 1) selects according to the extra large land identification code in FY-3C/MWHS-II data and reads 1 grade of the land of satellite load
Data extract the information such as 15 Channels Brightness Temperatures, geographical location and time therein.
Step 2) extracts the information such as precipitation, geographical location and the time in TMPA 3B42 data.
Step 3) carries out the matching of data according to the land matching rule of FY-3C/MWHS-II and TMPA 3B42 and obtains
The matched data collection met the requirements, specific matching rule are as follows：
(a) unreasonable data are removed using bright temperature extremum method, chooses the bright temperature between 50K~400K；
(b) undesirable data are removed using precipitation extremum method, chooses the drop between 0mm/hr~100mm/hr
Water；
(c) selection matched data surrounding time difference is no more than the data of 30min, i.e. temporal resolution is set as 30min；
(d) data of the selection latitude within the scope of 50 ° of S~50 ° N；
(e) selection matched data front and back longitude and difference of latitude are no more than 0.25 ° of data, i.e. spatial resolution is set as
0.25°。
The Channels Brightness Temperature that the matched data that step 4) obtains step 3) is concentrated calculates three bright temperature according to formula (3)-(5)
Difference is as follows：
Δ1=TB183±1-TB183±7 (3)
Δ2=TB183±3-TB183±7 (4)
Δ3=TB183±1-TB183±3 (5)
Wherein, Δ1For the first bright temperature difference, Δ2For the second bright temperature difference, Δ3For the third amount temperature difference, TB183±1、TB183±3With
TB183±7The respectively brightness temperature of 183 ± 1-GHz of FY-3C/MWHS-II, 183 ± 3-GHz and tri- channels 183 ± 7-GHz
Degree；
Step 5) determines the type of the convection intensity of cloud system according to formula (6)-(8), and wherein CI=1 is determined as weak right
Stream, CI=2 and CI=3 are determined as medium-sized convection current 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)
Step 6) is judged to the CI=2 and CI=3 (medium-sized convection current and strong convection) weather condition that determine in step 5) to drop
The larger situation of water event occurrence rate, remaining situation are determined as the lesser situation of precipitation event probability of happening.
Step 7) is CI=1 (weak convection current), CI=2 (medium-sized convection current) and CI=3 (strong convection) day determined in step 5)
Gas situation carries out the inverting of precipitation with BP neural network model 1, BP neural network model 2 and linear regression model (LRM) 3 respectively；Together
When, other non-classified weather conditions are carried out the inverting of precipitation with BP neural network model 4, obtains rate of rainall.
Wherein, BP neural network model 1 and model 2 use 15 bright temperature of full tunnel of FY-3C/MWHS-II respectively
BP neural network precipitation inversion algorithm under the CI=1 (weak convection current) and CI=2 (medium-sized convection current) weather condition of foundation, BP nerve
BP neural network under all weather conditions that network model 4 uses the bright temperature of 15 full tunnels of FY-3C/MWHS-II to establish
Precipitation inversion algorithm, and the CI=3 that linear regression model (LRM) 3 uses the bright temperature of 15 full tunnels of FY-3C/MWHS-II to establish
Linear regression precipitation inversion algorithm under (strong convection) weather condition.
The rate of rainall of inverting in step 7) is determined the smaller situation of precipitation event probability of happening according to step 6) is middle by step 8)
In the precipitation event less than 0.25mm/hr be set as 0mm/hr, improve precipitation inversion result.
The rate of rainall for being less than 0mm/hr obtained in previous step, that is, step 7) is set as 0mm/hr by step 9), perfect again
Precipitation inversion result obtains final Global land rate of rainall (mm/hr).
It should be noted last that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting.Although ginseng
It is described the invention in detail according to embodiment, those skilled in the art should understand that, to technical side of the invention
Case is modified or replaced equivalently, and without departure from the spirit and scope of technical solution of the present invention, should all be covered in the present invention
Scope of the claims in.
Claims (5)
1. a kind of Global Land Surface Precipitation inversion method, the method includes：
Step 1) matches 1 grade of land data and the land TMPA 3B42 data, obtains matched data collection；Calculating three is bright
Thus the temperature difference determines the type of the convection intensity of cloud system, use different precipitation inverse models to different types, obtain land
Ground rate of rainall；
Step 2) determines whether precipitation according to convection intensity, obtains final land rate of rainall in conjunction with the rate of rainall of step 1).
2. Global Land Surface Precipitation inversion method according to claim 1, which is characterized in that the step 1) specifically includes：
Step 1-1) 1 grade of the land data of satellite load are selected and read according to extra large land identification code, extract 15 channels therein
Bright temperature, geographical location and temporal information；
Step 1-2) extract TMPA 3B42 data in precipitation, geographical location and temporal information；
Step 1-3) by the data and step 1-2 of step 1-1)) data matching for data is carried out according to land matching rule
To the matched data collection met the requirements；
Step 1-4) according to Channels Brightness Temperature three bright temperature difference of calculating of the obtained matched data concentration of step 1-3)：
Δ1=TB183±1-TB183±7 (3)
Δ2=TB183±3-TB183±7 (4)
Δ3=TB183±1-TB183±3 (5)
Wherein, Δ1For the first bright temperature difference, Δ2For the second bright temperature difference, Δ3For the third amount temperature difference, TB183±1、TB183±3And TB183±7
The brightness temperature of respectively 183 ± 1-GHz, 183 ± 3-GHz and tri- channels 183 ± 7-GHz；
Step 1-5) determine cloud system convection intensity type：
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=1 is determined as weak convection current, and CI=2 is determined as medium-sized convection current, and CI=3 is determined as strong convection；
Step 1-6) CI=1, CI=2 and CI=3 weather condition determined in step 1-5) used BP neural network model respectively
1, BP neural network model 2 and linear regression model (LRM) 3 carry out the inverting of precipitation；Meanwhile other non-classified weather conditions are used
The inverting of the progress precipitation of BP neural network model 4；Obtain land rate of rainall.
3. Global Land Surface Precipitation inversion method according to claim 2, which is characterized in that the step 1-3) land
It is as follows with rule：
(a) unreasonable data are removed using bright temperature extremum method, chooses the bright temperature between 50K~400K；
(b) undesirable data are removed using precipitation extremum method, chooses the precipitation between 0mm/hr~100mm/hr；
(c) selection matched data surrounding time difference is no more than the data of 30min, i.e. temporal resolution is set as 30min；
(d) data of the selection latitude within the scope of 50 ° of S~50 ° N；
(e) selection matched data front and back longitude and difference of latitude are no more than 0.25 ° of data.
4. Global Land Surface Precipitation inversion method according to claim 2, which is characterized in that the BP neural network model 1
BP neural network precipitation under the CI=1 weather condition for using the bright temperature of 15 full tunnels of FY-3C/MWHS-II to establish is anti-
Algorithm；The BP neural network model 2 uses CI=2 days that the bright temperature of 15 full tunnels of FY-3C/MWHS-II is established
BP neural network precipitation inversion algorithm in the case of gas；The BP neural network model 4 uses the 15 of FY-3C/MWHS-II
BP neural network precipitation inversion algorithm under all weather conditions that a bright temperature of full tunnel is established, the linear regression model (LRM) 3 use
Be FY-3C/MWHS-II the CI=3 weather condition established of the bright temperature of 15 full tunnels under linear regression precipitation inversion algorithm.
5. Global Land Surface Precipitation inversion method according to claim 1, which is characterized in that the step 2) specifically includes：
Step 2-1) weather condition of CI=2 and CI=3 is determined as the larger situation of precipitation event probability of happening, remaining situation
It is determined as the lesser situation of precipitation event probability of happening；
Step 2-2) precipitation rate that inverting in step 1-6) is obtained according to determine in step 2-1) precipitation event probability of happening compared with
The precipitation event less than 0.25mm/hr in small situation is set as 0mm/hr；
Step 2-3) rate of rainall less than 0mm/hr that inverting in step 1-6) is obtained is set as 0mm/hr.
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| CN112462369A (en) * | 2020-10-30 | 2021-03-09 | 国家卫星气象中心(国家空间天气监测预警中心) | Method and device for microwave imager to invert sea atmosphere rainfall |
| CN112766531A (en) * | 2019-11-06 | 2021-05-07 | 中国科学院国家空间科学中心 | Runoff prediction system and method based on satellite microwave observation data |
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| CN115166871A (en) * | 2022-05-09 | 2022-10-11 | 北京信息科技大学 | Microwave imager rainfall inversion method based on hybrid neural network |
| CN115267786A (en) * | 2022-07-29 | 2022-11-01 | 北京彩彻区明科技有限公司 | Resunet-GAN global radar inversion method and device fusing satellite observation brightness and elevation |
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