CN108415101A - The rarefaction method of second grade Sounding Data - Google Patents
The rarefaction method of second grade Sounding Data Download PDFInfo
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Abstract
The present invention discloses the rarefaction method of second grade Sounding Data, includes the following steps：Step P1：Extraction sounding provides layer data from second grade sounding profile；Step P2：The warm and humid characteristic layer data of sounding is extracted from second grade sounding profile；Step P3：Troposphere top is chosen in selected humiture significant level；Step P4：Sounding wind characteristic layer data is extracted from second grade sounding profile；Step P5：Arrangement integration is carried out to the level of extraction.The present invention may be implemented from second grade Sounding Data, the notable inflection point of the Vertical Profile of the meteorological elements such as Extracting temperature, humidity, wind direction, wind speed, and by being integrated with important sounding levels such as each regulation isobaris surface layer, troposphere tops, obtain about 100 layers of complete sounding profile.The number of plies dramatically increases obtained by the more existing other methods of the profile number of plies, and the more acurrate fine structure feature for describing each meteorological element vertical change of energy meets the application demands such as Numerical Prediction Models, Data Assimilation.
Description
Technical field
The present invention relates to Sounding Datas to handle application process, more particularly to a kind of rarefaction side based on second grade Sounding Data
Method.
Background technology
Conventional raob data because its stable quality is reliable, the vertical number of plies is more, can accurate description air three-dimensional structure,
And as the offer extremely important information source of land atmospheric condition.This makes Sounding Data in numerical forecast, synoptic analysis, gas
It waits and is played an important role in the researchs such as variation, satellite data calibration, especially carry out Data Assimilation mistake in Numerical Prediction System
Cheng Zhong, Sounding Data are even more as the initial field quality of improved mode, improve indispensable most important basic of forecast precision
Data.
With the development of sounding technology, China has been completed from 2011 from 59-701 types sounding system to L-band electronics
Comprehensive update of sonde system, sounding system of new generation can provide the sounding number of second rank, high vertical resolution
According to.On the other hand, Numerical Weather is simulated, and Data Assimilation and numerical weather forecast may benefit from obtaining for high-resolution sounding data
It takes and applies, raob system is closeer, it is bigger to the contribution of analytical precision.However, due to the limitation of computing capability, mesh
Preceding meteorological numerical model can not be by thousands of layers of second grade sounding data whole assimilation application, in order to adapt to numerical forecast
The Data Assimilation ability of pattern just must carry out LS-SVM sparseness to second grade Sounding Data.It is chosen by certain technical method
Specific sounding level replaces thousands of layers of second grade sounding profile.It can be seen that rarefaction method is high-resolution Sounding Data in number
Very important sport technique segment in the application processes such as value forecast, pattern assimilation.
Currently, by integrate the obtained sounding specified layer of meteorological station current operation software and significant level to obtain the number of plies less
Sounding profile be a kind of common method.But the characteristic number of layers of this method extraction is on the low side, obtained sounding profile is not
The vertical change rule that meteorological element can precisely be portrayed has been difficult to meet current value pattern, the demand for development of Data Assimilation.
Another method is the autothinning using second grade Sounding Data on a timeline, such as utilizes the lower half point of temporal resolution
Clock, whole minute sounding data, but this method does not ensure that all precisely changes of meteorological element Vertical Profile of selected moment
Change inflection point, also cannot accurately parse the detailed features of meteorological element vertical change.Therefore, it is necessary to develop it is a kind of it is objective,
Reliable rarefaction method extracts the suitable sounding profile of the number of plies from second grade Sounding Data, both can guarantee numerical model to visiting
The assimilation application demand of empty data, and can precisely portray the physics law of meteorological element vertical change.
To solve the above problems, the present invention propose it is a set of for Chinese sounding L-band second grade data, adaptive sparse
Change technical solution, the program may be implemented from second grade Sounding Data, the meteorological elements such as Extracting temperature, humidity, wind direction, wind speed
The notable inflection point of Vertical Profile, and the important sounding levels such as each regulation isobaris surface layer, troposphere top are integrated, to be formed about
100 layers of complete sounding profile.The number of plies of gained profile is on close level with European and American developed countries, and more acurrate can be described each
The fine structure feature of a meteorological element vertical change.Observation based on mode context field (ERA-Interim again analysis of data)
Material deviation assessment technology, comparative analysis distinct methods obtain the data volume of meteorological element on sounding profile, deviation (Bias) and
The quantitative targets such as root-mean-square error (RMSE) have carried out the inspection of system to the effect of the rarefaction scheme.
Invention content
For the deficiency in art methods, it is an object of the invention to propose it is a set of for second grade Sounding Data,
Adaptive rarefaction technical method, this method may be implemented from second grade Sounding Data, Extracting temperature, humidity, wind direction, wind speed
Etc. the Vertical Profile of meteorological elements notable inflection point, and by with the important sounding level such as each regulation isobaris surface layer, troposphere top
It is integrated, obtains about 100 layers of complete sounding profile.The number of plies is notable obtained by the more existing other methods of the profile number of plies
Increase, the more acurrate fine structure feature for describing each meteorological element vertical change of energy meets Numerical Prediction Models, Data Assimilation
Etc. application demands.
In order to achieve the above objectives, the present invention uses following technical proposals：
The rarefaction method of second grade Sounding Data, includes the following steps：
Step P1：Extraction sounding provides layer data from second grade sounding profile；
Step P2：The warm and humid characteristic layer data of sounding is extracted from second grade sounding profile；
Step P3：Troposphere top is chosen in selected humiture significant level；
Step P4：Sounding wind characteristic layer data is extracted from second grade sounding profile；
Step P5：Arrangement integration is carried out to the level of extraction.
The rarefaction method of above-mentioned second grade Sounding Data, in step P1：If directly including regulation in second grade sounding profile
Layer, i.e., atmospheric pressure value be 1000hPa, 925hPa, 850hPa, 700hPa, 600hPa, 500hPa, 400hPa, 300hPa, 250hPa,
The layer of 200hPa, 150hPa, 100hPa, 70hPa, 50hPa, 40hPa, 30hPa, 20hPa, 15hPa, 10hPa, 7hPa and 5hPa
It is secondary, then it is directly extracted from second grade Sounding Data.
The rarefaction method of above-mentioned second grade Sounding Data, in step P1：For the specified layer that does not include in second grade profile,
Progress logarithm interpolation calculation is then needed to obtain the meteorological element value in relevant specified layer；For the specified layer on sounding profile
Arbitrary sounding meteorological element YxFor, sounding level meteorological element closed on up and down based on the specified layer, existing is respectively YaWith
Yb, calculate specified layer meteorological element YxInterpolation algorithm it is as follows：
Wherein, P is the atmospheric pressure value of the specified layer, PaAnd PbIt is the atmospheric pressure value that the specified layer closes on level up and down respectively.
The rarefaction method of above-mentioned second grade Sounding Data specifically comprises the following steps in step P2：
Step P201：Select ground floor, i.e. sounding data the 0th second, as first warm and humid significant level L1；
Step P202：With T0Represent the 0th second temperature value, TnRepresent n-th second temperature value, n=1,2,3 ...；GH0Generation
The 0th second geopotential unit value of table, GHnRepresent n-th second geopotential unit value, n=1,2,3 ...；With (Tn-T0)/(GHn-GH0) be
Slope connects 2 points of (T0, GH0) and (Tn, GHn) auxiliary straight line is obtained, which simulates the temperature of point-to-point transmission with position
Gesture height changes linearly, then by the offset of second calculating actual temperature profile to this auxiliary straight line；
Step P203：For humidity characteristic layer, by the T in step P2020It is changed to RH0、TnIt is changed to RHn, repeat step
Way in P202 obtains the humidity offset by the second；When above-mentioned temperature or humidity offset are respectively greater than respective threshold value,
It is second humiture significant level L2 to select the layer；
Step P204：Then using L2 as basic point, repeat the above steps P202 and P203, continues up and selects each spy successively
Property layer L3, L4 ..., Lx, x=1,2,3 ....
The rarefaction method of above-mentioned second grade Sounding Data includes the following steps in step P3：
Step P301：The selection of the first tropopause needs in the temperature characterisitic layer extracted in step P2, and setting is necessary
Condition obtain：First, the height of the first tropopause should be between 500hPa to 150hPa；Secondly, it is with 500hPa
Starting point gradually first selectes a temperature characterisitic layer upwards, and first assumes that the selected characteristics layer is the first tropopause, geopotential unit
For GH1, temperature T1；Then the arbitrary temp significant level in 2km altitude ranges, the potential of this temperature characterisitic layer above this layer are seen
Height is GHx, temperature TxIf temperature rate of change (the T that all temperature characterisitic layers providex-T1)/(GHx-GH1) numerical value
Size is all higher than -2, then it is the first tropopause to select the layer；Otherwise, on the basis of the temperature characterisitic layer selected in front, continue
Another temperature characterisitic layer is selected upwards, it is assumed that it is the first tropopause, continues whether criterion meets above-mentioned condition, until final
Select the first tropopause；
Step P302：The selection on the second troposphere top needs in the temperature characterisitic layer extracted in step P2, and setting is necessary
Condition obtain：First, the height on the second troposphere top is between 150hPa to 40hPa；Secondly, selected by step P301
The first tropopause is starting point, gradually first selectes a temperature characterisitic layer upwards, it is assumed that its geopotential unit is GH2, temperature T2；
Then see that the arbitrary temp significant level in 1.5km altitude ranges above this layer, the temperature characterisitic layer geopotential unit are GHy1, temperature
For Ty1If temperature rate of change (the T that all temperature characterisitic layers providey1-T2)/(GHy1-GH2) numerical values recited be respectively less than -3,
First assume that maximum layer significant level within the scope of the 1.5km is the second troposphere top, and above this layer in 2km altitude ranges
The geopotential unit of arbitrary temp significant level, the temperature characterisitic layer is GHy2, temperature Ty2, it is satisfied by temperature rate of change (Ty2-
T2)/(GHy2-GH2) numerical values recited be all higher than -1, then select the layer be the second troposphere top；Otherwise, it is above-mentioned it is selected most
On the basis of high one layer of temperature characterisitic layer, another temperature characterisitic layer of selection is continued up, it is assumed that it is the second troposphere top, and criterion is
It is no to meet above-mentioned condition, until finally selecting the second troposphere top.
The rarefaction method of above-mentioned second grade Sounding Data includes the following steps in step P4：
Step P401：Such as step P2, ground floor, i.e. sounding data the 0th second are equally selected first, as first wind spy
Property layer M1；
Step P402：With W0Represent the 0th second air speed value, WnRepresent n-th second air speed value, n=1,2,3 ...；GH0Generation
The 0th second geopotential unit value of table, GHnRepresent n-th second geopotential unit value, n=1,2,3 ...；With (Wn-W0)/(GHn-GH0) be
Slope connects 2 points of (W0, GH0) and (Wn, GHn) auxiliary straight line is obtained, which simulates the wind speed size of point-to-point transmission with position
Gesture height changes linearly, then by the offset of second calculating actual wind speed profile to this auxiliary straight line；
Step P403：For wind direction, by the W in step P4020It is changed to wind direction WD0、WnIt is changed to wind direction WDn, repeat to walk
The way of rapid P402, obtains the wind direction offset by the second；When above-mentioned wind speed or wind direction offset are respectively greater than respective threshold value,
It is second wind characteristic layer M2 to select the layer；
Step P404：Using M2 as basic point, repeat the above steps P402 and P403, continues up and selects each wind characteristic successively
Layer M3, M4 ..., Mx, x=1,2,3 ....
The rarefaction method of above-mentioned second grade Sounding Data, in step P5：The specified layer that will be obtained in step P1, step P2
In obtained humiture significant level, the wind characteristic layer in step P4 is ranked sequentially, high according to the potential of each sounding level
The size of degree, is arranged in order from low to high, obtains one completely based on the sounding exterior feature after second grade sounding profile rarefaction
Line；The profile can capture the notable inflection point of the Vertical Profile of the meteorological elements such as temperature, humidity, wind direction, wind speed.
Beneficial effects of the present invention are as follows：
1. this method significantly increases the characteristic number of layers of the meteorological elements such as temperature, humidity, wind direction, wind speed, can be more smart
The fine structure of meteorological element vertical change is described accurately.
2. this method assimilates the important technical links in application process as second grade Sounding Data towards numerical model, improve
The overall applicability benefit of second grade Sounding Data.
3. this method provides necessary technical support for the recruitment evaluation inspection of second grade Sounding Data quality control, pass through
After carrying out rarefaction to the second grade Sounding Data before and after quality control, compared and analyzed with existing homogeneous data source, favorably
In the mass of system problem for finding and improving second grade Sounding Data.
Description of the drawings
Specific embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings.
The comparison diagram for the temperature Vertical Profile that Fig. 1 is obtained using two methods (to indicate clear, put down in figure by curve
It moves)；
Two methods of Fig. 2 obtain the spatial distribution of average single profile medium temperature moisture performance number of layers：Fig. 2 a have significant level
Data set；Fig. 2 b rarefactions of the present invention；
Fig. 3 rarefaction schemes of the present invention obtain average single profile medium temperature moisture performance number of layers in the space of Various Seasonal point
Cloth：Fig. 3 a spring；Fig. 3 b summers；Fig. 3 c autumns；Fig. 3 d winters；
Assessment result of the two kinds of data sources of Fig. 4 with respect to ERA-Interim analysiss of data again：Fig. 4 a data volumes, Fig. 4 b deviations
(Bias), Fig. 4 c root-mean-square errors (RMSE)；
Tri- kinds of data sources of Fig. 5 (integrated data sources CMA-Merge, rarefaction data source CMA-Thin, CFSR) relative to
The vertical distribution figure of ERA-Interim assessment results of analysis of data again：Fig. 5 a data volumes, Fig. 5 b deviations (Bias), Fig. 5 c are equal
Square error (RMSE)；
Rarefaction method flow diagram of Fig. 6 present invention for second grade Sounding Data；
Significant level extraction algorithm schematic diagrames of the Fig. 7 based on second grade sounding profile；
The rarefaction method of Fig. 8 present invention rarefaction effect contrast figure under different threshold parameters：Fig. 8 a present invention's is dilute
Thinization method observes comparison diagram under different threshold parameters with grade of original second；Fig. 8 b are the partial enlarged view of Fig. 8 a.
Specific implementation mode
In order to illustrate more clearly of the present invention, the present invention is done further with reference to preferred embodiments and drawings
It is bright.Similar component is indicated with identical reference numeral in attached drawing.It will be appreciated by those skilled in the art that institute is specific below
The content of description is illustrative and be not restrictive, and should not be limited the scope of the invention with this.
1 data and method
1.1 research data
The Chinese high-altitude L-band that the L-band second grade sounding data that the present invention uses is issued from China Meteorological Administration
Second grade observation basic data collection (V1.0), the data pass through stringent quality control, as permissible value range check, stiff value check,
Consistency check etc. between vertical consistency check, the inspection of climatic border value, filtering inspection, monotonicity inspection, element.Due to the second
The quality control measure of level data is not belonging to a part for technical solution of the present invention, and the quality control measure of second level data is
The prior art, details are not described herein.The rarefaction scheme of the present invention is based on the second grade sounding data after being quality control, to protect
The accuracy of the data information obtained after card rarefaction.
1.2 research method
1.2.1 rarefaction method
As shown in fig. 6, rarefaction method of the present invention for second grade Sounding Data specifically comprises the following steps：
Step P1：Extraction sounding provides layer data from second grade sounding profile
Step P101：If directly including specified layer in second grade sounding profile, that is, provide that (atmospheric pressure value is for isobaris surface layer
1000hPa, 925hPa, 850hPa, 700hPa, 600hPa, 500hPa, 400hPa, 300hPa, 250hPa, 200hPa,
The level of 150hPa, 100hPa, 70hPa, 50hPa, 40hPa, 30hPa, 20hPa, 15hPa, 10hPa, 7hPa, 5hPa), it can be from
It is directly extracted in second grade Sounding Data.
Step P102：For the specified layer not included in second grade profile, then progress logarithm interpolation calculation is needed to obtain correlation
Specified layer on meteorological element value.For the arbitrary sounding meteorological element Y of the specified layer on sounding profilexFor, being based on should
Sounding level meteorological element Y that specified layer is closed on up and down, existingaAnd Yb, calculate specified layer meteorological element YxInterpolation algorithm
It is as follows：
Wherein, P is the atmospheric pressure value of the specified layer, PaAnd PbIt is the atmospheric pressure value that the specified layer closes on level up and down respectively.
Step P2：The warm and humid characteristic layer data of sounding is extracted from second grade sounding profile
Step P201：As shown in fig. 7, three curves respectively represent meteorology different on a complete second grade sounding profile
Element (relative humidity RH, temperature T, air pressure P), it is first warm and humid significant level to select ground floor (sounding data the 0th second) first
L1。
Step P202：With T0Represent the 0th second temperature value, TnRepresent n-th second temperature value (n=1,2,3.....), GH0
Represent the 0th second geopotential unit value, GHnThe geopotential unit value (n=1,2,3.....) for representing n-th second, with ((Tn-T0)/(GHn-
GH0)) it is that slope connects 2 points of (T0, GH0) and (Tn, GHn) obtain an auxiliary straight line, the straight line simulate the temperature of point-to-point transmission with
Geopotential unit changes linearly, and then calculates the offset that actual temperature profile (being usually curve) arrives this auxiliary straight line by the second.
Step P203：For humidity characteristic layer, by the T in step P2020And TnIt is changed to RH0And RHn, repeat above
Way, also obtain the humidity offset by the second；When above-mentioned temperature or humidity offset are respectively greater than respective threshold value, that is, select
The fixed layer is second humiture significant level L2 (in threshold size such as Fig. 7 shown in heavy black line section, in an example of the present invention
In, for the temperature threshold used for 0.75 DEG C, 5%) relative humidity threshold value is.
Step P204：Then using L2 as basic point, repeat the above steps P202 and P203, continues up and selects each spy successively
Property layer L3, L4 ..., Lx etc., x=1,2,3 ...
Step P205：Since meteorological element is very big in different meteorological sounding altitude changing rule difference, the gas of selection
As element threshold value is with height change, this needs repetition test to determine to meet different assimilation application demands.As shown in Figure 8 originally
One application example of invention, with when 51644 stations (Xinjiang, library station) August 00UTC on the 15th in 2015 L-band sounding temperature
It spends for Vertical Profile, grade observation of original second data have 3525 layers, if the temperature threshold being all made of at all meteorological sounding altitudes
0.75 DEG C of characteristic number of layers then obtained is 322 layers, and the temperature threshold used is highly located in 200hPa or less as 0.75 DEG C,
200hPa or more highly locates as 1 DEG C of obtained characteristic number of layers to be 220 layers.It is noted that each meteorological element is specifically adopted
Threshold size needs the vertical number of plies for considering Numerical Prediction Models, and to second grade sounding data assimilation application power, this hair
It is bright to suggest through experiment, according to the practical application effect of threshold parameter, (the feedback in such as flow chart 6 is determined by examining to assess
Improve link).
Step P3：Troposphere top is chosen in selected humiture significant level
Step P301：The selection of the first tropopause needs in the temperature characterisitic layer extracted in step P2, and setting is necessary
Condition obtain：First, the height of the first tropopause should be between 500hPa to 150hPa；Secondly, it is with 500hPa
Starting point gradually first selectes a temperature characterisitic layer upwards, and first assumes that the selected characteristics layer is the first tropopause, geopotential unit
For GH1, temperature T1；Then arbitrary temp significant level (the geopotential unit GH in 2km altitude ranges above this layer is seenx, temperature
For Tx), if the temperature rate of change ((T that all temperature characterisitic layers providex-T1)/(GHx-GH1)) numerical values recited be all higher than-
2, then it is the first tropopause to select the layer.Otherwise, on the basis of the temperature characterisitic layer selected in front, it is another to continue up selection
Temperature characterisitic layer, it is assumed that it is the first tropopause, sees whether above-mentioned criterion meets, until finally selecting the first tropopause.
Step P302：The selection on the second troposphere top needs in the temperature characterisitic layer extracted in step P2, and setting is necessary
Condition obtain：First, the height on the second troposphere top should be between 150hPa to 40hPa；Secondly, with step P301 institutes
The first tropopause of choosing is starting point, gradually first selectes a temperature characterisitic layer upwards, it is assumed that its geopotential unit is GH2, temperature
For T2；Then arbitrary temp significant level (the geopotential unit GH in 1.5km altitude ranges above this layer is seeny1, temperature Ty1), if
The temperature rate of change ((T that all temperature characterisitic layers providey1-T2)/(GHy1-GH2)) numerical values recited be respectively less than -3, it is first false
Maximum layer significant level within the scope of the fixed 1.5km is the second troposphere top, and arbitrary in 2km altitude ranges above this layer
Temperature characterisitic layer (geopotential unit GHy2, temperature Ty2), it is satisfied by temperature rate of change ((Ty2-T2)/(GHy2-GH2)) number
Value size is all higher than -1, then it is the second troposphere top to select the layer.Otherwise, the maximum layer temperature characterisitic layer base selected in front
On plinth, another temperature characterisitic layer of selection is continued up, it is assumed that it is the second troposphere top, sees whether above-mentioned criterion meets, until
Finally select the second troposphere top.
It is noted that in the inventive method, the judgement for the first tropopause and the second troposphere top is most
Complicated, the judgement especially to the second troposphere top.Since the meteorological element value on troposphere top is in numerical model Data Assimilation
Especially important effect is played in the process, needs to provide specific level identities code in thinning processes.Although therefore sentencing
Determine method complexity, the present invention still provides as above based on second grade Sounding Data, detailed judgment basis.Certainly, these judgements
It is specific temperature characterisitic layer to be labeled as troposphere top, and the sounding profile number of plies obtained for entire rarefaction method is that do not have
There is any influence.
Step P4：Sounding wind characteristic layer data is extracted from second grade sounding profile
Step P401：Such as step P2, it is first wind characteristic layer equally to select ground floor (sounding data the 0th second) first
M1。
Step P402：With W0Represent the 0th second air speed value, WnRepresent n-th second air speed value (n=1,2,3 ... ..), GH0Generation
The 0th second geopotential unit value of table, GHnThe geopotential unit value (n=1,2,3 ... ..) for representing n-th second, with ((Wn-W0)/(GHn-
GH0)) it is that slope connects 2 points of (W0, GH0) and (Wn, GHn) auxiliary straight line is obtained, the wind speed which simulates point-to-point transmission is big
It is small to be changed linearly with geopotential unit, then the offset that actual wind speed profile (being usually curve) arrives this auxiliary straight line is calculated by the second
Amount.
Step P403：For wind direction, by the W in step P4020And WnIt is changed to wind direction WD0And WDn, repeat above
Way also obtains the wind direction offset by the second；It is when above-mentioned wind speed or wind direction offset are respectively greater than respective threshold value, i.e., selected
The layer is second wind characteristic layer M2.
Step P404：Then using M2 as basic point, repeat the above steps P402 and P403, continues up and selects each wind successively
Significant level M3, M4 ..., Mx etc., x=1,2,3 ...
Step P405：In an example of the present invention application, for the wind direction threshold value used for 10 °, wind speed threshold value is 10m/
s。
Step P5：Arrangement integration is carried out to the level of extraction according to certain sequence
Step P501：The specified layer that will be obtained in above-mentioned steps P1, the humiture significant level obtained in step P2, step P4
In wind characteristic layer be ranked sequentially, according to the size of the geopotential unit of each sounding level, be arranged in order from low to high
Come, obtains one completely based on the sounding profile after second grade sounding profile rarefaction.The profile can capture temperature, wet
The notable inflection point of the Vertical Profile of the meteorological elements such as degree, wind direction, wind speed is (as shown in Figure 1, it can be seen that with 53068 station (Inner Mongols
Ancient Erlianhaote, GCOS sounding stations) January in 2016 of 00UTC on the 1st when time L-band sounding temperature Vertical Profile for, it is original
Second grade observation data have 4318 layers, and the warm and humid characteristic number of layers that existing station software approach provides is 30 layers, and applies this hair
The warm and humid characteristic number of layers that the rarefaction method based on second grade data of bright design obtains is 118 layers), describe respectively to more acurrate
The fine structure feature of a meteorological element vertical change is needed when sounding temperature applications are during numerical model Data Assimilation
By the temperature value linear interpolation of temperature characterisitic layer to AD HOC layer, it is clear that the temperature profile more than the significant level number of plies is capable of providing
More accurately temperature value.This just preferably meets the application demand of Numerical Prediction Models, Data Assimilation etc..
For ease of the needs of practical application, rarefaction scheme of the present invention, which also achieves, directly to be extracted from second grade sounding data
The meteorological element of each regulation isobaris surface, it is complete to form one together with the significant level Data Integration extracted above
About 100-200 layers of sounding profile.
1.2.2 rarefaction validity check method
In order to examine rarefaction effect, analyzed again by comparing the raob data and ERA-Interim of separate sources
Deviation (Bias) and root-mean-square error (RMSE) between data quantify to examine.Its large deviations Bias is represented in certain period again
The difference of analysis of data average value and sounding field data average value；And root-mean-square error
It is square of quadratic sum and observation frequency ratio that ERA-Interim analyzes data and sounding measured data deviation again
Root.The sounding profile of these separate sources is respectively：Based on " Chinese high-altitude significant level timing value data set (V1.0) " and " China
High-altitude provides isobaris surface timing value data set (V2.1) " integrate sounding profile data (CMA-Merge), based on the present invention propose
The obtained sounding profile data (CMA-Thin) of rarefaction scheme and the research and development of Environmental forecasting centre weather it is pre-
Examining system sounding profile data in analysis of data (CFSR) again.
Although due to being influenced by the Systematic Errors such as Numerical Prediction Models and assimilation scheme, then analysis of data and observation
As a result comparing still to have a certain difference, but they can rationally reflect that East Asia and its China Region Climate become substantially
The spatial-temporal distribution characteristic of change.Therefore, using ERA-Interim, analysis of data is as ambient field again in the present invention, by comparing not
Deviation (Bias) and root-mean-square deviation (RMSE) with data source and ambient field carry out quantitative analysis rarefaction effect.
2 results and analysis
2.1 rarefaction Contrast on effect are analyzed
Single sounding profile rarefaction effect is as shown in Figure 1, with 53068 stations (Erlianhaote, GCOS sounding stations) 2016 1
When moon 00UTC on the 1st for secondary L-band sounding temperature Vertical Profile, it can be seen that original second grade observation data have 4318
Layer, the warm and humid characteristic number of layers that Chinese high-altitude significant level timing value data set (V1.0) provides is 30 layers, and is set using the present invention
The warm and humid characteristic number of layers that the rarefaction method based on second grade data of meter obtains is 118 layers.Three profiles of comparison can be very clear
Find out to Chu, the temperature profile obtained by rarefaction method, more existing 30 layers of temperature profile, by capturing smaller scale
Temperature inflection point, can more accurately describe the vertical change feature of temperature.It is noted that when sounding temperature applications are in numerical value
When during pattern Data Assimilation, need the temperature value linear interpolation of temperature characterisitic layer to AD HOC layer, it is clear that significant level
Temperature profile more than the number of plies is capable of providing more accurately temperature value.
The spatial and temporal distributions of 2.2 humiture characteristic number of layers
In order to further examine rarefaction effect, the present invention to choose 120 the station 00UTC and 12UTC two of China of whole year in 2014
Secondary all second grade sounding profile data are for statistical analysis when a, obtain each website average single sounding profile and extract to obtain
Warm and humid characteristic number of layers, the website number distribution of different value ranges is as shown in table 1.
Table 1. compares the website number distribution table for the warm and humid characteristic number of layers of difference that two kinds of significant level extracting methods obtain
Fig. 2 gives the spatial distribution map of average single profile medium temperature moisture performance number of layers, and wherein Fig. 2 (a) statistics is
Existing significant level data set, Fig. 2 (b) then provide the spatial distribution for the characteristic number of layers that rarefaction of the present invention obtains.First, lead to
It crosses table 1 and Fig. 2 is shown, the warm and humid characteristic number of layers average value that existing significant level data set provides concentrates on 20~30 layers,
And the warm and humid characteristic number of layers average value overwhelming majority obtained based on second data rarefaction significantly increases spy at 60~90 layers
The number of property layer.Secondly, it can be seen that although the significant level number that two methods provide is widely different, Fig. 2 (a) and Fig. 2
(b) Spatial distribution of the characteristic number of layers provided shows good consistency；Such as, the significant level that two methods count
The relatively large number of website of number all integrated distributions are in the lower southeast China of height above sea level.Finally it may be noted that using circle in two figures
7 sounding websites of label, are GCOS sounding stations, and due to discharging big ball, maximum probe height is apparently higher than the non-GCOS in periphery
The sounding station is more than periphery website with the characteristic number of layers that two methods obtain.These all illustrate, proposed by the present invention to be based on
The rarefaction scheme of L-band second grade sounding data all has good universality for national 120 sounding stations.
The vertical change of meteorological element is not only related with the geographical distribution of website, but also equally should table in different seasons
Reveal different changing rules.Table 2 gives spring, summer, autumn, four seasons of winter, the station of different warm and humid characteristic number of layers value ranges
Points distribution.
Table 2. compares Various Seasonal, and rarefaction scheme of the present invention obtains the website number distribution table of different warm and humid characteristic number of layers
As Fig. 3 give rarefaction obtain average single profile medium temperature moisture performance number of layers Various Seasonal spatial distribution
As a result.By chart as can be seen that the annual distribution map provided in the Spatial distribution and Fig. 2 of the characteristic number of layers in four seasons
Substantially it coincide.It is noted that the characteristic number of layers that most of sounding website is extracted in winter-spring season is generally more than the summer
Autumn, this is higher with the temperature of summer and autumn entire atmosphere cause its energy to exchange up and down compared be sufficiently related.
The 2.3 rarefaction validity checks based on ERA-Interim analysiss of data again
First, from time series distribution, Fig. 4 comparisons give two kinds of data sources and are analyzed again with respect to ERA-Interim
The test effect of data, it can be seen that the sounding temperature data amount based on the participation comparison that second grade data rarefaction scheme obtains
Be far longer than by have it is that sounding data collection is integrated as a result, by the temperature deviation of opposite ERA-Interim analysis of data again
(Bias) be on close level with root-mean-square error (RMSE), there is no because data volume increase and increase；And either T numbers
Good consistency is all shown according to amount or T deviations and T root-mean-square errors, the tendency changed over time.
Secondly, from the spatial distribution of inspection result, Fig. 5 comparisons give the temperature of three kinds of data sources with respect to ERA-
The vertical distribution figure of Interim analysis of data inspection results again, wherein Fig. 5 (a) is represented participates in comparison at different meteorological sounding altitudes
The T data volumes of analysis, Fig. 5 (b) are temperature deviation (Bias), and Fig. 5 (c) is temperature root-mean-square error (RMSE).It can be with by 5 (a)
Find out, since Chinese site number is less in CFSR so that the data volume at any meteorological sounding altitude is minimum；And rarefaction obtains
The temperature data amount arrived the data volume that significantly more than data with existing collection is integrated again.It is obtained through rarefaction it can be seen from 5 (b)
Temperature deviation compared with other two data sources all closer to 0 DEG C, and its vertical change amplitude smaller, compared with other two Data source tables
Reveal better vertical consistency.In meteorological sounding altitude 925hPa~500hPa, the temperature obtained through rarefaction it can be seen from 5 (c)
Degree root-mean-square error is on close level with integrated data sources；And sounding low layer 1000hPa~925hPa and high level 500hPa~
100hPa, the temperature root-mean-square error obtained through rarefaction is bigger than normal than integrated data sources, and this may be with participation statistics
Data volume is more related.
In short, either (Fig. 5) is distributed from the time series of inspection result distribution (Fig. 4) or its vertical space, through dilute
The data source that thinization scheme obtains all shows apparent advantage：Its T data volume bigger, T_RMSE are on close level, and T_Bias is more
It is small.It is noted that the rarefaction effect of not only this meteorological element of temperature T shows above-mentioned statistical nature, other
Meteorological element such as humidity Rh, wind speed V etc. can obtain similar conclusion, not repeat herein.
3 conclusions and discussion
The present invention is based on the rarefaction schemes of L-band sounding second grade Data Design, can dramatically increase for assimilating application
Sounding profile characteristic number of layers obtained by doing LS-SVM sparseness to 120 sounding websites all second grade soundings in 2014
The spatial distribution of characteristic number of layers there is good consistency, Er Qieqi with the spatial distribution obtained by significant level data set
Number of plies spatial distribution shows apparent Seasonal variation, this embody the present invention rarefaction scheme feasibility with it is pervasive
Property.
Based on ERA-Interim, analysis of data does ambient field again, and the sounding data after rarefaction is inclined compared with the existing sounding number of plies
The Bias effect of few integral data is more preferable, can by the time series chart and vertical space distribution map of Bias and RMSE
The difference of sounding data is obtained clearly to compare three kinds of methods.With the development especially Mode normal point of Numerical Prediction Models
The raising of resolution will promote the sounding data of more layers to enter assimilation application.Rarefaction scheme proposed by the present invention is high hangs down
Effective assimilation application of straight resolution ratio Sounding Data provides guarantee.
In addition, the rarefaction scheme improves the algorithm of extraction property layer, the significant level number of plies is dramatically increased, it can be preferably
Applied in the researchs such as climate change.It, can be in conjunction with different Numerical Prediction Models, such as GRAPES in next research
Deng, the analysis of example is carried out for specific synoptic process, it, can be with by the sounding data after rarefaction in numerical forecast assimilation
Preferably embody the practical application effect of the rarefaction scheme.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention may be used also on the basis of the above description for those of ordinary skill in the art
To make other variations or changes in different ways, all embodiments can not be exhaustive here, it is every to belong to this hair
Row of the obvious changes or variations that bright technical solution is extended out still in protection scope of the present invention.
Claims (7)
1. the rarefaction method of second grade Sounding Data, which is characterized in that include the following steps：
Step P1：Extraction sounding provides layer data from second grade sounding profile；
Step P2：The warm and humid characteristic layer data of sounding is extracted from second grade sounding profile；
Step P3：Troposphere top is chosen in selected humiture significant level；
Step P4：Sounding wind characteristic layer data is extracted from second grade sounding profile；
Step P5：Arrangement integration is carried out to the level of extraction.
2. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P1：If the second
Grade sounding profile in directly include specified layer, i.e., atmospheric pressure value be 1000hPa, 925hPa, 850hPa, 700hPa, 600hPa,
500hPa、400hPa、300hPa、250hPa、200hPa、150hPa、100hPa、70hPa、50hPa、40hPa、30hPa、
The level of 20hPa, 15hPa, 10hPa, 7hPa and 5hPa are then directly extracted from second grade Sounding Data.
3. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P1：For
The specified layer not included in second grade profile then needs progress logarithm interpolation calculation to obtain the meteorological element in relevant specified layer
Value；For the arbitrary sounding meteorological element Y of the specified layer on sounding profilexFor, it is being closed on up and down based on the specified layer, existing
Sounding level meteorological element be respectively YaAnd Yb, calculate specified layer meteorological element YxInterpolation algorithm it is as follows：
Wherein, P is the atmospheric pressure value of the specified layer, PaAnd PbIt is the atmospheric pressure value that the specified layer closes on level up and down respectively.
4. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P2, specifically
Include the following steps：
Step P201：Select ground floor, i.e. sounding data the 0th second, as first warm and humid significant level L1；
Step P202：With T0Represent the 0th second temperature value, TnRepresent n-th second temperature value, n=1,2,3 ...；GH0Represent the 0th
The geopotential unit value of second, GHnRepresent n-th second geopotential unit value, n=1,2,3 ...；With (Tn-T0)/(GHn-GH0) it is slope
Connect 2 points of (T0, GH0) and (Tn, GHn) auxiliary straight line is obtained, which simulates the temperature of point-to-point transmission with potential height
Degree changes linearly, then by the offset of second calculating actual temperature profile to this auxiliary straight line；
Step P203：For humidity characteristic layer, by the T in step P2020It is changed to RH0、TnIt is changed to RHn, repeat step P202
In way, obtain the humidity offset by the second；When above-mentioned temperature or humidity offset are respectively greater than respective threshold value, that is, select
The fixed layer is second humiture significant level L2；
Step P204：Then using L2 as basic point, repeat the above steps P202 and P203, continues up and selects each significant level successively
L3, L4 ..., Lx, x=1,2,3 ....
5. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P3, including
Following steps：
Step P301：The selection of the first tropopause needs in the temperature characterisitic layer extracted in step P2, sets necessary item
Part obtains：First, the height of the first tropopause should be between 500hPa to 150hPa；Secondly, using 500hPa as starting point,
A temperature characterisitic layer is gradually first selected upwards, and first assumes that the selected characteristics layer is the first tropopause, and geopotential unit is
GH1, temperature T1；Then see that the arbitrary temp significant level in 2km altitude ranges above this layer, the potential of this temperature characterisitic layer are high
Degree is GHx, temperature TxIf temperature rate of change (the T that all temperature characterisitic layers providex-T1)/(GHx-GH1) numerical value it is big
Small to be all higher than -2, then it is the first tropopause to select the layer；Otherwise, in front on the basis of selected temperature characterisitic layer, continue to
Upper another temperature characterisitic layer of selection, it is assumed that it is the first tropopause, continues whether criterion meets above-mentioned condition, until finally selecting
Go out the first tropopause；
Step P302：The selection on the second troposphere top needs in the temperature characterisitic layer extracted in step P2, sets necessary item
Part obtains：First, the height on the second troposphere top is between 150hPa to 40hPa；Secondly, with first selected by step P301
Troposphere top is starting point, gradually first selectes a temperature characterisitic layer upwards, it is assumed that its geopotential unit is GH2, temperature T2；Then
See that the arbitrary temp significant level in 1.5km altitude ranges above this layer, the temperature characterisitic layer geopotential unit are GHy1, temperature be
Ty1If temperature rate of change (the T that all temperature characterisitic layers providey1-T2)/(GHy1-GH2) numerical values recited be respectively less than -3, first
It is assumed that maximum layer significant level within the scope of the 1.5km is the second troposphere top, and times above this layer in 2km altitude ranges
The geopotential unit of meaning temperature characterisitic layer, the temperature characterisitic layer is GHy2, temperature Ty2, it is satisfied by temperature rate of change (Ty2-T2)/
(GHy2-GH2) numerical values recited be all higher than -1, then select the layer be the second troposphere top；Otherwise, in above-mentioned selected highest one
On the basis of layer temperature characterisitic layer, another temperature characterisitic layer of selection is continued up, it is assumed that it is the second troposphere top, and whether criterion is full
Sufficient above-mentioned condition, until finally selecting the second troposphere top.
6. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P4, including
Following steps：
Step P401：Such as step P2, ground floor, i.e. sounding data the 0th second are equally selected first, as first wind characteristic layer
M1；
Step P402：With W0Represent the 0th second air speed value, WnRepresent n-th second air speed value, n=1,2,3 ...；GH0Represent the 0th
The geopotential unit value of second, GHnRepresent n-th second geopotential unit value, n=1,2,3 ...；With (Wn-W0)/(GHn-GH0) it is slope
Connect 2 points of (W0, GH0) and (Wn, GHn) auxiliary straight line is obtained, which simulates the wind speed size of point-to-point transmission with potential height
Degree changes linearly, then by the offset of second calculating actual wind speed profile to this auxiliary straight line；
Step P403：For wind direction, by the W in step P4020It is changed to wind direction WD0、WnIt is changed to wind direction WDn, repeat step
The way of P402 obtains the wind direction offset by the second；When above-mentioned wind speed or wind direction offset are respectively greater than respective threshold value, i.e.,
It is second wind characteristic layer M2 to select the layer；
Step P404：Using M2 as basic point, repeat the above steps P402 and P403, continues up and selects each wind characteristic layer successively
M3, M4 ..., Mx, x=1,2,3 ....
7. the rarefaction method of second grade Sounding Data according to claim 1, which is characterized in that in step P5：It will step
The specified layer obtained in rapid P1, the humiture significant level obtained in step P2, the wind characteristic layer in step P4 are ranked sequentially,
It according to the size of the geopotential unit of each sounding level, is arranged in order from low to high, obtains one completely based on second grade
Sounding profile after sounding profile rarefaction；The profile can capture hanging down for the meteorological elements such as temperature, humidity, wind direction, wind speed
The notable inflection point of straight profile.
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