CN108415101B - Second-level sounding data thinning method - Google Patents
Second-level sounding data thinning method Download PDFInfo
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Abstract
The invention discloses a second-level sounding data thinning method, which comprises the following steps: step P1: extracting sounding specified layer data from the second-level sounding outline; step P2: extracting data of the air sounding temperature and humidity characteristic layer from the second-level air sounding profile; step P3: selecting a convection layer top from the selected temperature and humidity characteristic layers; step P4: extracting data of sounding wind characteristic layers from the second-level sounding profile; step P5: and arranging and integrating the extracted layers. The invention can extract the obvious inflection points of the vertical profiles of meteorological elements such as temperature, humidity, wind direction, wind speed and the like from second-level sounding data, and obtains a complete sounding profile with about one or two hundred layers by integrating with important sounding levels such as specified isobaric surface layers, troposphere tops and the like. Compared with the layers obtained by other methods, the number of the layers of the profile is obviously increased, the fine structural characteristics of vertical change of each meteorological element can be more accurately described, and the application requirements of a numerical forecasting mode, data assimilation and the like are met.
Description
Technical Field
The invention relates to a sounding data processing application method, in particular to a second-level sounding data-based thinning method.
Background
The conventional sounding observation data is an extremely important information source for providing the land atmospheric state due to stable and reliable quality and a large number of vertical layers and the capability of accurately describing the atmospheric three-dimensional structure. Therefore, the sounding data plays an important role in research such as numerical prediction, weather analysis, climate change, satellite data calibration and the like, and especially in the data assimilation process of a numerical prediction system, the sounding data becomes the most important basic data which is indispensable for improving the initial field quality of the mode and improving the prediction precision.
With the development of sounding technology, the comprehensive updating from a 59-701 type sounding system to an L-band electronic sonde system has been completed in 2011 in China, and a new generation of sounding system can provide sounding data with second-level and high vertical resolution. On the other hand, numerical weather simulation, data assimilation and numerical weather forecast can all benefit from the acquisition and application of high-resolution sounding data, and the denser the sounding observation system is, the greater its contribution to the analysis accuracy is. However, due to the limitation of computing power, the current meteorological numerical model cannot assimilate and absorb all the second-level sounding data of thousands of layers, and in order to adapt to the data assimilation capability of the numerical prediction model, the second-level sounding data must be thinned. A specific sounding level is selected by a certain technical method to replace a second-level sounding profile of thousands of layers. Therefore, the sparse method is a very important technical link of the high-resolution sounding data in the application processes of numerical prediction, mode assimilation and the like.
At present, it is a common method to obtain a sounding profile with a small number of layers by integrating sounding specified layers and characteristic layers obtained by current service software of a weather station. However, the number of the characteristic layers extracted by the method is small, the obtained sounding profile cannot accurately depict the vertical change rule of meteorological elements, and the development requirements of the current numerical mode and data assimilation are difficult to meet. The other method is to use natural sparseness of second-level sounding data on a time axis, for example, half-minute and whole-minute sounding data with lower time resolution, but the method cannot ensure that the selected time is just the change inflection point of the vertical profile of the meteorological element, and cannot accurately analyze the detailed characteristics of the vertical change of the meteorological element. Therefore, an objective and reliable sparse method must be developed to extract an appropriate number of sounding profiles from the second-level sounding data, so that the assimilation application requirements of the numerical mode on the sounding data can be met, and the physical law of vertical change of meteorological elements can be accurately described.
In order to solve the problems, the invention provides a set of self-adaptive sparse technical scheme aiming at Chinese exploration L-waveband second-level data, which can extract significant inflection points of vertical profiles of meteorological elements such as temperature, humidity, wind direction, wind speed and the like from the second-level exploration data and integrate important exploration levels such as specified isobaric surface layers, troposphere tops and the like, thereby forming a complete exploration profile with about one or two hundred layers. The number of layers of the obtained profile is equivalent to the level of developed countries in Europe and America, and the fine structural characteristics of vertical changes of all meteorological elements can be accurately described. Based on the observed data deviation evaluation technology of the mode background field (ERA-Interim reanalysis data), quantitative indexes such as data volume, deviation (Bias), Root Mean Square Error (RMSE) and the like of meteorological elements on the sounding outline are obtained by different methods of contrastive analysis, and systematic inspection is carried out on the effect of the sparse scheme.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a set of self-adaptive sparse technical method for second-level sounding data, which can extract significant inflection points of vertical profiles of meteorological elements such as temperature, humidity, wind direction, wind speed and the like from the second-level sounding data and integrate with important sounding levels such as specified isobaric surface layers, troposphere tops and the like to obtain a complete sounding profile with about one to two hundred layers. Compared with the layers obtained by other methods, the number of the layers of the profile is obviously increased, the fine structural characteristics of vertical change of each meteorological element can be more accurately described, and the application requirements of a numerical forecasting mode, data assimilation and the like are met.
In order to achieve the purpose, the invention adopts the following technical scheme:
the second-level sparse method for the sounding data comprises the following steps of:
step P1: extracting sounding specified layer data from the second-level sounding outline;
step P2: extracting data of the air sounding temperature and humidity characteristic layer from the second-level air sounding profile;
step P3: selecting a convection layer top from the selected temperature and humidity characteristic layers;
step P4: extracting data of sounding wind characteristic layers from the second-level sounding profile;
step P5: and arranging and integrating the extracted layers.
In the second-level sparse method for sounding data, in step P1: if the second-level probing profile directly comprises a specified layer, namely, a layer with the air pressure value of 1000hPa, 925hPa, 850hPa, 700hPa, 600hPa, 500hPa, 400hPa, 300hPa, 250hPa, 200hPa, 150hPa, 100hPa, 70hPa, 50hPa, 40hPa, 30hPa, 20hPa, 15hPa, 10hPa, 7hPa and 5hPa, the second-level probing profile is directly extracted from the second-level probing data.
In the second-level sparse method for sounding data, in step P1: for a specified layer not included in the second-level profile, logarithmic interpolation calculation is required to obtain a meteorological element value on the relevant specified layer; for any sounding meteorological element Y of the specified layer on the sounding outlinexThe meteorological elements of the existing sounding hierarchy are Y based on the upper and lower adjacent specified levelsaAnd YbCalculating the meteorological element Y of the predetermined layerxThe interpolation algorithm of (1) is as follows:
wherein P is the pressure value of the prescribed layer, PaAnd PbRespectively, the air pressure values of the upper and lower adjacent levels of the specified level.
The second-level sparse method for sounding data in step P2 includes the following steps:
step P201: selecting a ground layer, namely 0 th second of sounding data, as a first temperature and humidity characteristic layer L1;
step P202: by T0Represents the temperature value, T, of the 0 th secondnA temperature value representing the nth second, n being 1, 2, 3 … …; GH0Representing the potential height value, GH, of the 0 th secondnThe potential height value of the nth second is represented, and n is 1, 2, 3 … …; with (T)n-T0)/(GHn-GH0) Connecting two points (T) for slope0，GH0) And (T)n，GHn) Obtaining an auxiliary straight line, wherein the auxiliary straight line simulates that the temperature between two points linearly changes along with the potential height, and then calculates the offset from the actual temperature profile to the auxiliary straight line by seconds;
step P203: for the moisture characteristics layer, T in step P2020Changed to RH0、TnChanged to RHnRepeating the method in the step P202 to obtain the humidity offset of each second; when the temperature or humidity offset is respectively greater than the respective threshold value, the layer is selected as a second temperature and humidity characteristic layer L2;
step P204: then, the above steps P202 and P203 are repeated with L2 as a base point, and the characteristic layers L3, L4, …, Lx, x are sequentially selected upward as 1, 2, and 3 … ….
The second-level sparse method for sounding data in step P3 includes the following steps:
step P301: the selection of the top of the first convection layer requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: first, the height of the top of the first convective layer should be between 500hPa and 150 hPa; secondly, with 500hPa as the starting point, a temperature characteristic layer is selected gradually upwards, and the selected characteristic layer is assumed to be the top of the first convection layer with the potential height GH1At a temperature of T1(ii) a Then, any temperature characteristic layer with the potential height GH within the height range of 2km above the layer is seenxTemperature of TxIf all temperature characteristic layers give a temperature gradient (T)x-T1)/(GHx-GH1) If the numerical value of the layer is larger than-2, selecting the layer as the top of the first convection layer; otherwise, on the basis of the previously selected temperature characteristic layer, another temperature characteristic layer is continuously selected upwards, and if the selected temperature characteristic layer is the top of the first convection layer, whether the conditions are met is continuously judged until the top of the first convection layer is finally selected;
step P302: the selection of the second convection layer top requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: firstly, the height of the top of the second convection layer is between 150hPa and 40 hPa; secondly, starting from the top of the first convection layer selected in step P301, a temperature characteristic layer is first selected gradually upward, assuming that the potential height is GH2At a temperature of T2(ii) a Then, looking at any temperature characteristic layer within the height range of 1.5km above the layer, the potential height of the temperature characteristic layer is GHy1Temperature of Ty1If all temperature characteristic layers give a temperature gradient (T)y1-T2)/(GHy1-GH2) Is less than-3, the highest characteristic layer in the range of 1.5km is assumed to be the top of the second convection layer, and any temperature characteristic layer in the height range of 2km above the second convection layer is assumed to have a potential height GHy2Temperature of Ty2All satisfy the temperature change gradient (T)y2-T2)/(GHy2-GH2) If the numerical values of the layers are all larger than-1, selecting the layer as the top of a second convection layer; otherwise, on the basis of the selected highest temperature characteristic layer, another temperature characteristic layer is continuously selected upwards, and if the selected temperature characteristic layer is the top of the second convection layer, whether the criterion meets the condition is judged until the top of the second convection layer is finally selected.
The second-level sparse method for sounding data in step P4 includes the following steps:
step P401: as with step P2, the ground layer, i.e., 0 th second of sounding data, is first selected as the first wind characteristics layer M1;
step P402: with W0Represents the wind speed value, W, at 0 secondnRepresenting the wind speed value of the nth second, wherein n is 1, 2, 3 … …; GH0Representing the potential height value, GH, of the 0 th secondnThe potential height value of the nth second is represented, and n is 1, 2, 3 … …; with (W)n-W0)/(GHn-GH0) Connecting two points for slope (W)0，GH0) And (W)n，GHn) Obtaining an auxiliary straight line which simulates the wind speed between two points to be presented along with the potential heightLinearly changing, and calculating the offset of the actual wind speed profile to the auxiliary line by seconds;
step P403: regarding the wind direction, W in step P4020Changing to wind direction WD0、WnChanging to wind direction WDnRepeating the procedure of the step P402 to obtain wind direction offset by one second; when the wind speed or the wind direction offset is respectively larger than the respective threshold value, the layer is selected as a second wind characteristic layer M2;
step P404: the above steps P402 and P403 are repeated with M2 as a base point, and the wind characteristics layers M3, M4, …, Mx, x being 1, 2, and 3 … … are sequentially selected upward.
In the second-level sparse method for sounding data, in step P5: arranging the specified layers obtained in the step P1, the temperature and humidity characteristic layers obtained in the step P2 and the wind characteristic layers in the step P4 in sequence, and arranging the specified layers, the temperature and humidity characteristic layers and the wind characteristic layers in the step P4 in sequence from low to high according to the potential heights of all sounding layers to obtain a complete sounding profile based on second-level sounding profile sparseness; the profile can capture significant inflection points of vertical profiles of meteorological elements such as temperature, humidity, wind direction, wind speed, and the like.
The invention has the following beneficial effects:
1. the method obviously increases the number of characteristic layers of meteorological elements such as temperature, humidity, wind direction, wind speed and the like, and can more accurately describe the vertical change microstructure of the meteorological elements.
2. The method is used as an important technical link in the assimilation application process of the second-level sounding data in the numerical mode, and the overall application benefit of the second-level sounding data is improved.
3. The method provides necessary technical support for the effect evaluation and inspection of the quality control of the second-level sounding data, and is beneficial to finding and improving the system quality problem of the second-level sounding data by performing comparative analysis on the second-level sounding data before and after the quality control and the existing similar data sources after the second-level sounding data are thinned.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a graph comparing vertical profiles of temperature obtained using two methods (the curves are shifted for clarity);
fig. 2 two methods result in a spatial distribution of the number of layers of warm-wet properties in an average single profile: FIG. 2a illustrates a prior art property layer dataset; FIG. 2b sparsification of the present invention;
FIG. 3 the sparsification scheme of the present invention yields the spatial distribution of the number of warm and humid characteristic layers in an average single profile in different seasons: FIG. 3a spring; FIG. 3b summer; FIG. 3c is autumn; FIG. 3d winter season;
FIG. 4 evaluation of two data sources versus ERA-Interim reanalysis data: FIG. 4a data volume, FIG. 4b Bias (Bias), FIG. 4c Root Mean Square Error (RMSE);
FIG. 5 is a vertical distribution plot of the results of evaluation of three data sources (integrated data source CMA-Merge, thinned data source CMA-Thin, CFSR) versus ERA-Interim reanalyzed data: FIG. 5a data volume, FIG. 5b Bias (Bias), FIG. 5c Root Mean Square Error (RMSE);
FIG. 6 is a flowchart of a method for thinning second-level sounding data according to the present invention;
FIG. 7 is a schematic diagram of a feature layer extraction algorithm based on a second-level exploration profile;
FIG. 8 is a comparison graph of the sparsification effect of the sparsification method of the present invention under different threshold parameters: FIG. 8a is a graph comparing the original second-level observation with the sparsification method of the present invention under different threshold parameters; fig. 8b is a partial enlarged view of fig. 8 a.
Detailed Description
In order to more clearly illustrate the invention, the invention is further described below with reference to preferred embodiments and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
1 materials and methods
1.1 study data
The L-waveband second-level sounding data used by the invention is from a Chinese high-altitude L-waveband second-level observation basic data set (V1.0) issued by a national meteorological information center, and the data is subjected to strict quality control, such as allowable value range inspection, runt value inspection, vertical consistency inspection, climate limit value inspection, filtering inspection, monotonicity inspection, consistency inspection among elements and the like. The quality control scheme of the second-level data does not belong to a part of the technical scheme of the invention, and the quality control scheme of the second-level data is the prior art, and is not described again here. The thinning scheme of the invention is based on second-level sounding data after quality control, so as to ensure the accuracy of data information obtained after thinning.
1.2 methods of investigation
1.2.1 sparsification method
As shown in fig. 6, the method for thinning second-level sounding data of the present invention specifically includes the following steps:
step P1: extracting sounding regulation layer data from second-level sounding outline
Step P101: if the second-class probing contour line directly comprises a specified layer, namely a specified isobaric surface layer (the layers with the air pressure value of 1000hPa, 925hPa, 850hPa, 700hPa, 600hPa, 500hPa, 400hPa, 300hPa, 250hPa, 200hPa, 150hPa, 100hPa, 70hPa, 50hPa, 40hPa, 30hPa, 20hPa, 15hPa, 10hPa, 7hPa and 5 hPa), the second-class probing data can be directly extracted.
Step P102: for a predetermined layer not included in the second-order profile, it is necessary to perform logarithmic interpolation to obtain a meteorological element value on the relevant predetermined layer. For any sounding meteorological element Y of the specified layer on the sounding outlinexBased on the existing sounding level meteorological element Y adjacent to the specified levelaAnd YbCalculating the meteorological element Y of the predetermined layerxThe interpolation algorithm of (1) is as follows:
wherein P is the pressure value of the prescribed layer, PaAnd PbRespectively, the air pressure values of the upper and lower adjacent levels of the specified level.
Step P2: extracting air exploration temperature and humidity characteristic layer data from second-level air exploration profile
Step P201: as shown in fig. 7, the three curves represent different meteorological elements (relative humidity RH, temperature T, air pressure P) on a complete second-level sounding profile, and a ground layer (0 th second of sounding data) is first selected as a first temperature-humidity characteristic layer L1.
Step P202: by T0Represents the temperature value, T, of the 0 th secondnA temperature value representing the nth second (n ═ 1, 2, 3.), GH0Representing the potential height value, GH, of the 0 th secondnA potential height value ( n 1, 2, 3.) representing the nth second is given as ((T)n-T0)/(GHn-GH0) Is a slope connecting two points (T)0，GH0) And (T)n，GHn) An auxiliary line is obtained which simulates the linear variation of the temperature between two points with the potential height, and the offset of the actual temperature profile (usually a curve) to this auxiliary line is calculated second by second.
Step P203: for the moisture characteristics layer, T in step P2020And TnChanged to RH0And RHnRepeating the above steps to obtain the humidity offset value by seconds; when the temperature or humidity offset is greater than the respective threshold, the second temperature/humidity characteristic layer L2 is selected (the threshold is shown by the thick black line in fig. 7, in an example application of the present invention, the temperature threshold is 0.75 ℃, and the relative humidity threshold is 5%).
Step P204: then, the above steps P202 and P203 are repeated with L2 as a base point, and the characteristic layers L3, L4, …, Lx, etc. are sequentially selected upward, where x is 1, 2, 3 … …
Step P205: because the meteorological elements have very large difference in the change rule of different sounding heights, the threshold value of the selected meteorological element changes along with the height, and the threshold value needs to be determined by repeated tests to meet different assimilation application requirements. As an application example of the present invention shown in fig. 8, taking the L-band sounding temperature vertical profile of the time 00UTC at 15/8/2015 at 51644 station (station of kurtosis, xinjiang), there are 3525 layers of original second-level observation data, and if the temperature threshold used at all sounding heights is 0.75 ℃, the number of obtained characteristic layers is 322 layers, and the number of obtained characteristic layers is 220 layers if the temperature threshold used at heights below 200hPa is 0.75 ℃, and at heights above 200hPa is 1 ℃. It is worth mentioning that the threshold value specifically adopted by each meteorological element needs to consider the number of vertical layers of the numerical prediction mode and the assimilation application capability of the second-level sounding data, and the method suggests that the threshold value is determined through test and evaluation according to the actual application effect of the threshold value parameters (such as a feedback improvement link in a flow chart 6).
Step P3: selecting a convection layer top from the selected temperature and humidity characteristic layers
Step P301: the selection of the top of the first convection layer requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: first, the height of the top of the first convective layer should be between 500hPa and 150 hPa; secondly, with 500hPa as the starting point, a temperature characteristic layer is selected gradually upwards, and the selected characteristic layer is assumed to be the top of the first convection layer with the potential height GH1At a temperature of T1(ii) a Then, see any temperature characteristic layer (potential height GH) within a height range of 2km above the layerxAt a temperature of Tx) If all temperature characteristic layers give a gradient of temperature change ((T)x-T1)/(GHx-GH1) ) are all greater than-2, then the layer is selected to be the top of the first convective layer. Otherwise, on the basis of the previously selected temperature characteristic layer, another temperature characteristic layer is continuously selected upwards, the other temperature characteristic layer is assumed to be the top of the first convection layer, and whether the criterion is met or not is considered until the top of the first convection layer is finally selected.
Step P302: the selection of the second convection layer top requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: first, the height of the top of the second convective layer should be between 150hPa and 40 hPa; secondly, starting from the top of the first convection layer selected in step P301, a temperature characteristic layer is first selected gradually upward, assuming that the potential height is GH2At a temperature of T2(ii) a Then, see any temperature characteristic layer (potential height GH) within a height range of 1.5km above the layery1At a temperature of Ty1) If all temperature characteristic layers give a temperature changeGradient ((T)y1-T2)/(GHy1-GH2) All smaller than-3, assuming that the highest characteristic layer in the 1.5km range is the top of the second convection layer and any temperature characteristic layer (potential height GH) in the height range of 2km above the layery2At a temperature of Ty2) All satisfy the temperature change gradient ((T)y2-T2)/(GHy2-GH2) ) are all greater than-1, then the layer is selected as the top of the second convective layer. Otherwise, on the basis of the highest temperature characteristic layer selected in the previous step, another temperature characteristic layer is continuously selected upwards, the other temperature characteristic layer is assumed to be the top of the second convection layer, and whether the criterion is met or not is judged until the top of the second convection layer is finally selected.
It is worth mentioning that in the inventive method the decision on the top of the first and the second convective layer, in particular the decision on the top of the second convective layer, is the most complex. Because the meteorological element value at the top of the current layer plays a particularly important role in the data assimilation process of the numerical mode, a specific layer identification code needs to be given in the thinning process. Therefore, despite the complicated determination method, the present invention provides a detailed determination basis based on the second-level exploration data. Of course, these determinations are only to mark the specific temperature characteristic layer as the top of the convection layer, and there is no influence on the number of sounding profile layers obtained by the whole thinning method.
Step P4: extracting sounding wind characteristic layer data from second-level sounding outline
Step P401: as with step P2, the ground layer (0 th second of sounding data) is also first selected as the first wind property layer M1.
Step P402: with W0Represents the wind speed value, W, at 0 secondnRepresenting the value of wind speed at the n-th second (n-1, 2, 3 ….), GH0Representing the potential height value, GH, of the 0 th secondnA potential height value (n ═ 1, 2, 3 ….) for the nth second to give ((W)n-W0)/(GHn-GH0) Is a slope connecting two points (W)0，GH0) And (W)n，GHn) Obtaining an auxiliary straight line which simulates the wind speed between two points to be presented along with the potential heightLinearly, and then calculates the offset of the actual wind profile (usually a curve) to this secondary line, second by second.
Step P403: regarding the wind direction, W in step P4020And WnChanging to wind direction WD0And WDnRepeating the above steps to obtain wind direction offset by seconds; when the wind speed or the wind direction deviation amount is greater than the respective threshold value, the layer is selected as the second wind characteristic layer M2.
Step P404: then, with M2 as a base point, repeating the above steps P402 and P403, and continuing to select the wind characteristic layers M3, M4, …, Mx, etc., in turn upward, where x is 1, 2, 3 … …
Step P405: in one example application of the invention, a wind direction threshold of 10 is used, and a wind speed threshold of 10 m/s.
Step P5: arranging and integrating the extracted layers according to a certain sequence
Step P501: and (3) arranging the specified layers obtained in the step P1, the temperature and humidity characteristic layers obtained in the step P2 and the wind characteristic layers obtained in the step P4 in sequence, and arranging the specified layers, the temperature and humidity characteristic layers and the wind characteristic layers in the step P4 in sequence from low to high according to the potential heights of all sounding layers to obtain a complete sounding profile based on second-level sounding profile rarefaction. The profile can capture the significant inflection point of the vertical profile of meteorological elements such as temperature, humidity, wind direction, wind speed and the like (as shown in fig. 1, it can be seen that, taking an L-band exploration temperature vertical profile of 2016 (GCOS exploration station), 1 month, 1 day, 00UTC times of 53068 stations (bilianhaot, inner mongolia), raw second-level observation data has 4318 layers, the number of the temperature and humidity characteristic layers given by the existing station software method is 30 layers, while the number of the temperature and humidity characteristic layers obtained by applying the sparsification method based on the second-level data designed by the invention is 118 layers), so that the fine structural characteristics of the vertical change of each meteorological element are more accurately described, when the exploration temperature is applied to the homogenization process of numerical mode data, the temperature value of the temperature characteristic layer needs to be linearly interpolated to the specific mode layer, and obviously, the temperature profile with a large number of the characteristic layer can provide more accurate temperature values. Therefore, the application requirements of a numerical prediction mode, data assimilation and the like are better met.
In order to meet the requirement of practical application, the sparsification scheme of the invention also realizes that meteorological elements of each specified isobaric surface are directly extracted from second-level sounding data and integrated with the characteristic layer data extracted from the second-level sounding data, so that a complete sounding profile with about 100-200 layers is formed.
1.2.2 sparsification effect inspection method
To test the sparsification effect, the test was quantified by comparing the deviation (Bias) and Root Mean Square Error (RMSE) between the probe observation data from different sources and the ERA-Interim reanalysis data. Wherein the Bias represents the difference between the average value of re-analyzed data and the average value of detected data in a certain period of time; and root mean square error
Is the square root of the ratio of the sum of the squares of the deviations of the ERA-Interim re-analysis data from the sounding measured data to the number of observations. The sounding profiles from these different sources are: the method comprises the steps of integrating sounding outline data (CMA-Merge) based on a timing value data set (V1.0) of a Chinese high altitude characteristic layer and a timing value data set (V2.1) of a Chinese high altitude specified equal pressure surface, obtaining the sounding outline data (CMA-Thin) based on a thinning scheme provided by the invention, and analyzing the sounding outline data in climate prediction system reanalysis data (CFSR) developed by the national environmental forecast center of America.
Although there may still be some difference between the re-analysis data and the observation result due to the influence of systematic errors such as numerical prediction mode and assimilation scheme, they can reasonably reflect the space-time distribution characteristics of the climate change of east Asia and its China region basically. Therefore, the ERA-Interim reanalysis data is used as a background field, and the sparse effect is quantitatively analyzed by comparing the deviation (Bias) and the root mean square difference (RMSE) of different data sources and the background field.
2 results and analysis
2.1 sparsification Effect contrast analysis
The single sounding profile thinning effect is shown in fig. 1, and by taking an L-band sounding temperature vertical profile of 53068 stations (disiante, GCOS sounding station) 2016, 1, 00UTC times as an example, it can be seen that there are 4318 layers of original second-level observation data, 30 layers of warm and wet characteristic layers given by a chinese high-level characteristic layer timing value data set (V1.0), and 118 layers of warm and wet characteristic layers obtained by applying the second-level data-based thinning method designed by the present invention. Comparing the three profiles, it can be clearly seen that the temperature profile obtained by the thinning method can more accurately depict the vertical change characteristic of the temperature by capturing the temperature inflection point with smaller scale than the existing 30-layer temperature profile. It is worth mentioning that when the detected air temperature is applied to the data assimilation process of the numerical mode, the temperature value of the temperature characteristic layer needs to be linearly interpolated to the specific mode layer, and obviously, the temperature profile with more layers of the characteristic layer can provide more accurate temperature values.
2.2 spatial-temporal distribution of the number of layers of the temperature-humidity characteristic
In order to further check the sparsification effect, the invention selects all second-level sounding profile data of 120 stations 00UTC and 12UTC in the year 2014 and carries out statistical analysis to obtain the number of temperature and humidity characteristic layers extracted by the average single sounding profile of each station, and the distribution of the number of stations in different value ranges is shown in table 1.
TABLE 1 site number distribution chart comparing different numbers of temperature and humidity characteristic layers obtained by two characteristic layer extraction methods
Fig. 2 shows a spatial distribution diagram of the number of layers of the wet-warm characteristic in the average single profile, in which fig. 2(a) shows the existing characteristic layer data set statistically, and fig. 2(b) shows the spatial distribution of the number of layers of the characteristic obtained by the thinning of the present invention. Firstly, both table 1 and fig. 2 show that the average value of the number of the warm-wet characteristic layers given by the existing characteristic layer data set is concentrated in 20-30 layers, while the average value of the number of the warm-wet characteristic layers obtained based on second data thinning is mostly in 60-90 layers, and the number of the characteristic layers is remarkably increased. Secondly, it can be seen that although the two methods give a large difference in the number of layers of characteristics, the spatial distributions of the number of layers of characteristics given in fig. 2(a) and 2(b) show good consistency; for example, the sites with relatively large number of characteristic layers obtained by statistics of the two methods are distributed in the southeast China with low altitude. Finally, it is noted that the 7 exploration stations marked by circles in the two figures are GCOS exploration stations, and the maximum exploration height of the exploration stations is obviously higher than that of the peripheral non-GCOS exploration stations due to the application of the amplification sphere, and the number of the characteristic layers obtained by the two methods is more than that of the peripheral stations. All the data show that the L-band second-level sounding data-based sparse scheme provided by the invention has good universality for 120 sounding stations in the country.
The vertical variation of the meteorological elements is not only related to the geographical distribution of the sites, but also should show different variation laws in different seasons. Table 2 shows the distribution of the number of sites in the value ranges of the number of different temperature and humidity characteristic layers in four seasons, namely spring, summer, autumn and winter.
TABLE 2 comparing different seasons, the thinning scheme of the present invention obtains the station number distribution table with different numbers of temperature and humidity characteristic layers
The results of the spatial distribution of the number of the warm and humid characteristic layers in different seasons in the average single profile obtained by the thinning are shown in fig. 3. As can be seen from the graph, the spatial distribution pattern of the number of characteristic layers for the four seasons substantially matches the annual distribution map given in fig. 2. It is worth mentioning that most sounding sites generally extract more number of characteristic layers in winter and spring than in summer and autumn, which is related to the fact that the temperature of the whole atmosphere is higher in summer and autumn, which results in more sufficient energy exchange.
2.3 sparsification Effect test based on ERA-Interim reanalysis data
Firstly, from the time series distribution, the comparison of fig. 4 shows the inspection effect of two data sources relative to the ERA-Interim reanalysis data, and it can be seen that the volume of the probe temperature data which is obtained based on the second-level data thinning scheme and participates in the comparison is far larger than the result of the integration through the existing probe data set, and the temperature deviation (Bias) relative to the ERA-Interim reanalysis data is equivalent to the level of the Root Mean Square Error (RMSE) and is not increased due to the increase of the data volume; and the trend of the T data volume, the T deviation and the T root mean square error along with time shows good consistency.
Second, from the spatial distribution of the test results, fig. 5 shows a vertical distribution plot of the temperature versus ERA-Interim reanalysis data test results for the three data sources in comparison, where fig. 5(a) represents the T data volume participating in the comparison analysis at different probe heights, fig. 5(b) is the temperature deviation (Bias), and fig. 5(c) is the temperature Root Mean Square Error (RMSE). From 5(a), the quantity of data at any sounding height is the least because of the smaller number of Chinese sites in CFSR; the amount of the temperature data obtained by the thinning is obviously more than that of the data integrated by the existing data set. From 5(b), the temperature deviation obtained by the thinning is closer to 0 ℃ than that of the other two data sources, and the vertical variation amplitude is smaller, so that the vertical consistency is better than that of the other two data sources. From 5(c), the temperature root mean square error obtained by thinning is equivalent to the level of the integrated data source at the sounding height 925 hPa-500 hPa; in the sounding lower layer 1000 hPa-925 hPa and the upper layer 500 hPa-100 hPa, the temperature root mean square error obtained by thinning is larger than that of the integrated data source, which may be related to the larger amount of data participating in statistics.
In summary, whether from the time-series distribution of the test results (fig. 4) or its vertical spatial distribution (fig. 5), the data source obtained by the sparsification scheme shows significant advantages: the T data volume is larger, the T _ RMSE level is equivalent, and the T _ Bias is smaller. It is worth mentioning that not only the sparsification effect of the meteorological element, temperature T, shows the statistical characteristics, but also other meteorological elements, such as humidity Rh, wind speed V, etc., can obtain similar conclusions, which are not described herein.
3 conclusion and discussion
The sparse scheme designed based on the L-band sounding second-level data can obviously increase the number of sounding profile characteristic layers for assimilation application, all second-level sounding of 120 sounding sites 2014 is sparse, the obtained spatial distribution of the number of the characteristic layers is in good consistency with the spatial distribution obtained through a characteristic layer data set, and the layer spatial distribution shows obvious seasonal change characteristics, so that the feasibility and universality of the sparse scheme are embodied.
And the sparse sounding data has better deviation evaluation effect than the integrated data with few sounding layers, and the difference of the sounding data can be clearly obtained by comparing the three methods through the time sequence diagrams and the vertical space distribution diagrams of the Bias and the RMSE. With the development of numerical prediction mode, especially the improvement of mode vertical resolution, more layers of sounding data are forced to enter assimilation application. The sparse scheme provided by the invention provides guarantee for the effective assimilation application of high vertical resolution sounding data.
In addition, the sparsification scheme improves an algorithm for extracting the characteristic layers, obviously increases the number of the characteristic layer, and can be better applied to researches such as climate change and the like. In the following research, different numerical prediction modes, such as GRAPES and the like, can be combined, case analysis is performed on a specific weather process, and the thinned sounding data is used for numerical prediction assimilation, so that the actual application effect of the thinning scheme can be better reflected.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (4)
1. The second-level sparse method for the sounding data is characterized by comprising the following steps of:
step P1: extracting sounding specified layer data from the second-level sounding outline;
if the second-level detection outline directly comprises a specified layer, namely a layer with the air pressure value of 1000hPa, 925hPa, 850hPa, 700hPa, 600hPa, 500hPa, 400hPa, 300hPa, 250hPa, 200hPa, 150hPa, 100hPa, 70hPa, 50hPa, 40hPa, 30hPa, 20hPa, 15hPa, 10hPa, 7hPa and 5hPa, the second-level detection outline is directly extracted from the second-level detection outline;
for a specified layer not included in the second-level profile, logarithmic interpolation calculation is required to obtain a meteorological element value on the relevant specified layer; for any sounding meteorological element Y of the specified layer on the sounding outlinexThe meteorological elements of the existing sounding hierarchy are Y based on the upper and lower adjacent specified levelsaAnd YbCalculating the meteorological element Y of the predetermined layerxThe interpolation algorithm of (1) is as follows:
wherein P is the pressure value of the prescribed layer, PaAnd PbThe air pressure values of the upper and lower adjacent layers of the specified layer are respectively;
step P2: extracting data of the air sounding temperature and humidity characteristic layer from the second-level air sounding profile;
step P201: selecting a ground layer, namely 0 th second of sounding data, as a first temperature and humidity characteristic layer L1;
step P202: by T0Represents the temperature value, T, of the 0 th secondnA temperature value representing the nth second, n being 1, 2, 3 … …; GH0Representing the potential height value, GH, of the 0 th secondnThe potential height value of the nth second is represented, and n is 1, 2, 3 … …; with (T)n-T0)/(GHn-GH0) Connecting two points (T) for slope0，GH0) And (T)n，GHn) Obtaining an auxiliary straight line which simulates that the temperature between two points linearly changes along with the potential height, and then calculating the actual temperature profile to the auxiliary straight line second by secondOffset of the straight assist line;
step P203: for the moisture characteristics layer, T in step P2020Changed to RH0、TnChanged to RHnRepeating the method in the step P202 to obtain the humidity offset of each second; when the temperature or humidity offset is respectively greater than the respective threshold, that is, the sounding observation layer meeting the condition that the temperature or humidity offset is respectively greater than the respective threshold is selected as the second temperature and humidity characteristic layer L2;
step P204: then, with L2 as a base point, repeating the above steps P202 and P203, and continuing to select the characteristic layers L3, L4, …, Lx, x being 1, 2, and 3 … … in the upward sequence;
step P3: selecting a convection layer top from the selected temperature and humidity characteristic layers;
step P4: extracting data of sounding wind characteristic layers from the second-level sounding profile;
step P5: and arranging and integrating the extracted layers.
2. The method for thinning the second-level sounding reference according to claim 1, wherein the step P3 comprises the following steps:
step P301: the selection of the top of the first convection layer requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: first, the height of the top of the first convective layer should be between 500hPa and 150 hPa; secondly, with 500hPa as the starting point, a temperature characteristic layer is selected gradually upwards, and the selected characteristic layer is assumed to be the top of the first convection layer with the potential height GH1At a temperature of T1(ii) a Then, any temperature characteristic layer with the potential height GH within the height range of 2km above the layer is seenxTemperature of TxIf all temperature characteristic layers give a temperature gradient (T)x-T1)/(GHx-GH1) If the numerical value of the layer is larger than-2, selecting the layer as the top of the first convection layer; otherwise, on the basis of the previously selected temperature characteristic layer, another temperature characteristic layer is continuously selected upwards, the temperature characteristic layer is assumed to be the top of the first convection layer, and whether the above conditions are met or not is continuously judged until the mostFinally, selecting the top of the first convection layer;
step P302: the selection of the second convection layer top requires setting the necessary conditions in the temperature profile extracted in step P2 to obtain: firstly, the height of the top of the second convection layer is between 150hPa and 40 hPa; secondly, starting from the top of the first convection layer selected in step P301, a temperature characteristic layer is first selected gradually upward, assuming that the potential height is GH2At a temperature of T2(ii) a Then, looking at any temperature characteristic layer within the height range of 1.5km above the layer, the potential height of the temperature characteristic layer is GHy1Temperature of Ty1If all temperature characteristic layers give a temperature gradient (T)y1-T2)/(GHy1-GH2) Is less than-3, the highest characteristic layer in the range of 1.5km is assumed to be the top of the second convection layer, and any temperature characteristic layer in the height range of 2km above the highest characteristic layer is assumed to have a potential height GHy2Temperature of Ty2All satisfy the temperature change gradient (T)y2-T2)/(GHy2-GH2) If the numerical values of the layers are all larger than-1, selecting the layer as the top of a second convection layer; otherwise, on the basis of the selected highest temperature characteristic layer, another temperature characteristic layer is continuously selected upwards, and if the selected temperature characteristic layer is the top of the second convection layer, whether the criterion meets the condition is judged until the top of the second convection layer is finally selected.
3. The method for thinning the second-level sounding reference according to claim 1, wherein the step P4 comprises the following steps:
step P401: as with step P2, the ground layer, i.e., 0 th second of sounding data, is first selected as the first wind characteristics layer M1;
step P402: with W0Represents the wind speed value, W, at 0 secondnRepresenting the wind speed value of the nth second, wherein n is 1, 2, 3 … …; GH0Representing the potential height value, GH, of the 0 th secondnThe potential height value of the nth second is represented, and n is 1, 2, 3 … …; with (W)n-W0)/(GHn-GH0) Connecting two points for slope (W)0，GH0) And (W)n，GHn) Obtaining an auxiliary straight line, wherein the straight line simulates that the wind speed between two points linearly changes along with the potential height, and then calculates the offset of the actual wind speed profile to the auxiliary straight line second by second;
step P403: regarding the wind direction, W in step P4020Changing to wind direction WD0、WnChanging to wind direction WDnRepeating the procedure of the step P402 to obtain wind direction offset by one second; when the wind speed or the wind direction offset is respectively greater than the respective threshold value, selecting the sounding observation layer meeting the condition that the wind speed or the wind direction offset is respectively greater than the respective threshold value as a second wind characteristic layer M2;
step P404: the above steps P402 and P403 are repeated with M2 as a base point, and the wind characteristics layers M3, M4, …, Mx, x being 1, 2, and 3 … … are sequentially selected upward.
4. The method for thinning the second-level sounding reference according to claim 1, wherein in step P5: arranging the specified layers obtained in the step P1, the temperature and humidity characteristic layers obtained in the step P2 and the wind characteristic layers in the step P4 in sequence, and arranging the specified layers, the temperature and humidity characteristic layers and the wind characteristic layers in the step P4 in sequence from low to high according to the potential heights of all sounding layers to obtain a complete sounding profile based on second-level sounding profile sparseness; the profile can capture significant inflection points of vertical profiles of meteorological elements such as temperature, humidity, wind direction, wind speed, and the like.
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