CN113311416A - Mountain region small watershed radar quantitative precipitation estimation technology - Google Patents
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Abstract
The invention discloses a mountainous small watershed radar quantitative precipitation estimation technology in the technical field of weather monitoring of a convective disaster, which comprises the following steps of S1: establishing a membership function, and acquiring Z-R relation statistics of different precipitation types; s2: judging the type of the echo, and removing the non-precipitation echo; s3: dividing precipitation types; s4: and (3) carrying out precipitation inversion through echo observation of different precipitation types and Z-R relation statistics of different precipitation types to generate a single-station radar quantitative precipitation estimation product, S5: according to the method, a radar precipitation rate jigsaw product is generated according to precipitation puzzles and radar reflectivity, and the precision of a QPE product of the mountainous region small watershed weather radar is improved through reflectivity factor quality control, precipitation type classification, radar mixed elevation reflectivity calculation, Z-R conversion relation and 5 aspects of radar puzzles, so that the QPE product can be better used for precipitation forecast and early warning of hydrological and geological disasters.
Description
Technical Field
The invention relates to the technical field of monitoring on weather of a convection disaster, in particular to a radar quantitative precipitation estimation technology for small watersheds in mountainous regions.
Background
The distribution of mountainous areas in China is wide, the risk of hydrological and geological disasters is high, the arrangement of weather radars with high space-time resolution can describe the space-time distribution of regional rainfall more finely, the accuracy of forecasting the hydrological and geological disasters in small mountainous areas is improved, but the difficulty of detecting the rainfall by the weather radars is increased by complicated topographic conditions in the mountainous areas, so that the precision of a weather radar quantitative rainfall estimation (QPE) product is difficult to meet the application requirement.
Currently, main radar QPE business products in China comprise national networking quantitative estimation precipitation (MQPE) products and disaster weather short-time nowcasting (SWAN) system regional networking products, compared with a foreign radar QPE product, the input data of the MQPE and the SWAN are single, only comprise radar base data and ground rain gauge observation data, a data source of the foreign radar QPE product also comprises a satellite, a mode and the like, is used for distinguishing precipitation types and particle phases and processing aiming at different precipitation characteristics, to improve the precision of QPE products, MQPE and SWAN are based on the reflectivity factors after quality control and are processed by adopting an algorithm of filtering and fuzzy logic, but also has certain difference, the quality control of the MQPE is more comprehensive, and the quality control comprises the elimination of ground object echoes, super-refraction echoes, electromagnetic radial interference, sea wave echoes, clear sky echoes, isolated point echoes and fault abnormal echoes; the SWAN system focuses on the filtration of isolated noise echoes and the inhibition of hyper-refraction echoes, in addition, the two systems use multi-elevation angle mixed scanning reflectivity to estimate ground precipitation, but the MQPE system only uses radar reflectivity data of elevation angles of 3.35 degrees, 2.4 degrees, 1.45 degrees and 0.5 degrees respectively in the distance intervals of 1-20, 21-34, 35-49 and 50-230 kilometers, and does not consider the influence of terrain occlusion on radar electromagnetic waves, the radar QPE product of the SWAN adopts a HybScan mixed scanning elevation angle automatic generation algorithm developed by the American national storm laboratory, namely, the terrain occlusion rate of different elevation angles of the radar on each azimuth angle and radial distance library is calculated according to high-resolution terrain data, a radar beam energy density distribution function and the propagation mode of the radar electromagnetic waves in the standard atmosphere, and observation information of higher elevation angles is used for the distance library with the occlusion rate of more than 60 percent to replace the distance library, when the precipitation rate is calculated, both the MQPE and the SWAN adopt a Z-R relation method, each radar uses a Z-R relation, the Z-R relation is fitted in real time by introducing rain gauge data, the space-time resolution of an MQPE product is 1 kilometer/1 hour, and the local strong convection weather characteristics frequently occurring in small flow domains of mountainous regions are difficult to describe; the space-time resolution of the SWAN product is 1 km/6 min, and the weather process monitoring applicability is stronger.
In summary, a main defect of the existing domestic radar QPE business product is that only a single Z-R relation is used for carrying out precipitation inversion, the weather radar can be considered to carry out effective precipitation detection on an area 230 kilometers away from a radar station basically, even if a ground rain gauge is used for observing and carrying out real-time fitting on the Z-R relation, the precipitation rate in the whole radar scanning plane is still difficult to accurately estimate through one Z-R relation, the precipitation rate is essentially determined by precipitation particle drop spectrums, precipitation in different areas and different types is researched, more than 200 different Z-R relations are obtained through statistic analysis of precipitation particle drop spectrum data, the single Z-R relation is difficult to accurately describe the characteristic difference of the precipitation drop spectrums caused by different precipitation processes, precipitation types and areas, and the accurate quantitative estimation of the precipitation radar is improved by using the corresponding Z-R relation according to the different precipitation particle drop spectrum characteristics The method has important significance, in actual observation, precipitation information observed by a weather radar through one-time body sweep can come from different precipitation types, namely lamellar cloud, convection cloud or mixed cloud precipitation, particularly in small mountainous regions with complex terrain, the space-time variability of precipitation is stronger, and the radar QPE can be deviated by using a single Z-R relation, so that the lamellar cloud, convection cloud or mixed cloud precipitation in a radar scanning plane needs to be distinguished in real time, the radar reflectivity is converted into the precipitation rate by adopting different Z-R relations aiming at different types of precipitation, the precision of the radar QPE is improved, the MQPE and SWAN adopt radar reflectivity factors to fit the Z-R relation with the observation of a ground rain gauge, but the radar reflectivity factors are the average of precipitation particle scattering information in a certain sampling space, and the ground rain gauge is single-point observation, the radar reflection rate and the precipitation rate can be determined by the rainfall spectrum of precipitation, and the Z-R relation of lamellar cloud, convection cloud and mixed cloud can be respectively counted in each radar scanning plane through long-time reflection rate observation and raindrop spectrum parameter inversion of a dual-frequency rain-measuring radar (GPM-DPR) of a global precipitation observation plan satellite, on the other hand, QPME and SWAN have respective advantages in reflection rate quality control and mixed scanning reflection rate shielding calculation, the quality control of the MQPE is comprehensive, the shielding of the terrain to radar electromagnetic waves is considered by the SWAN, and the improvement of the precision of the QPE product of the mountain small watershed radar can be promoted by comprehensively utilizing the technical advantages of the two sets of products.
The precision of the existing domestic radar QPE business product on the quantitative precipitation estimation of the small watershed of the mountainous region is still insufficient, and the early warning of precipitation forecast and hydrological and geological disasters is not fast and accurate enough.
Disclosure of Invention
The invention aims to provide a mountain small watershed radar quantitative precipitation estimation technology to solve the problem that the accuracy of the existing domestic radar QPE business product for mountain small watershed quantitative precipitation estimation is still insufficient in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the mountain land small watershed radar quantitative precipitation estimation technology comprises the following steps:
s1: artificially distinguishing physical quantities such as precipitation echoes, ground object echoes, clear sky echoes, sea wave echoes and the like according to historical radar base data, then establishing membership functions according to the characteristics of the echoes, and acquiring Z-R relation statistics of different precipitation types;
s2: calculating the characteristic value of the radar original observation reflectivity to obtain the criterion of all physical quantities for the 0-1 value range of different types of echoes, if the criterion value corresponding to the physical quantity of an echo point is larger, the possibility that the echo point belongs to the type of echo is higher, weighting and accumulating the criterion values, dividing the echo type according to a preset threshold value, and removing non-precipitation echoes;
s3: acquiring a 0 ℃ layer height and a-10 ℃ layer height from mode data or sounding data, when the 0 ℃ layer height is too low (lower than the radar height by 2km or less), determining that no convection is generated, identifying convection kernels based on reflectivity values of a plurality of elevation angles and vertical liquid water content, identifying the whole convection area by adopting a region growing method, identifying the whole convection area, wherein the rest non-convection precipitation areas are layered cloud precipitation areas after identifying the whole convection area, the layered cloud precipitation areas have bright band type precipitation and non-bright band type precipitation, identifying bright band kernels by a bright band identification algorithm firstly, namely grid points with radar combined reflectivity exceeding 35dBZ in the layered cloud area, and identifying the whole bright band area based on the bright band kernels by adopting a region growing method;
s4: for echo observation of different precipitation types, performing precipitation inversion through Z-R relation statistics of different precipitation types acquired in S1 to generate a single-station radar quantitative precipitation estimation product, and then generating multi-radar reflectivity, precipitation types and precipitation jigsaw grid point data according to single-station radar quantitative precipitation estimation data;
s5: for the precipitation jigsaw and the radar reflectivity, determining radar observation weights contributing to the precipitation jigsaw and the radar reflectivity on each jigsaw lattice point, wherein the weights depend on the height of the center of a radar electromagnetic wave beam from the ground and the vertical section diameter of the beam, the higher the beam is from the ground, the wider the beam is, the lower the representativeness of the precipitation is, namely the smaller the weight is, the weighted average is carried out on the reflectivity or the precipitation of different radars, and finally, the reflectivity, the precipitation type and the precipitation data with the space-time resolution of 1 km/6 min are output, and a radar precipitation rate jigsaw product is generated;
preferably, the acquisition of the Z-R relationship of different precipitation types in S1 is obtained by selecting an observation sample of a dual-frequency satellite rain radar (GPM-DPR) in a weather radar scanning plane, classifying the observation sample according to precipitation category information of the GPM-DPR, calculating a reflectivity in the same band (S or C band) as the weather radar by using a T matrix method according to a raindrop spectrum parameter provided by the GPM-DPR, and combining a ground rainfall estimated by the GPM-DPR raindrop spectrum.
Preferably, in S3, the identifying the whole convection region further includes further determining whether the identified convection region is correct by integrating three physical quantities, namely, the combined reflectivity of the lattice point exceeds 35dBZ and needs to satisfy: the combined reflectivity is greater than 45dBZ, the maximum reflectivity height is not at the height of bright band influence, and the reflectivity vertical gradient is less than 4 dBZ/km.
Preferably, in S5, for the precipitation type puzzle, if a radar identifies precipitation on a grid cell as convective precipitation and the radar observation information is closer to the ground, the precipitation type of the grid is marked as convective precipitation; if the precipitation type observed by the radar is lamellar cloud precipitation, and a bright band is identified by any grid unit radar, the precipitation type of the grid unit is marked as bright band lamellar cloud precipitation; if no bright band layered cloud precipitation is identified, then the mark is no bright band layered cloud precipitation.
Compared with the prior art, the invention has the beneficial effects that: according to the mountain small watershed radar quantitative precipitation estimation technology, reflectivity factor quality control, precipitation type classification, radar mixed elevation reflectivity calculation, Z-R conversion relation and 5 aspects of radar jigsaw processing are carried out, precision of a QPE product of a mountain small watershed weather radar is improved, and the radar QPE product with the space-time resolution of 1 kilometer/6 minutes is output to better serve precipitation forecast and early warning of hydrological and geological disasters.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a comparison graph of quantitative precipitation estimation algorithm results and ground rainfall station precipitation observations.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the sake of simplicity, common technical knowledge known to those skilled in the art is omitted in the following.
As shown in fig. 1, the method specifically comprises the following steps:
s1: artificially distinguishing physical quantities such as precipitation echoes, ground object echoes, clear sky echoes, sea wave echoes and the like according to historical radar base data, then establishing membership functions according to the characteristics of the echoes, and acquiring Z-R relation statistics of different precipitation types;
s2: calculating the characteristic value of the radar original observation reflectivity to obtain the criterion of all physical quantities for the 0-1 value range of different types of echoes, if the criterion value corresponding to the physical quantity of an echo point is larger, the possibility that the echo point belongs to the type of echo is higher, weighting and accumulating the criterion values, dividing the echo type according to a preset threshold value, and removing non-precipitation echoes;
s3: acquiring a 0 ℃ layer height and a-10 ℃ layer height from mode data or sounding data, when the 0 ℃ layer height is too low (lower than the radar height by 2km or less), determining that no convection is generated, identifying convection kernels based on reflectivity values of a plurality of elevation angles and vertical liquid water content, identifying the whole convection area by adopting a region growing method, identifying the whole convection area, wherein the rest non-convection precipitation areas are layered cloud precipitation areas after identifying the whole convection area, the layered cloud precipitation areas have bright band type precipitation and non-bright band type precipitation, identifying bright band kernels by a bright band identification algorithm firstly, namely grid points with radar combined reflectivity exceeding 35dBZ in the layered cloud area, and identifying the whole bright band area based on the bright band kernels by adopting a region growing method;
s4: for echo observation of different precipitation types, performing precipitation inversion through Z-R relation statistics of different precipitation types acquired in S1 to generate a single-station radar quantitative precipitation estimation product, and then generating multi-radar reflectivity, precipitation types and precipitation jigsaw grid point data according to single-station radar quantitative precipitation estimation data;
s5: for the precipitation jigsaw and the radar reflectivity, determining radar observation weights contributing to the precipitation jigsaw and the radar reflectivity on each jigsaw lattice point, wherein the weights depend on the height of the center of a radar electromagnetic wave beam from the ground and the vertical section diameter of the beam, the higher the beam is from the ground, the wider the beam is, the lower the representativeness of the precipitation is, namely the smaller the weight is, the weighted average is carried out on the reflectivity or the precipitation of different radars, and finally, the reflectivity, the precipitation type and the precipitation data with the space-time resolution of 1 km/6 min are output, and a radar precipitation rate jigsaw product is generated;
the Z-R relation of different precipitation types in S1 is obtained by selecting observation samples of a dual-frequency satellite rain-measuring radar (GPM-DPR) in a weather radar scanning plane, classifying the observation samples according to precipitation category information of the GPM-DPR, calculating the reflectivity under the same waveband (S or C waveband) as the weather radar by using a T matrix method according to the raindrop spectrum parameters provided by the GPM-DPR, and combining the ground rainfall estimated by the GPM-DPR raindrop spectrum.
Wherein, in S3, identifying the whole convection zone further includes further determining whether the identified convection zone is correct by integrating three physical quantities, namely, the lattice point combined reflectivity exceeds 35dBZ and needs to satisfy: the combined reflectivity is greater than 45dBZ, the maximum reflectivity height is not at the height of bright band influence, and the reflectivity vertical gradient is less than 4 dBZ/km.
Wherein, in S5, for the precipitation type tile, if a radar identifies precipitation on a grid cell as convective precipitation and the radar observation information is closer to the ground, the precipitation type of the grid is marked as convective precipitation; if the precipitation type observed by the radar is lamellar cloud precipitation, and a bright band is identified by any grid unit radar, the precipitation type of the grid unit is marked as bright band lamellar cloud precipitation; if no bright band layered cloud precipitation is identified, then the mark is no bright band layered cloud precipitation.
In the following, 7 rainfall processes with long duration and large influence range of 4-7 months in 2019 are selected from a certain province in the north by taking observation of a ground hour rainfall station as a reference to evaluate a radar quantitative rainfall estimation (QPE) product generated by the algorithm, and a specific implementation mode of the invention is further explained by combining with a technical scheme shown in FIG. 2.
FIG. 2 shows the root mean square error RMSE, the relative mean absolute error RMAE and the relative mean deviation RMB of the statistical scoring indicators for the two precipitation products of the 7 cases; in fig. 2 (a), the circle is the average rainfall of 1 hour of the rain gauge in the process, the box is the average rainfall of 1 hour of the radar QPE product in the process, and the date corresponding to 7 times of rainfall process is marked on the horizontal axis, it can be seen that the radar QPE product generated by the present invention is closer to the rainfall observed by the ground rainfall station, especially in the process 3 and the process 4 of (c), the average deviation RMB thereof is close to 0, generally, the radar QPE product generated by the present invention has a certain low estimation (RMB is a negative value), but basically controlled within-30%, and reaches-40% in the process 5 and the process 6, which indicates that the radar QPE product generated by the present invention is more stable in performance in different processes, and this point can also be reflected from the RMAE index, the RMAE of the radar QPE product generated by the present invention is stable at about 50%, and from the RMAE, the radar QPE product generated by the present invention is closer to the rainfall observed by the ground rainfall station, the RMSE of 7 processes is below 3mm, in the processes 3 and 4, the RMSE is below 1mm, the average rainfall of a rain gauge in 1 hour is closer to the average rainfall of a radar QPE1 hour, the deviation of the radar QPE product observed by the rain gauge is about 60% as seen from RMAE, the difference between different processes is mainly caused by the difference of the average rainfall intensity, in the 7-time rainfall process, the rainfall intensity of the process 6 and the process 7 is higher, the corresponding RMSE is relatively higher, and in the 3mm or so, the RMB of the process 6 is about-30%, which shows that the accuracy of algorithm estimation is reduced to a certain extent for the rainfall with medium intensity or above.
In summary, the method is stable, the generated radar QPE product is closer to the rainfall observation of the ground rainfall station, and the precision of the radar QPE product in the small watershed of the mountainous region can be improved, so that the method can be better used for rainfall forecast and early warning of hydrological and geological disasters.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the embodiments disclosed herein may be used in any combination, provided that there is no structural conflict, and the combinations are not exhaustively described in this specification merely for the sake of brevity and conservation of resources. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (4)
1. The mountain land small watershed radar quantitative precipitation estimation technology is characterized in that: the method comprises the following steps:
s1: artificially distinguishing physical quantities such as precipitation echoes, ground object echoes, clear sky echoes, sea wave echoes and the like according to historical radar base data, then establishing membership functions according to the characteristics of the echoes, and acquiring Z-R relation statistics of different precipitation types;
s2: calculating the characteristic value of the radar original observation reflectivity to obtain the criterion of all physical quantities for the 0-1 value range of different types of echoes, if the criterion value corresponding to the physical quantity of an echo point is larger, the possibility that the echo point belongs to the type of echo is higher, weighting and accumulating the criterion values, dividing the echo type according to a preset threshold value, and removing non-precipitation echoes;
s3: acquiring a 0 ℃ layer height and a-10 ℃ layer height from mode data or sounding data, when the 0 ℃ layer height is too low (lower than the radar height by 2km or less), determining that no convection is generated, identifying convection kernels based on reflectivity values of a plurality of elevation angles and vertical liquid water content, identifying the whole convection area by adopting a region growing method, identifying the whole convection area, wherein the rest non-convection precipitation areas are layered cloud precipitation areas after identifying the whole convection area, the layered cloud precipitation areas have bright band type precipitation and non-bright band type precipitation, identifying bright band kernels by a bright band identification algorithm firstly, namely grid points with radar combined reflectivity exceeding 35dBZ in the layered cloud area, and identifying the whole bright band area based on the bright band kernels by adopting a region growing method;
s4: for echo observation of different precipitation types, performing precipitation inversion through Z-R relation statistics of different precipitation types acquired in S1 to generate a single-station radar quantitative precipitation estimation product, and then generating multi-radar reflectivity, precipitation types and precipitation jigsaw grid point data according to single-station radar quantitative precipitation estimation data;
s5: and for the precipitation jigsaw and the radar reflectivity, determining radar observation weights contributing to the precipitation jigsaw and the radar reflectivity on each jigsaw lattice point, wherein the weights depend on the height of the radar electromagnetic wave beam center from the ground and the vertical section diameter of the beam, the higher the beam is from the ground, the wider the beam is, the lower the representativeness of the precipitation is, namely the smaller the weight is, the weighted average is carried out on the reflectivity or the precipitation of different radars, and finally, the reflectivity, the precipitation type and the precipitation data with the space-time resolution of 1 km/6 min are output, and a radar precipitation rate jigsaw product is generated.
2. The mountain land small watershed radar quantitative precipitation estimation technology of claim 1, wherein: the Z-R relation of different precipitation types in the S1 is obtained by selecting observation samples of a dual-frequency satellite rain-measuring radar (GPM-DPR) in a weather radar scanning plane, classifying the observation samples according to precipitation category information of the GPM-DPR, calculating the reflectivity under the same wave band (S or C wave band) as the weather radar by using a T matrix method according to the raindrop spectrum parameters provided by the GPM-DPR, and combining the ground rainfall estimated by the GPM-DPR raindrop spectrum.
3. The mountain land small watershed radar quantitative precipitation estimation technology of claim 1, wherein: in S3, identifying the entire convection region further includes further determining whether the identified convection region is correct by integrating three physical quantities, i.e., the lattice point combined reflectance exceeds 35dBZ and needs to satisfy: the combined reflectivity is greater than 45dBZ, the maximum reflectivity height is not at the height of bright band influence, and the reflectivity vertical gradient is less than 4 dBZ/km.
4. The mountain land small watershed radar quantitative precipitation estimation technology of claim 1, wherein: in S5, for the precipitation type tile, if a radar identifies precipitation on a grid cell as convective precipitation and the radar observation information is closer to the ground, the precipitation type of the grid is marked as convective precipitation; if the precipitation type observed by the radar is lamellar cloud precipitation, and a bright band is identified by any grid unit radar, the precipitation type of the grid unit is marked as bright band lamellar cloud precipitation; if no bright band layered cloud precipitation is identified, then the mark is no bright band layered cloud precipitation.
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| CN115792847A (en) * | 2022-11-08 | 2023-03-14 | 江西师范大学 | Quantitative precipitation estimation method based on neural network and echo vertical information |
| CN116049726A (en) * | 2023-04-03 | 2023-05-02 | 中国科学技术大学 | Method, device, equipment and storage medium for classifying rainfall types of Qinghai-Tibet plateau in summer |
| CN116911082A (en) * | 2023-09-14 | 2023-10-20 | 成都信息工程大学 | Precipitation particle quality and quantity estimation method based on precipitation radar and assimilation data |
| CN117409335A (en) * | 2023-12-14 | 2024-01-16 | 成都远望探测技术有限公司 | Meteorological radar precipitation rate downscaling method based on visible light image |
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