CN117741665A - Precipitation intensity estimation method and device, electronic equipment and storage medium - Google Patents
Precipitation intensity estimation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a precipitation intensity estimation method, a device, electronic equipment and a storage medium, and belongs to the technical field of atmosphere detection, wherein the method comprises the following steps: determining a radar differential attenuation rate according to the X/Ka dual-band radar data in the detection range; and correcting the radar differential attenuation rate by utilizing the predetermined X/Ka dual-band radar differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model. According to the method, the same precipitation target in the detection range is observed by using the X/Ka dual-band radar, richer radar data are collected, the radar differential attenuation rate is determined according to the radar data of the X band and the Ka band, a precipitation inversion model corresponding to the detection range is constructed, the influence of factors such as noise in the radar data is avoided, and finally the radar differential attenuation rate is input into the precipitation inversion model to obtain estimated precipitation intensity, so that the accuracy of precipitation intensity estimation is greatly improved.
Description
Technical Field
The present invention relates to the field of atmospheric detection technologies, and in particular, to a precipitation intensity estimation method, apparatus, electronic device, and storage medium.
Background
Precipitation intensity estimation is one of main contents of meteorological researches, and is widely applied to the fields of weather forecast, disaster early warning, water resource management, agricultural production, urban planning, construction and the like.
At present, the rainfall intensity estimation method is mainly based on single-band radars (such as Doppler weather radars and double-polarization weather radars), polarized parameters such as radar reflectivity factors, differential reflectivity, differential propagation phase shift rate, attenuation rate and the like are calculated in advance by utilizing echo power data measured by the single-band radars, a rainfall inversion model is built according to the polarized parameters, and finally the single-band radar data are input into the rainfall inversion model to estimate the rainfall intensity.
However, the precipitation inversion model established by utilizing the single-band radar data is influenced by factors such as raindrop spectrum change, radar noise, attenuation and the like, and the problems of unstable inversion relation and inaccurate inversion result exist, so that the precipitation intensity estimation accuracy is low.
Disclosure of Invention
The invention provides a precipitation intensity estimation method, a precipitation intensity estimation device, electronic equipment and a storage medium, which are used for solving the defect of low precipitation intensity estimation accuracy in the prior art.
In a first aspect, the present invention provides a method for estimating precipitation intensity, comprising:
According to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate;
and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
According to the precipitation intensity estimation method provided by the invention, the X/Ka dual-band radar data comprise radar near-end Ka band reflectivity factors, radar near-end X band reflectivity factors, radar far-end Ka band reflectivity factors and radar far-end X band reflectivity factors;
the method for determining the radar differential attenuation rate according to the X/Ka dual-band radar data in the detection range comprises the following steps:
determining a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determining a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor;
and determining the radar differential attenuation rate according to the first difference value and the second difference value.
According to the precipitation intensity estimation method provided by the invention, the calculation formula for determining the radar differential attenuation rate according to the first difference value and the second difference value is as follows:
Wherein k (X, ka) is the radar differential attenuation rate, r 1 R is the radar near-end distance 2 For the radar far-end distance, Z m (X,r 1 ) The radar near-end X-band reflectivity factor, Z m (X,r 2 ) The reflectivity factor of the far-end X wave band of the radar, Z m (Ka,r 1 ) Z is the radar near-end Ka band reflectivity factor m (Ka,r 2 ) Is the far-end Ka band reflectivity factor of the radar.
According to the precipitation intensity estimation method provided by the invention, the differential attenuation rate deviation is obtained by preprocessing a raindrop spectrum data sample and an X/Ka dual-band radar data sample of the detection range in any sampling period, and the method specifically comprises the following steps:
determining the number of raindrops in a unit volume and unit diameter range of the detection range in any sampling period according to the raindrop spectrum data sample, and constructing a T matrix scattering model related to the detection range;
respectively inputting the X-band radar parameters and the Ka-band radar parameters into the T matrix scattering model to obtain the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar;
calculating the difference between the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar to serve as a true value of the differential attenuation rate;
determining a differential attenuation rate sample value according to the X/Ka dual-band radar data sample;
And determining the differential attenuation rate deviation according to the difference value between the differential attenuation rate true value and the differential attenuation rate sample value.
According to the precipitation intensity estimation method provided by the invention, the calculation formula of the T matrix scattering model is as follows:
wherein A is H The radar attenuation rate is lambda is radar wavelength, im is an integral imaginary part, D is a raindrop diameter, N (D) is the number of raindrops in a unit volume unit diameter range, and f H (D) Is a horizontal forward scatter amplitude matrix.
According to the precipitation intensity estimation method provided by the invention, the precipitation inversion model is constructed by fitting historical X/Ka dual-band radar data and historical precipitation intensity data acquired in the detection range;
the historical X/Ka dual-band radar data comprises X/Ka dual-band radar data acquired in a plurality of time windows;
the historical precipitation intensity data includes precipitation intensities within each of the time windows.
According to the precipitation intensity estimation method provided by the invention, the expression of the precipitation inversion model is as follows:
R＝2.836k(X,Ka) 1.113 ；
wherein R is the estimated precipitation intensity, and k (X, ka) is the corrected radar differential attenuation rate.
In a second aspect, the present invention also provides a precipitation intensity estimation device, comprising:
The differential attenuation rate measuring and calculating unit is used for determining the differential attenuation rate of the radar according to the X/Ka dual-band radar data in the detection range;
and the precipitation intensity measuring and calculating unit is used for correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range, and obtaining the estimated precipitation intensity output by the precipitation inversion model.
In a third aspect, the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the precipitation intensity estimation method as described in any of the preceding claims when the program is executed by the processor.
In a fourth aspect, the invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a precipitation intensity estimation method as described in any of the above.
According to the precipitation intensity estimation method, the device, the electronic equipment and the storage medium, the same precipitation target in the detection range is observed by using the X/Ka dual-band radar, richer radar data are collected, the radar differential attenuation rate is determined according to the radar data of the X band and the Ka band, the precipitation inversion model corresponding to the detection range is constructed, the influence of factors such as noise in the radar data is avoided, and finally the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, so that the accuracy of precipitation intensity estimation is greatly improved.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a precipitation intensity estimation method according to the present invention.
FIG. 2 is a second flow chart of the method for estimating the intensity of precipitation according to the present invention.
FIG. 3 is a schematic diagram of the near-end reflectivity factor of the X/Ka dual-band radar provided by the invention.
FIG. 4 is a schematic diagram of the far-end reflectivity factor of the X/Ka dual-band radar provided by the invention.
Fig. 5 is a schematic diagram of a radar differential attenuation rate provided by the present invention.
Fig. 6 is a flow chart of a method for determining a differential attenuation rate deviation according to the present invention.
Fig. 7 is a schematic diagram of the corrected radar differential attenuation rate and the differential attenuation rate true value obtained according to the measured raindrop spectrum provided by the invention.
Fig. 8 is a schematic diagram of measured rainfall intensity provided by the present invention.
FIG. 9 is a schematic representation of a fitted precipitation inversion model provided by the present invention.
FIG. 10 shows the estimated precipitation intensity and the measured precipitation intensity, R (A) H ) One of the comparison diagrams of the method estimation results.
FIG. 11 is a diagram showing the second reflectivity factor of the near-end of the X/Ka dual-band radar according to the present invention.
FIG. 12 is a second schematic view of the far-end reflectivity factor of the X/Ka dual-band radar provided by the present invention.
FIG. 13 shows the estimated precipitation intensity and the measured precipitation intensity, R (A) H ) And a second comparison diagram of the estimation result of the method.
Fig. 14 is a schematic structural diagram of a precipitation intensity estimating device according to the present invention.
Fig. 15 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that in the description of embodiments of the present invention, 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. The orientation or positional relationship indicated by the terms "upper", "lower", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description and to simplify the description, and are not indicative or implying that the apparatus or elements in question must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The terms "first," "second," and the like in this specification are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present invention may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more. In addition, "and/or" indicates at least one of the connected objects, and the character "/", generally indicates that the associated object is an "or" relationship.
Precipitation intensity estimation is one of the main contents of meteorological studies, and is currently mainly based on single-polarization weather radar and double-polarization weather radar.
Rainfall intensity is estimated based on a single polarization weather radar (e.g., doppler weather radar), and a radar reflectivity factor Z (in mm) is typically calculated from the measured echo power 6 m -3 ) Then, a radar weather equation is applied, based on a predetermined Z-R relationship (z=ar b ) The precipitation intensity R (unit: mm/h). Typical relations commonly used at present have z=200r 1.6 。
The magnitude of the radar reflectivity factor Z is directly related to the rain spectrum, while the rainfall R is related to the falling end speed of the raindrops in addition to the rain spectrum. Thus, the same rainfall rate R may correspond to different rain spectrums, and may also correspond to different Z values.
Is affected by the variability of the raindrop spectrum, the Z-R relation (z=ar b ) And is unstable, the coefficients a and b of which vary with the location, season and type of precipitation, even though there is a change in the same precipitation process. Instability of the Z-R relationship greatly affects the accuracy of precipitation intensity estimation.
Precipitation intensity is estimated based on dual-polarization weather radar, typically by combining four polarization parameters differently.
The polarization parameters here include the radar reflectivity factor Z H (Unit: mm) 6 m -3 ) Differential reflectance Z DR (unit: dB), differential propagation phase shift rate K DP (unit:. Degree/km) and attenuation Rate A H (units: dB/km).
The different combinations can be broadly divided into five classes here, namely R (Z H )、R(K DP )、R(Z H ,Z DR )、R(K DP ,Z DR ) And R (A) H ) The specific relation may include: r (Z) H )＝a 1 Z H b1 、R(K DP )＝a 2 K DP b2 、R(Z H ,Z DR )＝a 3 Z H b3 10 c3ZDR 、R(K DP ,Z DR )＝a 4 K DP b4 10 c4ZDR 、R(A H )＝a 5 A H b5 。
However, there are drawbacks to the five types of combining methods under dual polarization weather radar.
R(Z H ) The method is a Z-R relation method under a dual polarization weather radar, and therefore, R (Z H ) The problem of inaccurate rainfall intensity estimation caused by unstable Z-R relationship is also faced.
Differential propagation phase shift rate K DP Phase shift Φ by differential propagation DP Derived, therefore, K is used DP The method of measuring precipitation is not affected by absolute calibration errors and attenuation. However, in the case where the rainfall R is not large, K DP Contains larger noise information, resulting in K DP Inaccuracy, and thus affect R (K) DP ) And R (K) DP ,Z DR ) Accuracy of precipitation estimation in weak precipitation areas, therefore, R (K DP ) And R (K) DP ,Z DR ) And is generally applied to the measurement of a strong dewatering area. In addition, K DP Is phi DP The derivative of the distance, whereas in actual calculation, can only be derived over a limited distance, and is therefore based on K DP There is a tradeoff between accuracy and range resolution in the precipitation intensity estimation method.
Differential reflectivity Z DR Is a relative power measurement, i.e. the ratio of the horizontally polarized power to the vertically polarized power, using Z DR Any method of precipitation measurement must be combined with Z H Or K DP Use, resulting in R (Z H ,Z DR ) And R (K) DP ,Z DR ) Also face R (Z H ) And R (K) DP ) Inaccurate rainfall intensity estimation.
R(A H ) Method the key to estimating precipitation intensity is inversion and obtaining accurate A H At present, generally through K DP Inversion (A) H ＝a 6 K DP b6 ) Or Z is H Inversion (A) H ＝a 7 Z H b7 ) Obtained. In one aspect, K DP Is easily polluted by noise, resulting in the passing of K DP Inversion of A H The data quality is poor. On the other hand, for passing Z H Inversion to obtain A H Due to the difference of precipitation types, the inversion coefficient of the method has larger instability, resulting in inversion A H There is also a large error. Thus, since a is inverted using a single band radar H There are cases where the inversion relationship is unstable and the inversion result is inaccurate, even with Z H 、Z DR And K DP Compared with the established precipitation intensity estimation relation, RA H ) The method has the advantage of insensitivity to rain drop spectrum variation, R (A H ) The method still has the problem of inaccurate rainfall intensity estimation.
Aiming at the problem of low accuracy in the existing precipitation intensity estimation method, the invention provides the precipitation intensity estimation method, the device, the electronic equipment and the storage medium with high accuracy based on the X/Ka dual-band radar.
It should be noted that, the execution main body of the precipitation intensity estimation method provided by the embodiment of the invention may be a server or a computer device, for example, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), and may also be various weather forecast computers.
The following describes a precipitation intensity estimation method, apparatus, electronic device and storage medium according to the present invention with reference to fig. 1 to 15.
Fig. 1 is a schematic flow chart of a precipitation intensity estimation method according to the present invention, as shown in fig. 1, including but not limited to the following steps:
step 101: and determining the radar differential attenuation rate according to the X/Ka dual-band radar data in the detection range.
In general, radar signals attenuate during propagation. First, the propagation of the radar signal in free space follows the law of free space propagation loss, and the power of the radar signal decreases with the increase of the propagation distance, i.e. the attenuation of the radar signal occurs. In addition to attenuation with increasing propagation distance, radar signals are attenuated by atmospheric attenuation, radar antenna loss or material characteristics, topography fluctuations, and obstruction during propagation.
When precipitation occurs, if the radar observes the precipitation in a certain area, the radar signal will attenuate. The water droplets may absorb radar waves such that the amplitude of the radar signal gradually decreases, and the water droplets may scatter the radar waves such that the radar signal becomes scattered and blurred. In general, the stronger the precipitation, the more pronounced the radar signal attenuation caused by the precipitation.
When the invention estimates the rainfall intensity, firstly, the X/Ka dual-band radar data in the detection range is collected. Specifically, the X/Ka dual-band radar transmits radar beams of an X band and a Ka band to the same precipitation target in the detection range through the antenna system, echo signals are generated after the radar beams of the two bands interact with the precipitation target, and the echo signals are collected and processed by the X/Ka dual-band radar to obtain X/Ka dual-band radar data in the detection range.
The X/Ka dual-band radar data, namely the radar data of two bands of X band and Ka band are used at the same time of being collected and processed by the radar system.
And then according to the radar data of the two frequency bands of the X band and the Ka band, the current radar differential attenuation rate can be determined through calculation.
The X wave band has longer wavelength, is suitable for long-distance detection, and can provide better ground resolution and penetrability. And the Ka wave band has shorter wavelength, is suitable for short-distance target detection, and can provide higher resolution and target recognition capability. Thus, the X/Ka dual band radar data may provide more abundant information and higher accuracy.
Step 102: and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
First, in the precipitation intensity estimation process, since the X/Ka dual-band radar data may be affected by noise or non-rayleigh scattering, an abnormal value may occur in the differential attenuation rate derived from the X/Ka dual-band radar data, and thus it is necessary to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation.
For example, according to the theory of attenuation of electromagnetic waves, which attenuates faster with higher frequencies, the radar differential attenuation rate should be positive, but affected by noise or non-rayleigh scattering, the radar differential attenuation rate derived from X/Ka dual-band radar data may be negative. Therefore, the radar differential attenuation rate needs to be corrected.
Then, the corrected radar differential attenuation rate is input to a precipitation inversion model corresponding to the detection range.
The precipitation inversion model is constructed according to historical precipitation data of the same precipitation target in the same detection range, and reflects the relationship between the precipitation intensity of the same precipitation target in the detection range and the radar differential attenuation rate.
Generally, when the X/Ka dual-band radar is used for observing the same precipitation meteorological target in the same detection range, the type and the set position of the X/Ka dual-band radar are fixed, and the topography and the obstacle in the detection range are fixed. Therefore, in the front and rear stages of precipitation, the attenuation of radar signals caused by factors such as propagation distance, radar antenna loss or material characteristics, topography fluctuation, obstacle shielding and the like is determined, and the radar differential attenuation rate obtained according to the X/Ka dual-band radar data is only different according to the precipitation conditions.
Finally, the estimated precipitation intensity output by the precipitation inversion model can be obtained by inputting the radar differential attenuation rate determined and corrected according to the current X/Ka dual-band radar data into the precipitation inversion model.
According to the precipitation intensity estimation method provided by the invention, the same precipitation target in the detection range is observed by using the X/Ka dual-band radar, richer radar data are acquired, the radar differential attenuation rate is determined according to the radar data of the X band and the Ka band, the precipitation inversion model corresponding to the detection range is constructed, the influence of factors such as noise in the radar data is avoided, and finally the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, so that the accuracy of precipitation intensity estimation is greatly improved.
Fig. 2 is a second flow chart of the precipitation intensity estimation method according to the present invention, as shown in fig. 2, in which, as an alternative embodiment, the X/Ka dual-band radar data includes a radar near-end Ka-band reflectivity factor, a radar near-end X-band reflectivity factor, a radar far-end Ka-band reflectivity factor, and a radar far-end X-band reflectivity factor.
The radar reflectivity factor is a parameter for representing the echo intensity of a meteorological target, and the radar reflectivity factor is dependent on the condition of a raindrop spectrum, namely, the radar reflectivity factor is related to the size, the number and the phase state of precipitation particles in a unit volume of a precipitation target object, and can be used for representing the precipitation intensity.
The near end of the radar refers to the area closer to the radar transmitter and receiver, in which the distance between the target and the radar system is relatively short, the propagation path of the radar wave is relatively simple, and the signal strength is high.
The far end of the radar refers to a region far from a radar transmitter and a receiver, in which a distance between a target and a radar system is relatively long, a propagation path of a radar wave is relatively complex, multiple absorption, reflection and scattering may be experienced, and signal strength is weak.
And receiving an X/Ka dual-band radar echo signal in a detection range by using the X/Ka dual-band radar, and combining the characteristics and parameters of a radar system according to the received echo signal intensity and arrival time, thereby obtaining X/Ka dual-band radar data such as a radar near-end Ka band reflectivity factor, a radar near-end X band reflectivity factor, a radar far-end Ka band reflectivity factor, a radar far-end X band reflectivity factor and the like.
Further, according to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate, specifically, calculating a first difference value between a radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, calculating a second difference value between a radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor, and finally determining the radar differential attenuation rate according to the first difference value and the second difference value.
FIG. 3 is a schematic diagram of the near-end reflectivity factor of the X/Ka dual-band radar provided by the invention. Wherein the X-band curve is an X-band radar near-end reflectivity factor schematic curve, the Ka-band curve is a Ka band radar near-end reflectivity factor schematic curve, and the observation time is 2022, 5 months, 11 days, and 00:53 to 07:53 in the early morning.
FIG. 4 is a schematic diagram of the far-end reflectivity factor of the X/Ka dual-band radar provided by the invention. Wherein the X-band curve is an X-band radar far-end reflectivity factor schematic curve, the Ka-band curve is a Ka-band radar far-end reflectivity factor schematic curve, and the observation time is 2022, 5 months, 11 days, 00:53 in the early morning to 07:53 in the early morning.
According to the precipitation intensity estimation method provided by the invention, the radar differential attenuation rate is determined according to the difference value of the reflectivity factors of the X wave band and the Ka wave band, the defect that the relation between the reflectivity factor of the single wave band radar and the precipitation intensity is unstable is overcome, and the radar differential attenuation rate is more reliable, so that the accuracy of precipitation intensity estimation is improved.
Based on the foregoing embodiment, as an alternative embodiment, the calculation formula for determining the radar differential attenuation rate according to the first difference value and the second difference value is as follows:
wherein k (X, ka) is the radar differential attenuation rate, r 1 R is the radar near-end distance 2 For the radar far-end distance, Z m (X,r 1 ) The radar near-end X-band reflectivity factor, Z m (X,r 2 ) The reflectivity factor of the far-end X wave band of the radar, Z m (Ka,r 1 ) Z is the radar near-end Ka band reflectivity factor m (Ka,r 2 ) Is the far-end Ka band reflectivity factor of the radar.
Fig. 5 is a schematic diagram of a radar differential attenuation rate provided by the present invention. The X/Ka dual-band radar data for calculating the radar differential attenuation rate are acquired from 00:53 in the early morning of 11 days of 5 months of 2022 to 07:53 in the early morning.
Fig. 6 is a schematic flow chart of a method for determining a differential attenuation rate deviation according to the present invention, as shown in fig. 6, as an alternative embodiment, the differential attenuation rate deviation is obtained by preprocessing a raindrop spectrum data sample and an X/Ka dual-band radar data sample of the detection range in any sampling period, and specifically includes:
determining the number of raindrops in a unit volume and unit diameter range of the detection range in any sampling period according to the raindrop spectrum data sample, and constructing a T matrix scattering model related to the detection range;
Respectively inputting the X-band radar parameters and the Ka-band radar parameters into the T matrix scattering model to obtain the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar;
calculating the difference between the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar to serve as a true value of the differential attenuation rate;
determining a differential attenuation rate sample value according to the X/Ka dual-band radar data sample;
and determining the differential attenuation rate deviation according to the difference value between the differential attenuation rate true value and the differential attenuation rate sample value.
The raindrop spectrum data sample and the X/Ka dual-band radar data sample are respectively obtained by measuring the same precipitation target in the detection range of X/Ka dual-wave Duan Lei by a raindrop spectrometer.
The sampling period can be comprehensively determined according to factors such as performance parameters of a raindrop spectrometer, performance parameters of an X/Ka dual-band radar, specific scene measurement requirements and the like, and the invention is not limited to the above.
It should be noted that, the X/Ka dual-band radar data sample for predetermining the differential attenuation rate deviation and the X/Ka dual-band radar data for estimating the precipitation intensity are both obtained by measuring the same precipitation target in the same detection range by the same type and set position of the X/Ka dual-band radar, and the sampling period of the former is earlier than the sampling period of the latter.
It should be noted that, the raindrop spectrometer used for collecting the raindrop spectrum data sample in the embodiment of the present invention includes, but is not limited to, a two-dimensional video raindrop spectrometer (2 DVD), a laser raindrop spectrometer, a photoelectric raindrop spectrometer, an acoustic raindrop spectrometer, and the like, which is not limited in this aspect of the present invention.
The T matrix scattering model is a numerical method for calculating scattered light of atmospheric particles or precipitation particles, and the scattering characteristics of the particles are simulated by adopting a matrix operation mode.
The method comprises the steps of constructing a T matrix scattering model according to a raindrop spectrum acquired by a raindrop spectrometer in a sampling period, inputting X-band radar parameters of an X/Ka dual-band radar and Ka-band radar parameters into the T matrix scattering model, simulating the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar of the raindrop spectrum, and taking the difference between the X-band radar and the attenuation rate of the Ka-band radar as a true value of a differential attenuation rate.
In addition, for an X/Ka dual-band radar data sample acquired by the X/Ka dual-band radar in a sampling period, a differential attenuation rate sample value is calculated according to a calculation formula for determining the differential attenuation rate of the radar according to the foregoing embodiment, and a difference between a differential attenuation rate true value and the differential attenuation rate sample value is obtained as a differential attenuation rate deviation.
According to the precipitation intensity estimation method provided by the invention, the differential attenuation rate deviation is determined according to the T matrix scattering model constructed by actually measured raindrop spectrums in the same sampling period and the corresponding actually measured X/Ka dual-band radar data, and the differential attenuation rate deviation is used for correcting the radar differential attenuation rate obtained by measurement and calculation in the precipitation estimation process, so that the adverse effect of noise factors outside the raindrop spectrums on the precipitation intensity estimation result is eliminated, and the accuracy of precipitation intensity estimation is improved.
Based on the foregoing embodiment, as an optional embodiment, the calculation formula of the T matrix scattering model is:
wherein A is H The radar attenuation rate is lambda is radar wavelength, im is an integral imaginary part, D is a raindrop diameter, N (D) is the number of raindrops in a unit volume unit diameter range, and f H (D) Is a horizontal forward scatter amplitude matrix.
According to the calculation formula of the T matrix scattering model, the X-band radar attenuation rate and the Ka-band radar attenuation rate in the same detection range in the sampling period can be calculated respectively, and the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate is the true value of the differential attenuation rate.
Fig. 7 is a schematic diagram of the corrected radar differential attenuation rate and the differential attenuation rate true value obtained according to the measured raindrop spectrum provided by the invention. The k (X, ka) -2DVD curve is a differential attenuation rate truth curve obtained according to a raindrop spectrum actually measured by a two-dimensional video raindrop spectrometer, the k (X, ka) -Radar curve is a corrected Radar differential attenuation rate curve, and X/Ka dual-band Radar data of the Radar differential attenuation rate are acquired from 00:53 in early morning to 07:53 in 11 days of 5 months of 2022.
In one embodiment, the wavelength in the X band corresponds to 3.2cm and the wavelength in the Ka band corresponds to 8.6mm.
In another embodiment, the sampling period is set to 1 minute, the sampling period of the raindrop spectrometer is set to 1 minute, and the sampling period of the X/Ka dual band radar is set to 2 seconds. In any sampling period, 1 observation data is acquired by the raindrop spectrometer, and 30 observation data can be acquired by the X/Ka dual-band radar.
When the differential attenuation rate true value is obtained, a T matrix scattering model is built according to the acquired raindrop spectrum data, radar parameters are combined, and scattering is simulated according to the T matrix scattering model to obtain the differential attenuation rate true value.
And when the differential attenuation rate sample value is obtained, the radar reflectivity factors are accumulated for 30 pieces of radar observation data, and the radar differential attenuation rate is calculated by using the accumulated radar reflectivity factors and is used as the differential attenuation rate sample value.
And finally, taking the difference value between the differential attenuation rate true value and the differential attenuation rate sample value as the differential attenuation rate deviation.
Based on the foregoing embodiment, as an optional embodiment, the precipitation inversion model is constructed by fitting historical X/Ka dual-band radar data and historical precipitation intensity data acquired in the detection range;
the historical X/Ka dual-band radar data comprises X/Ka dual-band radar data acquired in a plurality of time windows;
the historical precipitation intensity data comprise precipitation intensity in each time window, and the rainfall targets in the detection range are measured by the rain gauge.
The time window is set to collect historical X/Ka dual-band radar data and historical precipitation intensity data time ranges before estimating the precipitation intensity. The time window can be comprehensively determined according to the performance parameters of the rain gauge, the performance parameters of the X/Ka dual-band radar, specific scene measurement requirements and other factors, and the invention is not limited to the above.
The method for fitting the scattered points into the curve includes polynomial fitting, linear fitting, spline interpolation, least square fitting, nonlinear fitting, fourier series fitting and the like, and the method is not limited to this.
Fig. 8 is a schematic diagram of measured rainfall intensity provided by the present invention. Wherein, the observation time is 2022, 5 months, 11 days, 00:53 in the early morning to 07:53 in the early morning.
FIG. 9 is a schematic representation of a fitted precipitation inversion model provided by the present invention. The historical X/Ka dual-band radar data is radar differential attenuation rate, and the curve is a fitted rainfall inversion model.
As shown in FIG. 9, the historical precipitation intensity and the historical X/Ka dual-band radar differential attenuation rate have good corresponding relation, and the precipitation inversion model has good performance.
In one embodiment, the sampling period of the rain gauge is set to be 1 minute, the sampling period of the X/Ka dual-band radar is set to be 2 seconds, and the time window is set to be 1 minute. In any time window, 1 piece of historical rainfall intensity data is collected by the rain gauge, and 30 pieces of historical X/Ka dual-band radar data are collected by the X/Ka dual-band radar.
When the differential attenuation rate sample value is obtained, radar reflectivity factor accumulation is carried out on 30 pieces of radar observation data, and the radar differential attenuation rate is calculated by utilizing the accumulated radar reflectivity factors and is used as the differential attenuation rate sample value.
And taking the difference value between the differential attenuation rate true value and the differential attenuation rate sample value as the differential attenuation rate deviation.
And correcting the radar differential attenuation rate through the differential attenuation rate deviation.
And finally, fitting according to the corrected historical radar differential attenuation rate and the historical precipitation intensity data in a plurality of time windows, and constructing a precipitation inversion model.
According to the precipitation intensity estimation method provided by the invention, the X/Ka dual-band radar data with the same type and detection condition is used in the precipitation intensity estimation and precipitation inversion model construction process, and the X/Ka dual-band Duan Lei data are richer and higher in precision, so that a more stable and reliable precipitation inversion model is constructed, and the accuracy of the precipitation estimation method is improved.
Based on the foregoing embodiment, as an optional embodiment, the expression of the precipitation inversion model is:
R＝2.836k(X,Ka) 1.113 ；
wherein R is the estimated precipitation intensity, and k (X, ka) is the corrected radar differential attenuation rate.
Based on the above embodiment, as an alternative embodiment, each time an interval period (for example, 6 hours) passes, the estimated difference between the estimated precipitation intensity output by the precipitation inversion model in the sampling period and the real-time precipitation intensity is compared with a preset threshold, and when the estimated difference is greater than the preset threshold, the differential attenuation rate deviation and the precipitation inversion model are redetermined. And re-determining the differential attenuation rate deviation and the precipitation inversion model according to the method for determining the differential attenuation rate deviation and the precipitation inversion model.
And after the differential attenuation rate deviation is redetermined and the precipitation inversion model is reconstructed, correcting the radar differential attenuation rate by utilizing the redetermined differential attenuation rate deviation, inputting the radar differential attenuation rate to the reconstructed precipitation inversion model, and finally obtaining the estimated precipitation intensity which is suitable for the current precipitation condition.
In another embodiment, if the estimated precipitation intensity output by the precipitation inversion model during the sampling period is greater than the real-time precipitation intensity, the differential decay rate deviation and the precipitation inversion model are redetermined when the absolute value of the estimated difference between the two is greater than a first preset sub-threshold. If the estimated precipitation intensity output by the precipitation inversion model in the sampling period is smaller than the real-time precipitation intensity, when the absolute value of the estimated difference value between the estimated precipitation intensity and the real-time precipitation intensity is larger than a second preset sub-threshold value, the differential attenuation rate deviation and the precipitation inversion model are determined again.
Wherein the first preset sub-threshold is not equal to the second preset sub-threshold.
It should be noted that, the preset threshold, the first preset sub-threshold, and the second preset sub-threshold may be determined according to the historical precipitation intensity estimation result, the performance and the parameters of the measuring device, and the invention is not limited thereto.
The method and the device can adapt to the application scene of complex precipitation conditions and large precipitation fluctuation by checking the estimated precipitation intensity output by the precipitation inversion model at intervals of a period of time and when the difference value between the estimated precipitation intensity and the actually measured precipitation intensity is too large, indicating that the precipitation condition changes and determining the differential attenuation rate deviation and the precipitation inversion model again.
Finally, the invention also obtains the measured precipitation intensity by measuring the real-time precipitation intensity, and uses R (A H ) Method for estimating precipitation intensity to obtain R (A) H ) The method estimates the result, then the measured precipitation intensity, R (A H ) The method estimation result is compared with the precipitation intensity estimation result of the invention, so that the accuracy of the precipitation intensity estimation method provided by the invention is evaluated.
Since attenuation of Ka-band electromagnetic wave rain area is extremely serious and Ka-band rain area is observed to have meter scattering effect, R (A) based on X-band attenuation rate is adopted H ) The method estimates the precipitation process.
Attenuation rate A of X-band H According to the inversion relation calculation commonly used at present, the calculation formula is as follows:
wherein Z is H In mm 6 m -3 ，A H The unit is dB/km.
Precipitation intensity R (A) H ) According to the inversion relation calculation commonly used at present, the calculation formula is as follows:
FIG. 10 shows the intensity of precipitation provided by the present inventionEstimation results and measured precipitation intensity, R (A) H ) One of the comparison diagrams of the method estimation results. Wherein, the R (k (X, ka)) curve is the rainfall intensity estimation result curve of the invention, R Measured The curve is the measured rainfall intensity curve, R (A H ) The curve is R (A H ) The method estimates the result curve of the precipitation intensity, and the observation time is 2022, 5 months, 11 days, 00:53 to 07:53 in the early morning.
As shown in FIG. 10, the estimated precipitation intensity results obtained by the present invention are less different from the measured precipitation intensity and are much more accurate than R (A H ) The method.
In addition, the invention and R (A) are also evaluated by mean deviation (Average Deviation, AD), correlation coefficient (Correlation Coefficient, CC) H ) Accuracy of the method.
The average deviation AD and the correlation coefficient CC are defined as follows:
wherein Cov (R) Estimated -R Measured ) To estimate the precipitation intensity R Estimated With measured precipitation intensity R Measured Covariance of Var [ R ] Estimated ]Is R Estimated Variance of Var [ R ] Measured ]Is R Measured Is a variance of (c).
Table 1 shows the average deviation and correlation coefficient to obtain the values of the present invention and R (A H ) One of the statistical tables of the results of precipitation estimation. As shown in Table 1, the average deviation of the estimation results obtained by the R (k (X, ka)) estimation precipitation method based on the dual-band differential attenuation rate is smaller, the correlation coefficient is higher, and the R (A) estimation result is obviously superior to the R (A) based on the single-band attenuation rate H ) And (5) estimating a precipitation method.
TABLE 1 one of statistical tables of precipitation estimation results
In order to better evaluate the accuracy of the present invention in estimating precipitation, in one embodiment, the method for estimating precipitation intensity and R (A) provided by the present invention take the period from 04:19 in 11 am of 2022 to 14:19 in pm as the observation period, and the observation time span is 10 hours H ) The method estimates the precipitation intensity during this period, compares the obtained precipitation intensity estimation results, and again adopts the average deviation and the correlation coefficient to evaluate the accuracy of the present invention and the R (AH) method, which is described below with reference to fig. 11-13.
FIG. 11 is a diagram showing the second reflectivity factor of the near-end of the X/Ka dual-band radar according to the present invention. The X-band curve is an X-band radar near-end reflectivity factor schematic curve, the Ka-band curve is a Ka band radar near-end reflectivity factor schematic curve, and the observation time is 2022, 6 months, 11 days, 04:19 in the early morning and 14:19 in the afternoon.
FIG. 12 is a second schematic diagram of the far-end reflectivity factor of the provided X/Ka dual-band radar. The X-band curve is an X-band radar far-end reflectivity factor schematic curve, the Ka-band curve is a Ka-band radar far-end reflectivity factor schematic curve, and the observation time is 2022, 6 months, 11 days, 04:19 in the early morning to 14:19 in the afternoon.
FIG. 13 shows the estimated precipitation intensity and the measured precipitation intensity, R (A) H ) And a second comparison diagram of the estimation result of the method. Wherein, the R (k (X, ka)) curve is the rainfall intensity estimation result curve of the invention, R Measured The curve is the measured rainfall intensity curve, R (A H ) The curve is R (A H ) The method estimates the result curve of precipitation intensity.
As shown in FIG. 13, the estimated precipitation intensity results obtained by the present invention are less different from the measured precipitation intensity and are much more accurate than R (A H ) The method.
Table 2 shows the average deviation and correlation coefficient to obtain the values of the present invention and R (A H ) And two statistics of the precipitation estimation result of the method. As shown in Table 2, the invention estimates the precipitation based on the R (k (X, ka)) estimation method of the dual-band differential attenuation rateThe average deviation of the measured result is smaller, the correlation coefficient is higher, and the measured result is obviously superior to R (A) based on the single-band attenuation rate H ) And (5) estimating a precipitation method.
TABLE 2 second precipitation estimation statistics
Fig. 14 is a schematic structural diagram of a precipitation intensity estimation device according to the present invention, and as shown in fig. 14, the present invention further provides a precipitation intensity estimation device, which mainly includes:
the differential attenuation rate measuring and calculating unit 1401 is used for determining the differential attenuation rate of the radar according to the X/Ka dual-band radar data in the detection range;
the precipitation intensity measurement unit 1402 is configured to correct the radar differential attenuation rate by using a predetermined differential attenuation rate deviation, and then input the corrected radar differential attenuation rate to a precipitation inversion model corresponding to the detection range, so as to obtain an estimated precipitation intensity output by the precipitation inversion model.
It should be noted that, in the embodiment of the present invention, the precipitation intensity estimation device may execute the precipitation intensity estimation method described in any of the above embodiments during specific operation, which is not described in detail in this embodiment.
According to the precipitation intensity estimation device provided by the invention, the same precipitation target in the detection range is observed by using the X/Ka dual-band radar, richer radar data are acquired, the radar differential attenuation rate is determined according to the radar data of the X band and the Ka band, the precipitation inversion model corresponding to the detection range is constructed, the influence of factors such as noise in the radar data is avoided, and finally the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, so that the accuracy of precipitation intensity estimation is greatly improved.
Based on the above embodiment, as an alternative embodiment, the X/Ka dual-band radar data is acquired by using an X/Ka-band dual-antenna radar, where the X/Ka-band dual-antenna radar uses an all-solid-state frequency modulation continuous wave radar mechanism to work, and the X/Ka-band dual-antenna radar shares two transmit-receive antennas.
Fig. 15 is a schematic structural diagram of an electronic device according to the present invention, and as shown in fig. 15, the electronic device may include: a processor 1510, a communication interface (Communications Interface) 1520, a memory 1530, and a communication bus 1540, wherein the processor 1510, the communication interface 1520, and the memory 1530 communicate with each other via the communication bus 1540. The processor 1510 may invoke logic instructions in the memory 1530 to perform a precipitation intensity estimation method comprising: according to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate; and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
Further, the logic instructions in the memory 1530 described above may be implemented in the form of software functional units and may be stored on a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the precipitation intensity estimation method provided by the above embodiments, the method comprising: according to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate; and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
In yet another aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the precipitation intensity estimation method provided by the above embodiments, the method comprising: according to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate; and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for estimating precipitation intensity, comprising:
according to the X/Ka dual-band radar data in the detection range, determining a radar differential attenuation rate;
and correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.
2. The precipitation intensity estimation method according to claim 1, wherein the X/Ka dual-band radar data comprises a radar near-end Ka band reflectivity factor, a radar near-end X band reflectivity factor, a radar far-end Ka band reflectivity factor, and a radar far-end X band reflectivity factor;
the method for determining the radar differential attenuation rate according to the X/Ka dual-band radar data in the detection range comprises the following steps:
determining a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determining a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor;
and determining the radar differential attenuation rate according to the first difference value and the second difference value.
3. The precipitation intensity estimation method according to claim 2, wherein the calculation formula for determining the radar differential attenuation rate according to the first difference value and the second difference value is:
wherein k (X, ka) is the radar differential attenuation rate, r 1 R is the radar near-end distance 2 For the radar far-end distance, Z m (X,r 1 ) The radar near-end X-band reflectivity factor, Z m (X,r 2 ) The reflectivity factor of the far-end X wave band of the radar, Z m (Ka,r 1 ) Z is the radar near-end Ka band reflectivity factor m (Ka,r 2 ) Is the far-end Ka band reflectivity factor of the radar.
4. The precipitation intensity estimation method according to claim 1, wherein the differential attenuation rate deviation is obtained by preprocessing a raindrop spectrum data sample and an X/Ka dual-band radar data sample of the detection range in any sampling period, and specifically comprises:
determining the number of raindrops in a unit volume and unit diameter range of the detection range in any sampling period according to the raindrop spectrum data sample, and constructing a T matrix scattering model related to the detection range;
respectively inputting the X-band radar parameters and the Ka-band radar parameters into the T matrix scattering model to obtain the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar;
Calculating the difference between the attenuation rate of the X-band radar and the attenuation rate of the Ka-band radar to serve as a true value of the differential attenuation rate;
determining a differential attenuation rate sample value according to the X/Ka dual-band radar data sample;
and determining the differential attenuation rate deviation according to the difference value between the differential attenuation rate true value and the differential attenuation rate sample value.
5. The precipitation intensity estimation method of claim 4, wherein the T-matrix scattering model has a calculation formula:
wherein A is H The radar attenuation rate is lambda is radar wavelength, im is an integral imaginary part, D is a raindrop diameter, N (D) is the number of raindrops in a unit volume unit diameter range, and f H (D) Is a horizontal forward scatter amplitude matrix.
6. The precipitation intensity estimation method according to claim 1, wherein the precipitation inversion model is constructed by fitting historical X/Ka dual-band radar data and historical precipitation intensity data acquired in the detection range;
the historical X/Ka dual-band radar data comprises X/Ka dual-band radar data acquired in a plurality of time windows;
the historical precipitation intensity data includes precipitation intensities within each of the time windows.
7. The precipitation intensity estimation method of claim 6, wherein the expression of the precipitation inversion model is:
R＝2.836k(X,Ka) 1.113 ；
wherein R is the estimated precipitation intensity, and k (X, ka) is the corrected radar differential attenuation rate.
8. A precipitation intensity estimation device, comprising:
the differential attenuation rate measuring and calculating unit is used for determining the differential attenuation rate of the radar according to the X/Ka dual-band radar data in the detection range;
and the precipitation intensity measuring and calculating unit is used for correcting the radar differential attenuation rate by utilizing the predetermined differential attenuation rate deviation, inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range, and obtaining the estimated precipitation intensity output by the precipitation inversion model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the precipitation intensity estimation method according to any of claims 1 to 7 when executing the computer program.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the precipitation intensity estimation method according to any of claims 1 to 7.
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