Microwave link-weather radar combined rainfall inversion method based on Z-R relation adaptive adjustment

Abstract

The invention discloses a microwave link-weather radar combined rainfall inversion method based on Z-R relation adaptive adjustment, and the method comprises the steps: defining a related parameter set of microwave link rainfall estimation and weather radar reflectivity measurement in observation time, and carrying out the matching and space-time averaging processing, and obtaining rainfall intensity-radar reflectivity characteristics; fitting a radar reflectivity-rainfall intensity relation within observation time by using a least square method based on the rainfall intensity-radar reflectivity characteristics; according to the radar reflectivity-rainfall intensity relation sample of the known position, calculating the spatial distribution of the radar reflectivity-rainfall intensity relation in the research area by using an inverse distance weighted interpolation method; and according to the spatial distribution of the radar reflectivity-rainfall intensity relationship within the observation time and the average reflectivity factor measured by the radar, calculating to obtain a spatial distribution result of the accumulated rainfall within the observation time. According to the invention, the accuracy of regional rainfall monitoring is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of rainfall monitoring, and in particular relates to a microwave link-weather radar joint rainfall inversion method based on ZR relationship adaptive adjustment. Background Art

[0002] Accurate and timely rainfall monitoring has become a critical component of disaster prevention and mitigation, water resources management, and ecological and environmental protection. Weather radar, with its high spatiotemporal resolution and wide coverage, plays a vital role in regional precipitation monitoring. However, despite significant advances in weather radar technology, quantitative precipitation measurement still faces numerous challenges, one of which is the accuracy and applicability of the ZR relationship. The ZR relationship, the empirical relationship between radar reflectivity (Z) and rainfall intensity (R), is the foundation of precipitation estimation using weather radar. The accuracy of this relationship directly impacts the precision of radar precipitation estimates, and thus the scientific and effective nature of disaster prevention and mitigation decisions. However, the ZR relationship is not fixed; it is influenced by multiple factors, including precipitation type, precipitation particle characteristics, radar operating frequency, and environmental factors, and exhibits significant spatiotemporal variability. Therefore, precisely calibrating the ZR relationship of weather radar based on specific regional and environmental conditions is crucial to improving the accuracy of radar precipitation estimation.

[0003] In recent years, with the rapid development of microwave link technology, its application in meteorological monitoring has become increasingly widespread. Microwave links can indirectly reflect precipitation conditions by measuring changes in microwave signal attenuation along the transmission path. These links offer advantages such as high precision, high temporal and spatial resolution, and low cost, enhancing the accuracy and quality of precipitation products. Therefore, combining microwave link technology with weather radar to provide a novel approach and method for precipitation measurement is of significant significance. Summary of the Invention

[0004] To achieve the above object, the present invention provides a microwave link-weather radar joint rainfall inversion method based on ZR relationship adaptive adjustment, comprising:

[0005] Define the set of parameters related to microwave link rainfall estimation and weather radar reflectivity measurement within the observation time;

[0006] matching and spatiotemporally averaging the microwave link rainfall estimate and the weather radar reflectivity measurement to obtain a rainfall intensity-radar reflectivity characteristic;

[0007] Based on the rainfall intensity-radar reflectivity characteristics, the relationship between radar reflectivity and rainfall intensity during the observation time is fitted using the least squares method;

[0008] Based on the radar reflectivity-rainfall intensity relationship samples at known locations, the spatial distribution of the radar reflectivity-rainfall intensity relationship in the study area was calculated using the inverse distance weighted interpolation method.

[0009] The spatial distribution of the cumulative rainfall during the observation time was calculated based on the spatial distribution of the radar reflectivity-rainfall intensity relationship during the observation time and the average reflectivity factor measured by radar.

[0010] Preferably, the process of defining a set of relevant parameters for microwave link rainfall estimation and weather radar reflectivity measurement within an observation time includes:

[0011] A rainfall inversion algorithm based on a microwave link obtains a set of rainfall intensity estimates provided by the microwave link during the observation time after clear and rainy differentiation, baseline estimation, wet antenna attenuation correction, and rain attenuation relationship correction.

[0012] Gets the radar reflectivity map collection provided by the weather radar during the observation time.

[0013] Preferably, the rainfall intensity estimation set provided by the microwave link is N provided by multiple microwave links in the study area. l ×n ls An ensemble of rainfall intensity estimates;

[0014] The formula expression is:

[0015]

[0016] Among them, N l is the number of available microwave links, n ls is determined by the microwave link sampling time t ls and the number of observation samples of a single microwave link with observation time T.

[0017] Preferably, the process of obtaining a radar reflectivity map set provided by a weather radar during an observation time includes:

[0018] Weather radar provides N zs A collection of radar reflectivity maps of size N lon ×N lat ;

[0019] Among them, N lon and N lat are the number of pixels in the radar reflectivity map in longitude and latitude, N zs The radar scanning period t zs And the observation time T is determined by:

[0020]

[0021] Preferably, the process of matching and spatiotemporally averaging the microwave link rainfall estimate and the weather radar reflectivity measurement to obtain the rainfall intensity-radar reflectivity characteristic comprises:

[0022] Selecting a radar pixel included in a minimum rectangle that can completely cover the microwave link, and matching the microwave link with the radar pixel;

[0023] selecting radar pixels matching each microwave link and performing spatial averaging processing on the radar pixels;

[0024] The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time were processed by moving time averaging to obtain the rainfall intensity-radar reflectivity characteristics.

[0025] Preferably, the formula for performing spatial averaging processing on the radar pixels is:

[0026]

[0027] Where N(z, k) is the number of radar pixels matched to the microwave link, is the radar reflectivity factor of the radar pixel matched to the kth microwave link at the i-th time step, where i = 1, 2…, N zs ; m = 1, 2, ..., N (z，k) .

[0028] Preferably, the process of performing moving time averaging processing on the microwave link rainfall estimate and the spatially averaged radar reflectivity measurement during the observation time comprises:

[0029] The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time are respectively performed with a window size of T ω The moving time average processing is performed to obtain the average value of radar reflectivity and rainfall intensity estimation within the window;

[0030] The formula expression of the average value is:

[0031]

[0032] Among them, n lω and n zω Window T ω Number of internal microwave link measurements and radar reflectivity observations, np = N zs -n zω +1 is the number of average rainfall intensity-reflectivity observation sample pairs in the window, f(p)=[(p-1)(t zs / t ls )] represents the time step of the microwave link observation samples within the window.

[0033] Preferably, the process of fitting the radar reflectivity-rainfall intensity relationship during the observation time using the least squares method includes:

[0034] For the position of the kth microwave link (lon k ,lat k ), based on the rainfall intensity-reflectivity observation samples, the least squares method is used to fit the radar reflectivity-rainfall intensity relationship within the observation time;

[0035] The formula expression is:

[0036]

[0037] Among them, A T (lonk, latk) and B T (lonk, latk) is the observation time T (lon k ,lat k ) position and the radar reflectivity-rainfall intensity relationship coefficient.

[0038] Preferably, the formula for calculating the spatial distribution of the radar reflectivity-rainfall intensity relationship in the study area using the inverse distance weighted interpolation method is:

[0039]

[0040] Among them, λ k is the weight coefficient of the radar reflectivity-rainfall intensity relationship sample at the kth microwave link position for the unknown position, d k is the Euclidean distance between the kth microwave link position and the position to be estimated, r is the influence radius, and ρ is the power parameter.

[0041] Preferably, the formula for calculating the spatial distribution of the cumulative rainfall within the observation time is:

[0042] log(R T (lon,lat))=A T (lon,lat)+B T (lon,lat)Z T (lon,lat)

[0043] CR T (lon,lat)=T·R T (lon,lat)

[0044] Among them, Z T (lon,lat) and R T (lon, lat) represent the average radar reflectivity factor and average rainfall intensity at the position (lon, lat) within the observation time T, CRT (lon,lat) represents the cumulative rainfall experienced at the location during the observation time T.

[0045] Compared with the prior art, the present invention has the following advantages and technical effects:

[0046] By combining the high-temporal-resolution near-surface precipitation information provided by microwave links, the present invention can dynamically calibrate the ZR relationship of weather radar to adapt to changes in different precipitation types and environmental conditions, thereby improving the accuracy and reliability of radar precipitation estimation.

[0047] This paper proposes a microwave link-weather radar joint rainfall inversion method based on adaptive ZR relationship adjustment. Based on the ZR relationship adaptive adjustment model, this method accurately estimates the spatial distribution of accumulated precipitation by utilizing near-surface rainfall information retrieved from the microwave link and spatial rainfall information observed by the weather radar. Furthermore, this method relies on widely available commercial microwave link networks and does not require additional hardware investment. This method provides a cost-effective radar ZR relationship calibration method that can overcome the limitations of traditional radar rainfall estimation, such as the fixed ZR relationship coefficient and limited rainfall estimation accuracy. This method has high application value and can be applied as a new method for rainfall monitoring in real-world operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0049] Figure 1 This is a schematic diagram of the distribution of microwave links, weather radars, and rain gauges according to an embodiment of the present invention;

[0050] Figure 2 Schematic diagram of a method flow in an embodiment of the present invention.

[0051] Figure 3 This is a comparison diagram of the rainfall inversion result of a rainfall process according to an embodiment of the present invention and the radar quantitative precipitation estimation result based on the empirical ZR relationship. DETAILED DESCRIPTION

[0052] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] like Figure 1-3 As shown, this embodiment provides a microwave link-weather radar joint rainfall inversion method based on ZR relationship adaptive adjustment, including:

[0055] Define the set of parameters related to microwave link rainfall estimation and weather radar reflectivity measurement within the observation time;

[0056] The rainfall estimation from the microwave link and the weather radar reflectivity measurements are matched and spatially averaged to obtain the rainfall intensity-radar reflectivity signature.

[0057] Based on the rainfall intensity-radar reflectivity characteristics, the least squares method is used to fit the radar reflectivity-rainfall intensity relationship during the observation time.

[0058] Based on the radar reflectivity-rainfall intensity relationship samples at known locations, the spatial distribution of the radar reflectivity-rainfall intensity relationship in the study area was calculated using the inverse distance weighted interpolation method.

[0059] The spatial distribution of the cumulative rainfall during the observation time was calculated based on the spatial distribution of the radar reflectivity-rainfall intensity relationship during the observation time and the average reflectivity factor measured by radar.

[0060] Furthermore, the process of defining a set of relevant parameters for microwave link rainfall estimation and weather radar reflectivity measurement within the observation time includes:

[0061] A rainfall inversion algorithm based on a microwave link obtains a set of rainfall intensity estimates provided by the microwave link during the observation time after clear and rainy differentiation, baseline estimation, wet antenna attenuation correction, and rain attenuation relationship correction.

[0062] Gets the radar reflectivity map collection provided by the weather radar during the observation time.

[0063] Furthermore, the rainfall intensity estimation set provided by the microwave link is N provided by multiple microwave links in the study area. l ×n ls An ensemble of rainfall intensity estimates;

[0064] The formula expression is:

[0065]

[0066] Among them, N l is the number of available microwave links, n ls is determined by the microwave link sampling time t ls and the number of observation samples of a single microwave link with observation time T.

[0067] More specifically, in this embodiment, the observation time T is 90 minutes, and the number of available microwave links N is l is 48, n ls is 90, the microwave link sampling time t ls For 1 minute.

[0068] Furthermore, the process of obtaining a radar reflectivity map set provided by the weather radar during the observation time includes:

[0069] Weather radar provides N zs A collection of radar reflectivity maps of size N lon ×N lat ;

[0070] Among them, N lon and N lat are the number of pixels in the radar reflectivity map in longitude and latitude, N zs The radar scanning period t zs And the observation time T is determined by:

[0071]

[0072] More specifically, in this embodiment, N zs is 15, N lon is 49, N lat is 35, the radar scanning period t zs For 6 minutes.

[0073] Furthermore, the microwave link rainfall estimation and weather radar reflectivity measurements are matched and spatially averaged to obtain the rainfall intensity-radar reflectivity characteristics. The process includes:

[0074] For the kth microwave link, select the radar pixel included in the smallest rectangle that can completely cover the microwave link, and match the microwave link with the radar pixel;

[0075] Select the radar pixels that match each microwave link and perform spatial averaging on the radar pixels;

[0076] The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time were processed by moving time averaging to obtain the rainfall intensity-radar reflectivity characteristics.

[0077] Furthermore, the formula for spatial averaging of radar pixels is:

[0078]

[0079] Where N(z, k) is the number of radar pixels matched to the microwave link, is the radar reflectivity factor of the radar pixel matched to the kth microwave link at the i-th time step, where i = 1, 2…, N zs ; m = 1, 2, ..., N (z，k) .

[0080] Furthermore, the process of performing moving time averaging on the microwave link rainfall estimation and the spatially averaged radar reflectivity measurement during the observation time includes:

[0081] The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time are respectively performed with a window size of T ω The moving time average processing is performed to obtain the average value of radar reflectivity and rainfall intensity estimation within the window;

[0082] The formula for the average value is:

[0083]

[0084] Among them, n lω and n zω Window T ω Number of internal microwave link measurements and radar reflectivity observations, np = N zs -n zω +1 is the number of average rainfall intensity-reflectivity observation sample pairs in the window, f(p)=[(p-1)(t zs / t ls )] represents the time step of the microwave link observation samples within the window.

[0085] More specifically, in this embodiment, the window size T ω 18 minutes, n lω The number of n is 18, zω The number of observation samples of average rainfall intensity-reflectivity in the window is 3, and the number of observation sample pairs of average rainfall intensity-reflectivity in the window is 13.

[0086] Furthermore, the process of fitting the radar reflectivity-rainfall intensity relationship during the observation time using the least squares method includes:

[0087] For the position of the kth microwave link (lon k ,lat k ), based on the rainfall intensity-reflectivity observation samples, the least squares method is used to fit the radar reflectivity-rainfall intensity relationship within the observation time;

[0088] The formula expression is:

[0089]

[0090] Among them, A T (lonk, latk) and BT (lonk, latk) is the observation time T (lon k ,lat k ) position and the radar reflectivity-rainfall intensity relationship coefficient.

[0091] Furthermore, the ZR relationship fitting method of this embodiment includes but is not limited to least squares fitting.

[0092] Furthermore, based on the ZR relationship samples at known locations, the spatial distribution of the radar reflectivity-rainfall intensity relationship in the study area is calculated using the inverse distance weighted interpolation method as follows:

[0093]

[0094] Among them, λ k is the weight coefficient of the radar reflectivity-rainfall intensity relationship sample at the kth microwave link position for the unknown position, d k is the Euclidean distance between the kth microwave link position and the position to be estimated, r is the influence radius, and ρ is the power parameter.

[0095] More specifically, in this embodiment, the influence radius r is 10 km and the power parameter ρ is 2.

[0096] Furthermore, the spatial distribution estimation method of the ZR relationship in this embodiment includes but is not limited to the inverse distance weighted interpolation method.

[0097] Furthermore, the formula for calculating the spatial distribution of cumulative rainfall during the observation period is:

[0098] log(R T (lon,lat))=A T (lon,lat)+B T (lon,lat)Z T (lon,lat)

[0099] CR T (lon,lat)=T·R T (lon,lat)

[0100] Among them, Z T (lon,lat) and R T (lon, lat) represent the average radar reflectivity factor and average rainfall intensity at the position (lon, lat) within the observation time T, CR T (lon,lat) represents the cumulative rainfall experienced at the location during the observation time T. The results are as follows: Figure 3The rainfall results of the proposed method are well consistent with those measured by rain gauges, with a correlation coefficient of 0.65. The coefficient of variation and root mean square error are improved by approximately 17% and 15%, respectively, compared with the rainfall inversion results based on the empirical ZR relationship. The systematic bias is also relatively low, indicating that the proposed method can effectively improve the accuracy of precipitation estimation.

[0101] This embodiment proposes a microwave link-weather radar joint rainfall inversion method based on adaptive adjustment of the ZR relationship. By matching microwave link rainfall observations with weather radar reflectivity measurements, the temporal and spatial average rainfall intensity-radar reflectivity characteristics over the observation period are obtained. Based on an adaptive ZR relationship adjustment model, the ZR relationship is fitted in real time to adapt to the evolution of weather systems. The spatial distribution characteristics of the ZR relationship are analyzed based on observations from multiple microwave links and radars within a region. Based on the spatial distribution of the ZR relationship within the region, a cumulative rainfall map over the observation period is inverted. This method fully utilizes the near-surface rainfall information carried by the microwave link and the spatial rainfall information observed by radar. By adaptively adjusting the ZR relationship, it overcomes the problems of fixed ZR relationship coefficients and limited precipitation estimation accuracy in traditional radar precipitation estimation, effectively improving the accuracy of regional rainfall monitoring and finding wide application in regional quantitative precipitation monitoring.

[0102] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A microwave link-weather radar joint rainfall inversion method based on ZR relationship adaptive adjustment, characterized in that: include: Define the set of parameters related to microwave link rainfall estimation and weather radar reflectivity measurement within the observation time; matching and spatiotemporally averaging the microwave link rainfall estimate and the weather radar reflectivity measurement to obtain a rainfall intensity-radar reflectivity characteristic; Based on the rainfall intensity-radar reflectivity characteristics, the relationship between radar reflectivity and rainfall intensity during the observation time is fitted using the least squares method; Based on the radar reflectivity-rainfall intensity relationship samples at known locations, the spatial distribution of the radar reflectivity-rainfall intensity relationship in the study area was calculated using the inverse distance weighted interpolation method. The spatial distribution of the cumulative rainfall during the observation time was calculated based on the spatial distribution of the radar reflectivity-rainfall intensity relationship during the observation time and the average reflectivity factor measured by radar.

2. The method according to claim 1, characterized in that The process of defining the relevant set of parameters for microwave link rainfall estimates and weather radar reflectivity measurements over the observation time period involves: A rainfall inversion algorithm based on a microwave link obtains a set of rainfall intensity estimates provided by the microwave link during the observation time after clear and rainy differentiation, baseline estimation, wet antenna attenuation correction, and rain attenuation relationship correction. Gets the radar reflectivity map collection provided by the weather radar during the observation time.

3. The method according to claim 2, characterized in that The rainfall intensity estimation set provided by the microwave link is N provided by multiple microwave links in the study area. l ×n ls An ensemble of rainfall intensity estimates; The formula expression is: Among them, N l is the number of available microwave links, n ls is determined by the microwave link sampling time t ls and the number of observation samples of a single microwave link with observation time T.

4. The method according to claim 2, characterized in that The process of obtaining a radar reflectivity map set provided by the weather radar during the observation period includes: Weather radar provides N zs A collection of radar reflectivity maps of size N lon ×N lat ; Among them, N lon and N lat are the number of pixels in the radar reflectivity map in longitude and latitude, N zs The radar scanning period t zs And the observation time T is determined by:

5. The method according to claim 1, wherein The process of matching and spatiotemporally averaging the microwave link rainfall estimate and the weather radar reflectivity measurement to obtain a rainfall intensity-radar reflectivity characteristic includes: Selecting a radar pixel included in a minimum rectangle that can completely cover the microwave link, and matching the microwave link with the radar pixel; selecting radar pixels matching each microwave link and performing spatial averaging processing on the radar pixels; The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time were processed by moving time averaging to obtain the rainfall intensity-radar reflectivity characteristics.

6. The method according to claim 5, characterized in that The formula for performing spatial averaging processing on the radar pixels is: Where N(z, k) is the number of radar pixels matched to the microwave link, is the radar reflectivity factor of the radar pixel matched to the kth microwave link at the i-th time step, where i = 1, 2…, N zs ; m = 1, 2, ..., N (z，k) .

7. The method according to claim 5, characterized in that The process of performing moving time averaging on the microwave link rainfall estimate and the spatially averaged radar reflectivity measurement over the observation time includes: The microwave link rainfall estimation and spatially averaged radar reflectivity measurements within the observation time are respectively performed with a window size of T ω The moving time average processing is performed to obtain the average value of radar reflectivity and rainfall intensity estimation within the window; The formula expression of the average value is: Among them, n lω and n zω Window T ω Number of internal microwave link measurements and radar reflectivity observations, np = N zs -n zω +1 is the number of average rainfall intensity-reflectivity observation sample pairs in the window, f(p)=[(p-1)(t zs / t ls )] represents the time step of the microwave link observation samples within the window.

8. The method according to claim 1, characterized in that The process of fitting the radar reflectivity-rainfall intensity relationship during the observation time using the least squares method includes: For the position of the kth microwave link (lon k ,lat k ), based on the rainfall intensity-reflectivity observation samples, the least squares method is used to fit the radar reflectivity-rainfall intensity relationship within the observation time; The formula expression is: Among them, A T (lonk, latk) and B T (lonk, latk) is the observation time T (lon k ,lat k ) position and the radar reflectivity-rainfall intensity relationship coefficient.

9. The method according to claim 1, characterized in that The formula for calculating the spatial distribution of the relationship between radar reflectivity and rainfall intensity in the study area using the inverse distance weighted interpolation method is as follows: Among them, λ k is the weight coefficient of the radar reflectivity-rainfall intensity relationship sample at the kth microwave link position for the unknown position, d k is the Euclidean distance between the kth microwave link position and the position to be estimated, r is the influence radius, and ρ is the power parameter.

10. The method according to claim 1, characterized in that The formula for calculating the spatial distribution of cumulative rainfall during the observation period is: log(R T (lon,lat))=A T (lon,lat)+B T (lon,lat)Z T (lon,lat) CR T (lon,lat)=T·R T (lon,lat) Among them, Z T (lon,lat) and R T (lon, lat) represent the average radar reflectivity factor and average rainfall intensity at the position (lon, lat) within the observation time T, CR T (lon,lat) represents the cumulative rainfall experienced at the location during the observation time T.

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