Optimal weighted fusion rainfall field reconstruction method based on cooperation of microwave link and rainfall measurement satellite

Abstract

The invention relates to an optimal weighted fusion rainfall field reconstruction method based on cooperation of a microwave link and a rainfall measurement satellite. The method comprises the following steps: reconstructing a microwave link rainfall field based on observation data of the microwave link in a research area; collecting a satellite rainfall field and a satellite rainfall estimation quality index of the research area; respectively constructing a weight coefficient matrix of the microwave link rainfall field and a weight coefficient matrix of the satellite rainfall field; and based on the weight coefficient matrix, obtaining a near-real-time regional rainfall field in which the microwave link and the satellite cooperate by using an optimal weight fusion method. According to the invention, near-ground rainfall information carried by a microwave link and rainfall space information observed by a rainfall measurement satellite are fully utilized, a space-ground integrated collaborative rainfall monitoring system is constructed, and the problem of limited inversion precision caused by factors such as cloud layer shielding and terrain influence in traditional satellite rainfall inversion is solved. The accuracy of regional rainfall monitoring is effectively improved, and the method has a very high practical application prospect.

Description

Technical Field

[0001] This invention relates to the field of rainfall monitoring technology, and in particular to an optimal weighted fusion rainfall field reconstruction method based on the collaboration of microwave links and rain measuring satellites. Background Technology

[0002] Against the backdrop of global climate change, extreme weather events are becoming increasingly frequent, particularly torrential rains and their secondary disasters, posing a significant threat to economic and social development. High-precision, real-time rainfall monitoring technology has become an urgent need in disaster prevention and mitigation, water resource management, and ecological protection. Rainfall measurement satellites, with their global coverage advantage, play an irreplaceable role in regional precipitation monitoring. However, existing satellite rainfall retrieval technologies still have significant limitations: geostationary meteorological satellites equipped with visible light / infrared imagers need to indirectly infer precipitation through cloud top characteristics, but the cloud-rain conversion relationship is highly uncertain; while low-orbit satellites equipped with microwave radiometers and rainfall radars offer higher retrieval accuracy, they are limited by long revisit periods, making it difficult to meet the continuous monitoring needs of short-duration heavy rainfall events. This technological bottleneck urgently needs to be overcome by fusing near-surface observation data.

[0003] Notably, the rapidly developing microwave link technology in recent years has provided an innovative solution for precipitation monitoring. By analyzing the attenuation characteristics of microwave signals along their propagation path, this technology can achieve high-precision precipitation retrieval at the minute and kilometer levels, offering significant advantages such as high spatiotemporal resolution, low construction costs, and ease of maintenance. In particular, the near-surface precipitation data it acquires precisely compensates for the inherent limitations of the "top-down" observation method of satellite remote sensing. By deeply integrating the wide-area coverage advantage of space-based satellites with the precise observation characteristics of ground-based links, an integrated "space-ground" three-dimensional monitoring network can be constructed. This not only effectively corrects systematic errors caused by cloud cover and topographic effects in satellite retrieval but also significantly improves the spatiotemporal resolution and measurement accuracy of precipitation products, potentially providing reliable technical support for key areas such as smart meteorology and urban flood control. Summary of the Invention

[0004] The purpose of this invention is to propose an optimal weighted fusion precipitation field reconstruction method based on the collaboration of microwave links and rain-measuring satellites, to address the problems existing in the prior art. This method analyzes the attenuation characteristics of microwave link signals to obtain path-averaged precipitation estimates. It then reconstructs high-resolution microwave link precipitation fields and satellite precipitation fields based on microwave link precipitation observations and satellite precipitation products, using both a precipitation field reconstruction algorithm and a spatial interpolation method. The optimal weights for the two precipitation fields are determined according to the distribution characteristics of microwave links within the observation area and the quality indicators of satellite precipitation estimates. Finally, a weighted fusion algorithm integrates the advantages of both data sources to generate a high-precision regional precipitation spatial distribution product. This method fully utilizes near-surface precipitation information carried by microwave links and precipitation spatial information observed by rain-measuring satellites, constructing an integrated space-ground collaborative precipitation monitoring system. It overcomes the limited inversion accuracy caused by factors such as cloud cover and terrain influence in traditional satellite precipitation inversion, effectively improving the accuracy of regional precipitation monitoring and possessing high practical application prospects.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] An optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration includes:

[0007] Reconstructing the microwave link precipitation field based on observational data of the microwave link within the study area;

[0008] Collect satellite precipitation fields and satellite precipitation estimation quality indicators for the study area;

[0009] Construct weight coefficient matrices for the microwave link precipitation field and the satellite precipitation field, respectively;

[0010] Based on the aforementioned weight coefficient matrix, the near-real-time regional precipitation field obtained by microwave link and satellite collaboration is obtained using the optimal weighted fusion method.

[0011] Optionally, reconstructing the microwave link precipitation field based on observational data of the microwave link within the study area includes:

[0012] The observation data of microwave links within the study area are processed by a rainfall inversion algorithm to obtain an estimate of the average rainfall intensity of the microwave links.

[0013] Based on the average rainfall intensity estimate, a high-resolution microwave link rainfall field is reconstructed using a rain field reconstruction algorithm.

[0014] Optionally, the satellite precipitation field and satellite precipitation estimation quality indicators for the study area include:

[0015] Collect satellite precipitation products for the study area, including: satellite precipitation estimates and quality indicators;

[0016] Spatial interpolation is performed on the satellite rainfall estimates and quality indices to obtain a high-resolution satellite rainfall field and satellite rainfall estimation quality indices.

[0017] Optionally, constructing the weighting coefficient matrix of the microwave link precipitation field includes:

[0018] For the microwave link rainfall field, a weight coefficient matrix for the microwave link rainfall field is established based on the distribution characteristics of the microwave link network at each grid point location in the rainfall field.

[0019] Optionally, constructing the weighting coefficient matrix of the satellite precipitation field includes:

[0020] For the satellite precipitation field, a weighting coefficient matrix of the satellite precipitation field is constructed based on the satellite precipitation estimation quality index.

[0021] Optionally, the weighting coefficient matrix of the microwave link rainfall field is:

[0022]

[0023] Among them, W ML,i,j Q represents the weighting coefficient of the microwave link rainfall field at position (i,j). i,j and D i,j These represent the number of microwave links within a 10km radius of position (i,j) and the distance to the nearest microwave link, respectively. α and β are constants for adjusting the weighting coefficients, M and N are the sizes of the precipitation field matrix, and ML represents the microwave links.

[0024] Optionally, the weighting coefficient matrix of the satellite precipitation field is:

[0025]

[0026] Among them, W Sat,i,j QI represents the weighting coefficient of the satellite precipitation field at position (i,j). Sat,i,j This represents the quality index of the satellite rainfall estimate at position (i,j) after spatial interpolation.

[0027] Optionally, the near-real-time regional rainfall field coordinated by the microwave link and satellite is as follows:

[0028]

[0029] Among them, R merged,i,j It is a precipitation field fused from microwave links and rain-measuring satellites, W ML,i,j W represents the weighting coefficient of the microwave link rainfall field at position (i,j). Sat,i,j R represents the weighting coefficient of the satellite precipitation field at position (i,j). ML,i,jR represents the rainfall estimate of the microwave link rainfall field at location (i,j). Sat,i,j This represents the rainfall estimate of the satellite rainfall field at location (i,j).

[0030] The beneficial effects of this invention are as follows:

[0031] This invention proposes an optimal weighted fusion method for reconstructing rainfall fields through the collaboration of microwave links and rain-measuring satellites. Based on the optimal weighted fusion method, it utilizes near-surface rainfall information retrieved from microwave links and spatial rainfall information from rain-measuring satellite remote sensing to achieve high-resolution and accurate estimation of regional rainfall fields. Furthermore, this method is based on a widely available microwave link network and requires no additional hardware investment, providing a cost-effective regional rainfall monitoring method with significant application value. This method can be applied to practical operations as a novel rainfall monitoring approach. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of microwave link and rain-measuring satellite collaborative observation of rainfall, which is an embodiment of the optimal weighted fusion rainfall field reconstruction method of microwave link and rain-measuring satellite in this invention.

[0034] Figure 2 This is a schematic diagram of the optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration according to an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram comparing the rainfall inversion results obtained by the collaboration between the microwave link and the rain-measuring satellite in an embodiment of the present invention with the rainfall estimation results based solely on the rain-measuring satellite; wherein, (a) is a scatter plot comparing the joint reconstruction results of the satellite and the microwave link with the ground rain gauge measurements, and (b) is a scatter plot comparing the simple satellite rainfall estimation with the ground rain gauge measurements. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] This embodiment proposes an optimal weighted fusion precipitation field reconstruction method based on microwave link and precipitation satellite collaboration. A schematic diagram of the microwave link and precipitation satellite collaborative rainfall observation in this embodiment is shown below. Figure 1 As shown, the workflow of the proposed optimal weighted fusion rainfall field reconstruction method based on the collaboration between microwave links and rainfall satellites is as follows: Figure 2 As shown, the specific steps include the following:

[0039] Step 1. Reconstruct the microwave link precipitation field based on observation data of the microwave link within the study area;

[0040] Step 2. Collect satellite precipitation fields and satellite precipitation estimation quality indicators for the study area;

[0041] Step 3. Construct the weighting coefficient matrices for the microwave link precipitation field and the satellite precipitation field, respectively;

[0042] Step 4. Based on the weight coefficient matrix, use the optimal weighted fusion method to obtain the near-real-time regional precipitation field of microwave link and satellite coordination.

[0043] Furthermore, step 1, reconstructing the microwave link precipitation field based on observational data of the microwave link within the study area, includes:

[0044] The observation data of microwave links within the study area are processed by a rainfall inversion algorithm to obtain an estimate of the average rainfall intensity of the microwave links.

[0045] Based on the average rainfall intensity estimate, a high-resolution microwave link rainfall field is reconstructed using a rain field reconstruction algorithm.

[0046] Specifically, in this embodiment, step 1 reconstructs a high-resolution microwave link precipitation field based on observation data from 48 microwave links within the study area, including performing steps (1-1) to (1-2):

[0047] (1-1) For time t, analyzing the observation data of microwave link signals, after processing by a rainfall inversion algorithm based on microwave links, including clear / rainy weather differentiation, baseline estimation, wet antenna attenuation correction, and rain attenuation relationship correction, the 48 microwave links in the study area can provide a set of 48 path-average rainfall intensity estimates:

[0048]

[0049] Where R MLi,t This represents the path-average rainfall estimate for the i-th microwave link at time t;

[0050] (1-2) Based on the estimated average rainfall intensity of 48 paths, the high-resolution microwave link rainfall field can be reconstructed using the inverse distance weighted interpolation method:

[0051]

[0052] where R ML,i,j (0 < i ≤ M, 0 < j ≤ N) represents the rainfall estimate of the microwave link rainfall field at the position (i, j), and M = 114 and N = 178 are the sizes of the rainfall field matrix.

[0053] Furthermore, step 2 for collecting the satellite rainfall field and the satellite rainfall estimate quality indicators of the study area includes:

[0054] Collect satellite rainfall products of the study area, including: satellite rainfall estimates and quality indicators;

[0055] Perform spatial interpolation processing on the satellite rainfall estimates and quality indicators to obtain a high-resolution satellite rainfall field and satellite rainfall estimate quality indicators.

[0056] Specifically, in this embodiment, step 2 for collecting satellite rainfall products of the study area includes satellite rainfall estimates and quality indicators. Use bicubic interpolation to improve the spatial resolution of the satellite rainfall products, and obtain a high-resolution satellite rainfall field and satellite rainfall estimate quality indicators:

[0057]

[0058] where R Sat,i,j (0 < i ≤ M, 0 < j ≤ N) represents the rainfall estimate of the satellite rainfall field at the position (i, j), and QI Sat,i,j (0 < i ≤ M, 0 < j ≤ N) represents the quality indicator of the satellite rainfall estimate after spatial interpolation at the position (i, j).

[0059] Furthermore, step 3 for constructing the weight coefficient matrix of the microwave link rainfall field includes:

[0060] For the microwave link rainfall field, based on the distribution characteristics of the microwave link network at each grid point position of the rainfall field, establish a weight coefficient matrix of the microwave link rainfall field. Among them, the distribution characteristics are the topological structure, quantity of the link distribution, and the distance between the nearest link and the rainfall field grid point, etc., which describe the distribution of the microwave link network near the grid point.

[0061] Specifically, in step 3 for the microwave link rainfall field, based on the distribution characteristics of the microwave link network at each grid point position of the rainfall field, establish a weight coefficient matrix of the microwave link rainfall field:

[0062]

[0063] Among them W ML,i,j Q represents the weighting coefficient of the microwave link rainfall field at position (i,j). i,j and D i,j These are the number of microwave links within 10km of position (i,j) and the distance to the nearest microwave link, respectively. α = 3 and β = 0.1 are constants used to adjust the weighting coefficients.

[0064] Furthermore, the weighting coefficient matrix for the satellite precipitation field is constructed as follows:

[0065] For the satellite precipitation field, a weighting coefficient matrix is ​​constructed based on the estimated quality index of the satellite precipitation. The quality index can be directly used as the weighting coefficient.

[0066] Specifically, in step 3, for the satellite precipitation field, a satellite precipitation field weighting coefficient matrix is ​​constructed based on the satellite precipitation estimation quality index after spatial interpolation:

[0067]

[0068] Among them W Sat,i,j This represents the weighting coefficient of the satellite precipitation field at position (i,j).

[0069] Specifically, in this embodiment, step 4 uses the optimal weighted fusion method to obtain the near-real-time regional rainfall field obtained by the collaboration between the microwave link and the satellite, based on the rainfall field and weighting coefficient matrix of the microwave link and the rain measuring satellite:

[0070]

[0071] Among them, R merged,i,j It is a rainfall field fused from microwave links and rain measuring satellites.

[0072] Figure 3 The results of fused precipitation fields and satellite-based precipitation fields were compared; among them, Figure 3 (a) is a scatter plot comparing the results of the joint reconstruction of satellite and microwave links with ground rain gauge measurements. Figure 3 (b) is a scatter plot comparing satellite-based precipitation estimates with ground-based rain gauge measurements. The results of the proposed method show a higher linear correlation with ground-based rain gauge references, a lower coefficient of variation, and a 27% and 34% reduction in root mean square error and mean relative deviation, respectively, compared to satellite-based results alone. Furthermore, the method exhibits less systematic bias, demonstrating its ability to effectively improve precipitation estimation accuracy.

[0073] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reconstructing a rainfall field based on the collaboration of microwave links and rainfall satellites, characterized in that, include: Reconstructing the microwave link precipitation field based on observational data of the microwave link within the study area; Collect satellite precipitation fields and satellite precipitation estimation quality indicators for the study area; Construct weight coefficient matrices for the microwave link precipitation field and the satellite precipitation field, respectively; Based on the aforementioned weight coefficient matrix, the near-real-time regional precipitation field obtained by microwave link and satellite collaboration is obtained using the optimal weighted fusion method.

2. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, Reconstructing the microwave link precipitation field based on observational data of the microwave link within the study area includes: The observation data of microwave links within the study area are processed by a rainfall inversion algorithm to obtain an estimate of the average rainfall intensity of the microwave links. Based on the average rainfall intensity estimate, a high-resolution microwave link rainfall field is reconstructed using a rain field reconstruction algorithm.

3. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The satellite precipitation field and satellite precipitation estimation quality indicators for the study area include: Collect satellite precipitation products for the study area, including: satellite precipitation estimates and quality indicators; Spatial interpolation is performed on the satellite rainfall estimates and quality indices to obtain a high-resolution satellite rainfall field and satellite rainfall estimation quality indices.

4. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The weighting coefficient matrix for constructing the microwave link precipitation field includes: For the microwave link rainfall field, a weight coefficient matrix for the microwave link rainfall field is established based on the distribution characteristics of the microwave link network at each grid point location in the rainfall field.

5. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The weighting coefficient matrix for constructing the satellite precipitation field includes: For the satellite precipitation field, a weighting coefficient matrix of the satellite precipitation field is constructed based on the satellite precipitation estimation quality index.

6. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The weighting coefficient matrix of the microwave link rainfall field is as follows: Among them, W ML,i,j Q represents the weighting coefficient of the microwave link rainfall field at position (i,j). i,j and D i,j These represent the number of microwave links within a 10km radius of position (i,j) and the distance to the nearest microwave link, respectively. α and β are constants for adjusting the weighting coefficients, M and N are the sizes of the precipitation field matrix, and ML represents the microwave links.

7. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The weighting coefficient matrix of the satellite precipitation field is as follows: Among them, W Sat,i,j QI represents the weighting coefficient of the satellite precipitation field at position (i,j). Sat,i,j This represents the quality index of the satellite rainfall estimate at position (i,j) after spatial interpolation.

8. The optimal weighted fusion precipitation field reconstruction method based on microwave link and rain measuring satellite collaboration as described in claim 1, characterized in that, The near-real-time regional precipitation field coordinated by the microwave link and satellite is as follows: Among them, R merged,i,j It is a precipitation field fused from microwave links and rain-measuring satellites, W ML,i,j W represents the weighting coefficient of the microwave link rainfall field at position (i,j). Sat,i,j R represents the weighting coefficient of the satellite precipitation field at position (i,j). ML,i,j R represents the rainfall estimate of the microwave link rainfall field at location (i,j). Sat,i,j This represents the rainfall estimate of the satellite rainfall field at location (i,j).

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