Improving the Data Consistency Between GPM and Weather Radar with Advection Correction.
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| Title: | Improving the Data Consistency Between GPM and Weather Radar with Advection Correction. |
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| Authors: | Kuang, Yijia1 (AUTHOR), Li, Haoran2 (AUTHOR) lihr@cma.gov.cn |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p782. 15p. |
| Subjects: | Optical flow, Rainfall, Space-based radar, Radar meteorology, Data integrity, Thunderstorms |
| Abstract: | Highlights: What are the main findings? Advection correction can effectively address the temporal mismatch in multi-source data. The performances among the various advection correction methods are similar. Overall, the LK method performs slightly better than AD, followed by VET. What are the implications of the main findings? When studying fast-moving convective storms, temporal mismatch among multi-source instruments should not be ignored. The choice of advection correction method has little impact on the performance of temporal matching. Multi-instrument synergistic observation is vital for studying cloud and precipitation physics. However, using the nearest scan time for matching inevitably introduces temporal mismatches. Here we employ three advection correction methods for temporal matching in weather radar and spaceborne radar observations: Lucas–Kanade (LK), Variational Echo Tracking (VET), and Anisotropic Diffusion (AD). These methods calculate the movement speed of the storms using optical flow methods, and then determine their positions based on the elapsed time between instruments. Next, we conducted a quantitative assessment of the performance of these three methods based on the consistency of storm morphology and rainfall rates. Our results demonstrate that all three advection correction methods effectively reduce the discrepancies in morphology and rainfall rate among multi-source data. Without correction, the Coincidence Rate (CR) and Structural Similarity (SSIM) were 30.96% and 0.689 in the US and 29.44% and 0.670 in China, respectively. In comparison, applying the LK, VET, and AD methods increased those indices to 32.94%, 32.72%, 32.85% and 0.718, 0.715, 0.716 in the US, and 31.34%, 31.17%, 31.24% and 0.696, 0.694, 0.693 in China, respectively. The rainfall rate inconsistencies were also effectively reduced after advection correction. The performances among the three methods were similar. Overall, the LK method performed slightly better than AD, followed by VET. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Advection correction can effectively address the temporal mismatch in multi-source data. The performances among the various advection correction methods are similar. Overall, the LK method performs slightly better than AD, followed by VET. What are the implications of the main findings? When studying fast-moving convective storms, temporal mismatch among multi-source instruments should not be ignored. The choice of advection correction method has little impact on the performance of temporal matching. Multi-instrument synergistic observation is vital for studying cloud and precipitation physics. However, using the nearest scan time for matching inevitably introduces temporal mismatches. Here we employ three advection correction methods for temporal matching in weather radar and spaceborne radar observations: Lucas–Kanade (LK), Variational Echo Tracking (VET), and Anisotropic Diffusion (AD). These methods calculate the movement speed of the storms using optical flow methods, and then determine their positions based on the elapsed time between instruments. Next, we conducted a quantitative assessment of the performance of these three methods based on the consistency of storm morphology and rainfall rates. Our results demonstrate that all three advection correction methods effectively reduce the discrepancies in morphology and rainfall rate among multi-source data. Without correction, the Coincidence Rate (CR) and Structural Similarity (SSIM) were 30.96% and 0.689 in the US and 29.44% and 0.670 in China, respectively. In comparison, applying the LK, VET, and AD methods increased those indices to 32.94%, 32.72%, 32.85% and 0.718, 0.715, 0.716 in the US, and 31.34%, 31.17%, 31.24% and 0.696, 0.694, 0.693 in China, respectively. The rainfall rate inconsistencies were also effectively reduced after advection correction. The performances among the three methods were similar. Overall, the LK method performed slightly better than AD, followed by VET. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050782 |
