Dynamic Inversion Algorithm for Rainfall Intensity Based on Dual-Mode Microwave Radar Combined Rain Gauge

ZHANG Qishuo; ZHANG Wenxin; GAO Mengyu; XIONG Fei

doi:10.11999/JEIT250535

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ZHANG Qishuo, ZHANG Wenxin, GAO Mengyu, XIONG Fei. Dynamic Inversion Algorithm for Rainfall Intensity Based on Dual-Mode Microwave Radar Combined Rain Gauge[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250535

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ZHANG Qishuo, ZHANG Wenxin, GAO Mengyu, XIONG Fei. Dynamic Inversion Algorithm for Rainfall Intensity Based on Dual-Mode Microwave Radar Combined Rain Gauge[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250535

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Dynamic Inversion Algorithm for Rainfall Intensity Based on Dual-Mode Microwave Radar Combined Rain Gauge

doi: 10.11999/JEIT250535 cstr: 32379.14.JEIT250535

ZHANG Qishuo^{2, 2},
ZHANG Wenxin^{2, 2
,
,},
GAO Mengyu^{2, 2},
XIONG Fei²

1.
College of Automation, Beijing Information Science and Technology University, Beijing 100192, China
2.
Department 21, Unit 32272, PLA, Chengdu 610214, China

Funds: The Youth Fund of the National Natural Science Foundation of China (62406032), Beijing Natural Science Foundation (4242036)

Received Date: 2025-06-09
Rev Recd Date: 2025-09-03

Available Online: 2025-09-21

Abstract

Abstract

Objective Microwave meteorological radar has broad application potential in rainfall detection due to its non-contact measurement, high spatiotemporal resolution, and multi-parameter retrieval capability. However, in the context of climate change, increasingly complex rainfall events require monitoring systems to deliver high-precision, multi-dimensional, real-time data to support disaster warning and climate research. Conventional single-mode radars, constrained by fixed functionalities, cannot fully meet these requirements, which has led to the development of multi-mode radar technology. The dual-mode radar examined in this study employs Frequency Modulated Continuous Wave (FMCW) and Continuous Wave (CW) modes. These modes adopt different algorithmic principles for raindrop velocity measurement: FMCW enables spatially stratified detection and strong anti-interference performance, whereas CW provides more accurate measurements of raindrop fall speed, yielding integral rainfall information in the vertical column. Despite these advantages, retrieval accuracy remains limited by the reliance of traditional algorithms on fixed empirical parameters, which restrict adaptability to regional climate variations and dynamic microphysical precipitation processes, and hinder real-time response to variations in rain Drop Size Distribution (DSD). Ground rain gauges, by contrast, provide near-true reference data through direct measurement of rainfall intensity. To address the above challenges, this paper proposes a dynamic inversion algorithm that integrates dual-mode (FMCW–CW) radar with rain gauge data, enhancing adaptability and retrieval accuracy for rainfall monitoring. Methods Two models are developed for the two radar modes. For the FMCW mode, which can retrieve DSD parameters, a fusion algorithm based on Attention integrated with a double-layer Long Short-Term Memory (LSTM) network (LSTM–Attention–LSTM) is proposed. The first LSTM extracts features from DSD data and rain gauge–measured rainfall intensity through its hidden state output, with a dropout layer applied to randomly discard neurons and reduce overfitting. The Attention mechanism calculates feature similarity using dot products and converts it into attention weights. The second LSTM then processes the time series and integrates the hidden-layer features, which are passed through a fully connected layer to generate the retrieval results. For the CW mode, which cannot directly retrieve DSD parameters and is constrained to the reflectivity factor–Rainfall rate (Z–R) relationship (Z=aR^b), an algorithm based on the Extended Kalman Filter (EKF) is proposed to optimize this relationship. The method dynamically models the Z–R parameters, computes the residual between predicted rainfall intensity and rain gauge observations, and updates the prior estimates accordingly. Physical constraints are applied to parameters a and b during state updates to ensure consistency with physical laws, thereby enabling accurate fitting of the Z–R relationship. Results and Discussions Experimental results show that both models enhance the accuracy of rainfall intensity retrieval. For the FMCW mode, the LSTM–Attention–LSTM model applied to the test dataset outperforms traditional physical models, single-layer LSTM, and double-layer LSTM. It effectively captures the temporal variation of rainfall intensity, with the absolute error relative to observed values remaining below 0.25 mm/h (Fig. 5). Compared with the traditional physical model, the LSTM–Attention–LSTM reduces RMSE and MAE by 46% and 38%, achieving values of 0.1623 mm/h and 0.147 mm/h, respectively, and increases R² by 14.5% to 0.95 (Table 2). For the CW mode, the Z–R relationship optimized by the EKF model provides the best fit for the Z and R distribution in the validation dataset (Fig. 6). Rainfall intensity retrieved with this algorithm on the test set exhibits the smallest deviation from actual observations compared with convective cloud empirical formulas, Beijing plain area empirical formulas, and the dynamic Z–R method. The corresponding RMSE, MAE, and R² reach 0.1076 mm/h, 0.094 mm/h, and 0.972, respectively (Fig. 7; Table 4). Conclusions This study proposes two multi-source data fusion schemes that integrate dual-mode radar with rain gauges for short-term rainfall monitoring. Experimental results confirm that both methods significantly improve the accuracy of rainfall intensity retrieval and demonstrate strong dynamic adaptability and robustness.
- Microwave radar,
- Rainfall intensity,
- Long Short-Term Memory (LSTM),
- Z–R relationship,
- Extended Kalman Filter (EKF)

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References(23)

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