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\"This book is based on authors' extensive involvement in large Synthetic Aperture Radar (SAR) mapping projects, targeting the health of an important earth ecosystem, the tropical forests. It highlights past achievements, explains the underlying physics that allow the radar practitioners to understand what radars image, and can't yet image, and paves the way for future developments including wavelet-based techniques to estimate tropical forest structural measures combined with InSAR and Lidar techniques. As first book on this topic, this composite approach makes it appealing for students, learning through important case studies ; and for researchers finding new ideas for future studies\"-- Provided by publisher.

Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3${\\mathrm{m}}^{3}$on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%$95\\%$ . It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards. Plain Language Summary Steep mountains and hills produce dangerous rockfalls and similar phenomena such as landslides and debris flows. A major collapse is typically preceded by a series of rockfalls over days or months. It is therefore crucial to reliably detect these events and recognize the warning signs of an impending major collapse. When rocks bounce on the ground they release seismic waves, which generate vibrations that propagate long distances. Such vibrations stretch and compress fiber optic cables within the ground enough so they can be measured with a novel technique called Distributed Acoustic Sensing (DAS). Here we show how to identify such DAS signals using machine learning algorithms to detect precursory rockfall activity and a major collapse on a slope in Switzerland. We compare our detections with radar measurements, which are highly reliable but also come at a greater cost for installation. Since we can apply DAS to unused fiber within the ground, our approach may pave the way for the next generation of natural hazard warning. Key Points A semi‐supervised neural network is developed for rock‐slope failure monitoring with Distributed Acoustic Sensing at Brienz, Switzerland Our model achieves over 95% precision for rock slope failures detected by a Doppler radar system over 45 days The sustained detection of slope failures before the major collapse highlights the potential of our approach for early warning

Precipitation nowcasting plays a vital role in preventing meteorological disasters, and Doppler radar data act as an important input for nowcasting models. When using the traditional extrapolation method it is difficult to model highly nonlinear echo movements. The key challenge of the nowcasting mission lies in achieving high-precision radar echo extrapolation. In recent years, machine learning has made great progress in the extrapolation of weather radar echoes. However, most of models neglect the multi-modal characteristics of radar echo data, resulting in blurred and unrealistic prediction images. This paper aims to solve this problem by utilizing the features of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling, and design the radar echo extrapolation model GAN–argcPredNet v1.0. The model is composed of an argcPredNet generator and a convolutional neural network discriminator. In the generator, a gate controlling the memory and output is designed in the rgcLSTM component, thereby reducing the loss of spatiotemporal information. In the discriminator, the model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and it thus has a more powerful discrimination ability. Through experiments on a radar dataset from Shenzhen, China, the results show that the radar echo hit rate (probability of detection; POD) and critical success index (CSI) have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet under different intensity rainfall thresholds, and the false alarm rate (FAR) has decreased by an average of 17.9 %. We also found a problem during the comparison of the result graph and the evaluation index. The recursive prediction method will produce the phenomenon that the prediction result will gradually deviate from the true value over time. In addition, the accuracy of high-intensity echo extrapolation is relatively low. This is a question worthy of further investigation. In the future, we will continue to conduct research from these two directions.

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data.

We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time series of rockfall events with volumes of 1 m3 and greater since 2018 and serves as automatic traffic control for an important main road. In the period between January 2018 and October 2022, 6743 events were detected, which is 2 orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow area, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that, due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings.

Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events. Plain Language Summary Tornadoes, highly destructive small‐scale weather phenomena, demand accurate detection for informed decision‐making. Although meteorological radars are commonly utilized for tornado identification, current methods often suffer from false alarms or missed detections due to radar noise. In this study, we introduce the multi‐task learning‐based identification network (MTI‐Net), which not only enables tornado detection but also estimates tornado counts within radar data. We integrate Convolutional Neural Networks (CNNs) with Transformer techniques to enhance the model's ability to capture tornado information. CNNs detect local details using filters, while Transformers manage global connections through attention mechanisms. A series of experiments demonstrate significant improvements in tornado detection with MTI‐Net compared to traditional methods. Key Points A tornado dataset with detailed radar features was created over China from 2017 to 2023 Multi‐task learning was designed to simultaneously infer tornado detection and tornado number estimation Integration of spatial and channel attention units can better extract tornado features from radar data

The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts.

The medium‐scale traveling ionospheric disturbances (MSTIDs) can be excited by many sources. Among those magnetic reconnection has been proposed as a potential driver for dayside MSTIDs, but direct evidence has been limited. Using ground‐based radar data from the Super Dual Auroral Radar Network on 14 December 2012, we observed quasi‐periodic multiple MSTIDs propagating from auroral latitudes to mid latitudes near magnetic local noon, which showed one‐to‐one correspondences to intermittent lobe reconnections with periodicities of about 20–30 min. Simultaneous EISCAT Svalbard incoherent scatter radar data revealed enhanced electric field and Joule heating within the cusp region following each lobe reconnection. These multi‐instrument observations strongly suggest lobe reconnection as a possible driver for the dayside MSTID. Plain Language Summary Medium scale traveling ionospheric disturbances (MSTIDs) are wave‐like disturbances in the ionosphere, with wavelengths typically spanning a few hundred to a thousand kilometers. Dayside MSTIDs have been proposed to link with magnetospheric processes, such as magnetic reconnection in the cusp region, though direct observational evidence of this connection has been limited. Using coordinated DMSP SSUSI auroral images with EISCAT and SuperDARN radar observations, we establish a clear correlation between lobe reconnection and dayside MSTID under sustained northward interplanetary magnetic field conditions, providing evidence that MSTID can indeed be triggered by energy input during lobe reconnection. Key Points One‐to‐one correspondences between lobe reconnections and Equatorward Moving Structures observed at sub‐auroral latitude were identified The Equatorward Moving Structures were confirmed to be the medium scale traveling ionospheric disturbances (MSTIDs) The observations suggest lobe reconnection as a possible source for the generation of dayside MSTID

Doppler weather radars play a pivotal role in meteorology, providing critical data for monitoring severe weather phenomena, such as thunderstorms. However, Doppler velocity measurements are subjected to aliasing errors when the true velocity exceeds the radar’s maximum detection velocity, compromising the accuracy of velocity data. Effective dealiasing techniques are essential to correct these errors and improve data, leading to reliable data assimilation and therefore improved numerical weather prediction (NWP) as well as nowcasting applications. In this study, an attempt is made to present a comparative study of four dealiasing algorithms—convolution-, expansion-, amplitude correction-, and sine-based algorithms—to assess their effectiveness in processing Doppler radar velocity data. The study aims to evaluate these algorithms based on their ability to correct aliasing errors, their computational efficiency, and their practical applicability in real-world meteorological scenarios. Through an experimental evaluation, the performance of each algorithm is analyzed. Results indicate varying degrees of effectiveness among the algorithms, highlighting their respective strengths and limitations in dealing with the velocity aliasing of radar data. It was found that the Amplitude Correction and Convolution algorithms outperformed the others in correcting aliasing. A combined multi-algorithm approach achieved the highest overall accuracy when compared to manually corrected reference data and other algorithms. This research contributes to advancing the understanding of radar data processing techniques and provides insights into optimizing dealiasing strategies for enhanced meteorological forecasting and nowcasting, as well as severe weather prediction.

In small- to medium sized river basins, flood forecast accuracy and adequate lead times are especially important for the scheduling of catchment management decisions, involving flood prevention measures and disaster mitigation. For this study, the Zhanghe River Basin in China was selected as the study area. A meteorological–hydrological coupled model, which linked the Weather Research and Forecasting (WRF) model to the WRF-Hydro model was used with Doppler radar data, including radial velocity and reflectivity in the S-band, to explore the influence of data assimilation frequency on rainfall and runoff forecasts, as well as the differences in error propagation characteristics between meteorological and hydrological models. The results were as follows: (1) Doppler radar data assimilation has the ability to improve the temporal and spatial variability of rainfall forecasts. Appropriate data assimilation show positive effect on improving the rainfall forecast. 3 h assimilation intervals data assimilation may result in over-estimating under the influence of complex topography in Zhanghe River Basin. The rainfall forecast results based on 6 and 12 h assimilation intervals were more accurate than those derived from a 3 h interval, with the average cumulative rainfall errors being reduced by 44.86% and 53.26%, respectively. (2) Rainfall forecasts have a significant impact on the accuracy of subsequent runoff forecasts. The runoff results showed that the assimilation of radar data at higher frequencies does not guarantee the further improvement of the runoff simulations due to the overestimation of forecast rainfall. The average flood peak error under the 6 and 12 h assimilation intervals was 27.52% and 20.0%, respectively, less than that using the 3 h interval. Therefore, the effective information contained in the assimilation data is more important than the amount of data. (3) Error propagation between models differs with the changing assimilation frequency of the radar data and the consequent effect of the rainfall forecast. With the increase in assimilation frequency of the radar observations, the error range increases. Compared with the rainfall errors, the runoff errors show greater variability. Through quantitative analysis, it was found that there is no well-defined linear relationship between the rainfall and runoff errors. At the same time, the potential of radar data assimilation is discussed, and some suggestions for improvement are put forward.