The emerging high-altitude platform station (HAPS, such as the near-space airship) is located in the stratosphere at a height around 20 km and has great potential in remote sensing due to its insensitivity to severe weather conditions and the advantage of long dwell time over observation area of interest. Traditionally, airborne Doppler radars employ fixed-angle symmetric coplanar analysis technique (FA-SCAT) for 3-D wind field (3D-WF) retrieval in severe weather. However, FA-SCAT fails in HAPS-borne weather radar due to its low velocity (usually <15 m/s) and subsequently low spatial coverage. In this letter, a novel multiangle asymmetric coplanar analysis technique (MA-ASCAT) for 3D-WF retrieval applied to HAPS-borne phased array radar is proposed. It utilizes multiple observations of the same target bin to retrieve 3D-WF based on the multiple-angle data collected by multiple HAPSs at multiple observation positions. The MA-ASCAT performance is deduced theoretically, which is used to optimize the systematic scanning strategy. The method is validated through simulation and real data experiments. Results demonstrate that MA-ASCAT achieves superior detection coverage and accuracy compared to FA-SCAT.

Radial velocity data of Doppler weather radars are crucial for wind field retrieval and nowcasting, but multi-fold velocity aliasing in short-wavelength radar systems (e.g., X/Ku-band) severely restricts their application. Conventional velocity dealiasing methods relying on velocity continuity fail in such scenarios due to unreliable initial reference velocity determination. To resolve this challenge, the Morphology-based Zero Iso-Doppler Contour (Zero-Isodop) Extraction and Velocity Correction Algorithm (MZI-VCA) is proposed for multi-fold velocity-aliased weather radar. The method first constructs a large-scale uniform horizontal wind model to characterize aliased zero-isodop distributions, then applies morphological operations combined with Depth-First Search (DFS) for candidate isodop extraction in Plan Position Indicator (PPI) images, and finally identifies the zero-isodop through spatial structure metrics. Leveraging the extracted zero-isodop, dealiasing is performed under spatial continuity constraints. Validations using both simulated multi-fold aliasing data (derived from S-band radar) and Ku-band weather radar observations demonstrate that MZI-VCA achieves superior performance compared to the traditional dealiasing method UNRAVEL. MZI-VCA achieves 100% dealiasing success rate in correcting double- and triple-fold aliasing, with a success rate of 90.74% across all diverse multi-fold scenarios tested in 108 simulated volume scans. In contrast, UNRAVEL fails to resolve multi-fold aliasing, exhibiting only a 6.92% Probability of Detection (POD). The systematic method establishes a new framework for resolving multi-fold velocity aliasing in short-wavelength radar systems.

Animal migration poses risks to human health and economic stability, highlighting the need for effective monitoring. Weather radars are essential tools for monitoring migratory insects and birds. While S-band radars can accurately distinguish insect and bird echoes, X-band radar, offering higher resolution, has not been sufficiently explored, limiting its use in aerial ecological monitoring. In this paper, joint observational experiments were conducted to evaluate insect and bird echoes from S-band and X-band weather radars. The results show significant overlap in the polarization features on X-band radar, making existing algorithms unsuitable for X-band data. To address this issue, a point-surface feature fusion method is proposed. This approach extracts polarization variables to construct point-scale features for initial classification with statistical models. A residual network captures surface-scale morphological features, which are integrated with the point-scale recognition results. Finally, a feature fusion module generates the final classification. The method achieves a mean intersection-over-union (mIoU) of 84.56% and demonstrates high accuracy and robustness in historical data tests. This study enhances X-band radar’s ability to differentiate between insect and bird echoes, providing a new solution for aerial ecological monitoring.

The global climate change has led to a sharp decline in the number and species diversity of aerial migratory animals. Breakthroughs in the field of ecological monitoring using weather radar enable large-scale and long-term ecological monitoring. However, the primary challenges are the compression of spectral details in base data products, which leads to a loss of scatterer-information, and bias caused by frequency offset in dual-polarized products, which affects classification consistency between radar sites. To address these challenges, we explored the multi-dimensional range-Doppler (RD) characteristics of migration traits from Level-I IQ data and proposed an echo classification method for insect and bird of weather radar. We employ adaptive linear filtering for clutter preprocessing, followed by morphological image process to extract biological connected domains, leveraging spectral feature differences between insects and birds. Subsequently, a hierarchical classifier model is developed for classification, complemented by a minimal value inflection points detection method to identify insect-bird coexistence. Our approach is implemented to support the large-scale monitoring of aerial animal migration in the network of S-band weather radar stations. Experiments conducted with five operational weather radars have comprehensively validated the benefits of the proposed method in the accurate classification of insect and bird echoes. Future work will concentrate on precise species identification and biological quantification within resolution volumes.

Predicting aerial animal migration is of great significance for biological research, ecological conservation, and agricultural production. The mechanism of animal migration is deeply coupled with spatiotemporal and meteorological factors. However, the existing large-scale prediction models using weather radar isolate the spatiotemporal characteristics and the meteorological factors. Additionally, their long-term prediction capabilities are limited, posing challenges in accurately forecasting long-term migration patterns to support applications, such as ecological warnings. This article introduces an aerial migration prediction neural network model combining multiple meteorological factors with weather radar data while expanding the horizon of the migration forecast to the scale of 7 days. Differentiated feature extraction methods are applied to different meteorological factors in the network. The transfer characteristics of the wind field in 2-D space are used to construct a dynamic migration model. The scalar meteorological data are encoded by entity embedding to perform feature fusion with the dynamic branch, collectively forming the forecast model that outputs future migration intensity. We validate the effectiveness of our model China weather radar network real data and reanalysis data, accurately forecasting migratory biomass within China for a horizon of up to 7 days. Moreover, our model is compared with two existing prediction models, demonstrating a maximum improvement of 14.00% in the coefficient of determination ($R^{2}$) in long-term forecast, and the visualized results highlight the predictive effectiveness for the spring and autumn seasons. In future applications, more meteorological factors should be considered and radar data from more stations should be collected to enhance the dataset.

Accurately extracting biological echoes is a fundamental prerequisite for weather radar aeroecology monitoring. However, the concurrent presence of meteorological echoes and biological echoes greatly restricts the extraction accuracy. Traditional neural network-based echo extraction algorithms rely on the spatial continuity feature of the echoes. But, the concurrent presence of multiple types of echoes will lead to the invalidation of the spatial feature and the error of boundary identification of biological echoes. To address this challenge, this study proposes a weak biological echo extraction algorithm using a superpixel technique, aimed at preserving richer biological details in adverse weather conditions. To amplify the imaging distinctions between biological and meteorological components, we design 8-D differential features for each superpixel patch on the CIELAB color space. The gradient boosting tree model is trained for biology classification in handling complex data scenarios. Trained trees exhibit strong generalization capabilities and imbalanced testing data that reflect real weather conditions. To mitigate the limitations posed by the lack of publicly available datasets, we establish a trainable weather radar image dataset encompassing typical weather conditions across national weather radar stations. Experimental results validated that the algorithm retains over 98% of biological data under adverse weather conditions.

Weather radar serves as a crucial tool for monitoring aeroecology by enabling the observation of migrating birds and insects. Although dual-polarization weather radar offers the possibility of classifying echoes, extracting migration echoes of birds and insects from historical single-polarization weather radar data remains challenging. The current deep-learning methods have been successfully extracting aerial migrations from single-polarization weather radar data. However, it still faces challenges in distinguishing between birds and insects at the pixel level, primarily due to the absence of distinct semantic features for each. To tackle this challenge, we propose a semi-supervised radar data processing framework, which generates a large number of single polarization training datasets from a small amount of dual polarization truth data and trains the image segmentation network of single polarization data to distinguish between bird and insect echoes. The framework comprises three components: an image classifier, an image generator, and an image segmentation model. Specifically, the image classifier and image generator leverage a small set of manually annotated dual-polarization radar data to generate the pixel-level single-polarization dataset for training the image segmentation model. The well-trained image segmentation model extracts migration echoes of birds and insects from radar images. Experimental results demonstrate that the proposed method achieves a mean intersection over union (IoU) of 97% for segmenting precipitation, bird, and insect targets. The proposed framework can utilize historical archived single-polarization weather radar data to provide large-scale, long-term, and repeatable monitoring data for birds and insects.

Communal roosts are essential stopover sites for migratory birds. Monitoring the diurnal activity patterns of birds in communal roosts (DAPBCRs) is crucial for understanding their migratory behavior and ecological needs. This information is crucial for guiding habitat conservation and management strategies and assessing the impact of environmental changes on bird populations. Traditional methods for extracting DAPBCR often rely on detecting high reflectivity factor arc features generated by birds collectively leaving or returning to communal roosts using weather radar data and deep learning target detection algorithms. However, many bird echoes do not produce these high reflectivity factor arc features, making pixel-level extraction of DAPBCR challenging. To address this, we propose a method for DAPBCR extraction based on the differences in the probability distribution function (pdf) of differential backscattering phase between birds and insects. This method first removes nonbiological echoes from polarimetric weather radar data, retaining only biological echoes. By calculating the differential backscattering phase using the differential phase and system differential phase, we obtain the pdf of the differential backscattering phase for biological echoes. We fit this pdf to a mixed von Mises distribution to obtain the PDFs for birds and insects. Using posterior probabilities for birds and insects, we estimate the bird-insect mixing ratio and further estimate the number of birds by combining the reflectivity factor and mean radar cross section (RCS) of birds. Applying the proposed method, we extracted DAPBCR data in the midsection of the Huai River Basin near Fuyang City from June to October 2021. We found that bird activity peaked in August and September. Based on normalized cumulative bird activity, we estimated the start, peak, and end times of DAPBCR to be July 6, August 19, and September 30, respectively.

Monitoring and forecasting aerial animal migration benefit biological conservation, aviation safety, and agricultural production. Due to the lack of large-scale observation data and quantitative knowledge of aerial animal migration mechanisms, it is difficult to build a numerical simulation system for migration prediction. However, the extensive deployment of weather radars makes it possible to obtain large-scale aerial migration information. Meanwhile, artificial intelligence technologies provide new insights into the modeling of complex system. In this article, we develop a deep-learning model to predict aerial migration from the perspective of spatio-temporal evolution. Specifically, an undirected graph is applied to describe the geographic structure of the weather radar network, and then graph convolution and gated recurrent unit (GRU) are combined to extract spatio-temporal features of migration information. In addition, a multi-head self-attention mechanism is applied to enhance long-term dependence. Experiments are conducted to validate the effectiveness of the proposed model on the data from the Chinese weather radar network. The results show that our model can achieve state-of-the-art performance among the competing methods. Moreover, improvements from graph convolution and multi-head self-attention are also analyzed. In future applications, more weather radar data will be collected to enrich the dataset and build an aerial migration monitoring and prediction system.

Quantifying the distribution of the aerial organisms is essential for investigating the movement and behavior of migratory animals. This large-scale broad-front migration can be readily detected by weather radars. However, estimating their vertical distribution is still biased due to the vertical variability of the reflectivity in the radar beam. In this article, we establish a weather radar biological observation model and propose a retrieval method to identify the vertical profiles of reflectivity (VPRs) using regularization technique, which can eliminate the estimation bias. The performance of the method is evaluated using different radar antenna patterns and different regularization parameters, and a sensitivity analysis is performed. The improvement of the method is represented by comparing to the direct method. We apply this method to autumn migration cases over the east coast of China; the demonstration results show the potential of this method in the study of migratory animals.

This paper studies echo simulations of spaceborne millimeter-wave cloud profiling radar (CPR). It employs the cloud reflectivity data from 94GHz CloudSat CPR to construct the real scene and the cloud scattering model. In the simulation, the radar model, the relative motion model, the cloud scattering model, the scattering particle motion model and the atmospheric propagation and attenuation model are established considering the characteristics of the millimeter-wave cloud radar. The US standard atmosphere mode is adopted to calculate the profile of atmospheric attenuations at the different heights. The System Tool Kit (STK) is used to generate the relative geometry and movement. Finally, all the aforementioned models are combined to construct the radar echoes according to the Doviak-Zrnic meteorological echo formulas. The simulation results are verified by comparing input simulation true values and experimental inversion values, and it shows a good consistence.

Since geosynchronous synthetic aperture radar (GEO SAR) has curved trajectories, back-projection algorithm (BPA) greatly fits for its imaging. However, for a scene with height variation, the reference range based on the fixed-height imaging grid under curved trajectories is inaccurate in azimuth back-projection. Resultantly, the GEO SAR image quality will be obviously deteriorated in high resolution imaging. To address the issue, this paper proposed the digital elevation model (DEM)-assisted BPA to realize the accurate high resolution GEO SAR imaging for the scene with height variation. DEM information is utilized to construct the imaging grid in the new method for generating the accurate reference range. Simulation results validate that the proposed method achieves good imaging performance for the scene with height variation.