摘要 :
A simulator of weather radar signals can be exploited as a useful reference for many applications, such as weather forecasting and nowcasting models or for training artificial intelligence systems designed to optimize the trajecto...
展开
A simulator of weather radar signals can be exploited as a useful reference for many applications, such as weather forecasting and nowcasting models or for training artificial intelligence systems designed to optimize the trajectory of aircrafts with the purpose to reduce flight hazard and fuel consumption. However, before being used, it must be accurately examined under different operating conditions, in order to evaluate the consistency of the outputs produced. In this paper, we present a validation procedure for a newly developed polarimetric weather radar simulator (POWERS). The goal is to assess the ability of the simulator to deal with any kind of input data, be they simulated and real raindrop-size distributions, or outputs generated by numerical weather prediction models. Three different approaches are proposed, each providing a connection between meteorological inputs and the radar observables simulated by POWERS. The analysis is carried out in the case of rainfall, both at S- and X-bands.
收起
摘要 :
We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can disting...
展开
We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can distinguish among precipitation particles of different shapes, compositions, and orientations. A relatively simple electromagnetic idea resulted in the 2012 upgrade of the National Weather Service (NWS) network of 160 high-resolution Doppler weather radars in the United States, with dual-polarization technology. We describe the details of the Colorado State University (CSU)–CHILL research radar, featuring exceptional polarization purity and dual-frequency operation, and its setup and role in winter field experiments in Colorado.
收起
摘要 :
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 migratio...
展开
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.
收起
摘要 :
This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in elec...
展开
This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the co polar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.
收起
摘要 :
Weather radar technologies and instrumentation play a vital role in early warning of severe weather. For example, the annual impacts of adverse weather on the U.S. national highway system and roads are staggering: 7,400 weather-re...
展开
Weather radar technologies and instrumentation play a vital role in early warning of severe weather. For example, the annual impacts of adverse weather on the U.S. national highway system and roads are staggering: 7,400 weather-related deaths and 1.5 million weather-related crashes [1]. In addition, US$4.2 billion is lost each year as a result of air traffic delays attributed to weather. Research on high-impact weather is broadly motivated by society's need to improve the prediction of these weather events. The research approaches to accomplish this goal vary significantly with the inherent predictability of the weather system. For example, the current forecast approaches for issuing warnings of short-lived events, such as tornadoes and flash floods, are primarily based on observations with a focus on advanced Doppler radar measurements.
收起
摘要 :
Rain, snow, and volcanic ash clouds contain particles generated by different physical and chemical processes. When electromagnetic radiation interacts with particle distribution, causing absorption and scattering, the backscattere...
展开
Rain, snow, and volcanic ash clouds contain particles generated by different physical and chemical processes. When electromagnetic radiation interacts with particle distribution, causing absorption and scattering, the backscattered power enables the retri
收起
摘要 :
This article proposes a method for estimating the Doppler power spectrum (DPS) of a weather radar via minimum mean square error (MMSE). In order to detect severe weather phenomena that mostly occur within the lowest few kilometers...
展开
This article proposes a method for estimating the Doppler power spectrum (DPS) of a weather radar via minimum mean square error (MMSE). In order to detect severe weather phenomena that mostly occur within the lowest few kilometers of the atmosphere, weather radars have to direct their beams at low elevation angles, and the received signals from such observations usually contain reflections from the ground and buildings, so-called ground clutter. The MMSE estimator, which is an adaptive spectral estimator, allows weather radar DPSs to be obtained with excellently reduced ground clutter contaminations. The MMSE estimator was examined by numerical simulations, which supposed various precipitation and ground clutter scenarios and DPS estimation parameter values. The MMSE estimator provided DPSs almost as accurate as those from the traditional Fourier and windowed Fourier estimators in simulations with no ground clutter and much better DPSs than those estimators in the presence of ground clutter. Furthermore, the MMSE estimator gave better suppression of ground clutter contamination than the Capon estimator, which is another adaptive spectral estimator. As a result of statistical evaluations, ground clutter signals with a strong clutter-to-noise ratio of 70 dB appeared only in a narrow velocity range of the MMSE DPS, from −2 to 2 m/s, and caused degradation of the mean and standard errors outside this velocity range by just 1 dB. The MMSE estimator was also applied to signals received by actual weather radar, and DPS estimation of precipitation signals with similarly low ground clutter contamination was demonstrated.
收起
摘要 :
During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars ha...
展开
During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source parameters. We extend this capability to include an early-warning detection scheme within the overall volcanic ash radar retrieval methodology. This scheme, called the volcanic ash detection (VAD) algorithm, is based on a hybrid technique using both fuzzy logic and conditional probability. Examples of VAD applications are shown for some case studies, including the Icelandic Grímsvötn eruption in 2011, the Eyjafjallajökull eruption in 2010, and the Italian Mt. Etna volcano eruption in 2013. Estimates of the eruption onset from the radar-based VAD module are compared with infrasonic array data. One-dimensional numerical simulations and analytical model estimates of MFR are also discussed and intercompared with sensor-based retrievals. Results confirm in all cases the potential of MW weather radar for ash plume monitoring in near real time and its complementarity with infrasonic array for early-warning system design.
收起
摘要 :
Weather radar variables provide useful information about the characteristics and motion of hydrometeors. However, the bulk information may be masked, when the meteorological signal of interest is contaminated by clutter. The dual-...
展开
Weather radar variables provide useful information about the characteristics and motion of hydrometeors. However, the bulk information may be masked, when the meteorological signal of interest is contaminated by clutter. The dual-polarimetric spectral densities (DPSDs) may unveil additional information about the polarimetric characteristics of the groups of scatterers moving at different Doppler velocities in a given radar resolution volume. Previous DPSD estimation methods required averaging a large number of spectra (obtained from different spatial locations or times), or averaging in frequency to get accurate estimates; though by doing so, the resolution is degraded, and the important features of the meteorological phenomenon may be masked. In an attempt to overcome these limitations, the Bootstrap DPSD estimator is proposed, which allows the estimation of DPSDs from a single dwell with minimal spatial, temporal, or spectral resolution loss. The performance and the limitations of the Bootstrap and conventional DPSD estimators are assessed when identifying signals with different polarimetric signatures of scatterers moving at different radial velocities in the radar volume. The advantages of the Bootstrap DPSD estimator as a tool for the polarimetric spectral analysis are demonstrated with a few examples of polarimetric spectral signatures in data from tornado cases. It is expected that, with the Bootstrap DPSD and the polarimetric spectral analysis, it will be possible to better understand tornado dynamics and their connection to weather radar measurements, as well as to elucidate important scientific questions that motivated this paper.
收起
摘要 :
Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range m...
展开
Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radars performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25 degrees in the direction of the spacecrafts orbit.
收起
