G-Band Radar for Humidity and Cloud Remote Sensing Cooper, Ken B.; Roy, Richard J.; Dengler, Robert ...
IEEE transactions on geoscience and remote sensing,
02/2021, Letnik:
59, Številka:
2
Journal Article
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VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated ...continuous-wave (FMCW) radar mode, offering a transmit power of 200-300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167-174.8 GHz results in a nominal range resolution of 15 m. The radar's measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as −26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR's absolute sensitivity was validated using a spherical metal target in the radar antenna's far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver.
Precipitation nowcasting pertains to the localized forecasting of rainfall over a brief time horizon, characterized by precise estimates of both coverage and intensity. This capability holds ...particular significance in various societal applications, including agriculture, aviation safety, and transportation. However, since traditional methods mainly by extrapolating radar echo in 2-D space, cannot accurately and sufficiently represent the spatiotemporal state of clouds in the vertical direction, the accuracy of precipitation nowcasting using weather radar has reached a bottleneck. A new deep learning precipitation nowcasting model called 3dCloudNet is designed and evaluated in this study. The 3dCloudNet incorporates historical 3-D radar echo sequences obtained from weather radar data to improve the accuracy and reliability of precipitation nowcasting. By capturing both horizontal and vertical motion patterns of clouds at various altitude levels, this model demonstrates an enhanced capability in detecting and distinguishing regions prone to severe convective weather events. The experimental results show that the model better captures the cloud's motion patterns and trends, and therefore has a noteworthy ability to detect and distinguish areas that may lead to severe convective weather. This study provides a step toward further improving the accuracy of precipitation nowcasting.
In the context of escalating frequency spectrum congestion, the prevalence of radio frequency interference (RFI) poses a growing challenge for weather radars, compromising data quality and adversely ...affecting the accuracy of variable estimations. This paper proposes a novel algorithm for the separation of precipitation and RFI, based on Low-Rank Sparse Decomposition (LRSD). When precipitation and RFI overlap, this method effectively filters out RFI while minimizing its impact on precipitation by analyzing the property difference between precipitation and RFI in the time domain. The proposed algorithm operates on the assumption that precipitation exhibits low-rank properties, whereas RFI manifests as sparse signals. This assumption is grounded in the observation that RFI in weather radars is typically unintentional, occupying a limited number of pixels in the range-time power image, while precipitation demonstrates approximate stationarity with a slow speed within a coherent processing interval (CPI). This algorithm is intended to alleviate the adverse effects of RFI, and the efficacy of the approach is validated using data collected by polarimetric Doppler weather radar systems at the Royal Netherlands Meteorological Institute. This paper systematically evaluates the impact of the proposed LRSD method in comparison to two traditional RFI mitigation strategies(e.g., the Vaisala-3 method and 2D filter) on the quality of meteorological data. The results demonstrate that the proposed LRSD method outperforms the alternatives in terms of RFI suppression and precipitation retention performance. At present, the LRSD method loses phase information, which may impact the accuracy of measuring polarization parameters of precipitation.
This contribution presents the results of a dual-polarized radiating element designed to achieve low cross-polarization (lower than -40 dB measured for the E-and H-plane, at least -30 dB in the ...D-plane based on simulations) and large fractional bandwidth (18%) over wide scanning angles (±60°). The proposed design includes multiple features that enable high isolation between ports, reduction of spurious radiation, highly symmetrical radiated fields, and suppression of diffracted fields between contiguous subarray gaps. To verify the polarimetric requirements for a weather radar, simulated and measured results, including electronic scanning of the array and embedded element patterns of the antenna, are shown.
Abstract
Meteorological radar technology has a very important application in power grid disaster prevention and mitigation. Radar technology is used to measure related disaster information, so that ...people can better take preventive measures and reduce the impact and loss of disasters on the power grid. Using the detection data of meteorological radar, the types of severe weather are automatically identified and divided, and objective short-term nowcasting and forecasting are automatically carried out. The generated early warning products are organically combined with the geographic information system to realize the accurate forecast of short-term severe weather, and accurately locate the power grid equipment through the spatial analysis service engine.
Fireworks disturbance across bird communities Hoekstra, Bart; Bouten, Willem; Dokter, Adriaan ...
Frontiers in ecology and the environment,
February 2024, 2024-02-00, 20240201, Letnik:
22, Številka:
1
Journal Article
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Fireworks are important elements of celebrations globally, but little is known about their effects on wildlife. The synchronized and extraordinary use of fireworks on New Year's Eve triggers strong ...flight responses in birds. We used weather radar and systematic bird counts to quantify how flight responses differed across habitats and corresponding bird communities, and determined the distance‐dependence of this relationship. On average, approximately 1000 times as many birds were in flight on New Year's Eve than on other nights. We found that fireworks‐related disturbance decreased with distance, most strongly in the first five kilometers, but overall flight activity remained elevated tenfold at distances up to about 10 km. Communities of large‐bodied species displayed a stronger response than communities of small‐bodied species. Given the pervasive nature of this disturbance, the establishment of large fireworks‐free zones or centralizing fireworks within urban centers could help to mitigate their effects on birds. Conservation action should prioritize avian communities with the most disturbance‐prone, large‐bodied bird species.
Abstract
Nowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but ...none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.
Typhoon is one of the major hazards in ocean coastal areas, but traditional techniques are inadequate to monitor typhoons due to limited or high‐cost observations, like radio sounding and ...meteorological radar. Previous studies have found that typhoons can cause ionospheric disturbances, but the relationship and characteristics are still unclear. In this paper, about 400 stations observations of the Global Positioning System (GPS) network in Taiwan are used to extract ionospheric disturbances during multiple typhoons. The detailed characteristics of the ionospheric disturbances are investigated using a fourth‐order Butterworth filter following the 2016 Nepartak, 2019 Lekima, 2019 Mitag, and 2020 Hagupit typhoons. The results show that significant ionospheric disturbances were observed during the typhoons, and the larger disturbances are mostly located 400–1200 km far from the typhoon eye. The estimated horizontal propagation velocity of the ionospheric disturbances is about 127–194 m/s. The locations of the ionospheric disturbances between the typhoon eye and the landfall site are related to the typhoon path. The azimuth distribution of the ionospheric disturbance around the typhoon eye is affected by the GPS elevation angles. At 500–700 km from the typhoon eye, the mean ionospheric disturbances are 0.17 TECU (TEC Unit) and 0.15 TECU for super typhoon Nepartak and Lekima, and 0.13 TECU and 0.18 TECU for typhoon Mitag and Hagupit. The higher the intensity of the typhoon is, the greater the magnitude of the ionospheric disturbance is.
Key Points
Significant ionospheric disturbances are observed following multiple typhoons from GPS‐derived Total Electron Content data
The directivities of positive and negative ionospheric disturbances are found in different azimuths
The magnitude of ionospheric disturbances is related to the typhoon intensity with a positive proportion
Information of aerodynamic parameters of volcanic ash particles, such as
terminal velocity, axis ratio, and canting angle, are necessary for
quantitative ash-fall estimations with weather radar. In ...this study,
free-fall experiments of volcanic ash particles were accomplished using a
two-dimensional video disdrometer under controlled conditions. Samples containing a rotating symmetric axis were selected and divided into
five types according to shape and orientation: oblate spheroid with
horizontal rotating axis (OH), oblate spheroid with vertical axis (OV),
prolate spheroid with horizontal rotating axis (PH), prolate spheroid with
vertical rotating axis (PV), and sphere (Sp). The horizontally (OH and PH)
and vertically (OV and PV) oriented particles were present in proportions of
76 % and 22 %, and oblate and prolate spheroids were in proportions of
76 % and 24 %, respectively. The most common shape type was OH (57 %). The terminal velocities of OH, OV, PH, PV, and Sp were obtained analyzing
2-D video disdrometer data. The terminal velocities of PV were highest compared to those of
other particle types. The lowest terminal velocities were found in OH
particles. It is interesting that the terminal velocities for OH decreased
rapidly in the range 0.5<D<1 mm, corresponding to the
decrease in axis ratio (i.e., smaller the particle, the flatter the shape).
The axis ratios of all particle types except Sp were found to be converged
to 0.94 at D>2 mm. The histogram of canting angles followed unimodal and bimodal distributions
with respect to horizontally and vertically oriented particles,
respectively. The mean values and the standard deviation of entire particle
shape types were close to 0 and 10∘, respectively,
under calm atmospheric conditions.
Dual‐frequency dual‐polarization radar observations of the melting of two ice populations in a stratiform rainfall event are presented. The observed phenomenon occurs as a two‐layer linear ...depolarization ratio (LDR) signature in a single radar bright band. Doppler spectra observations show that the upper LDR layer is caused by the melting of ice needles, potentially generated by the rime‐splintering process, while the lower one is mainly due to the melting of background ice particles formed at the cloud top. The melting signal of small needles acts as a unique benchmark for detecting the onset of melting and is used to verify the current methods for the identification of melting layer boundaries. The radar‐derived characteristics of the melting layer are found to be dependent on the radar variable and frequency used. The implications of the presented findings for radar‐based studies of precipitation properties in and above the melting layer are also discussed.
Plain Language Summary
The melting layer of precipitation is a relatively narrow layer where snowflakes melt into raindrops. This layer is clearly visible in weather radar observations and is often called the “ bright band” because of the stronger radar return coming from this region. In this paper, we present an event where two layers of melting ice particles, as indicated by dual‐polarization radar observations, are present inside one bright band. Using state‐of‐the‐art radar observations, we show that this phenomenon is caused by the melting of two ice particle populations. The upper layer is due to the melting of ice needles that are potentially generated by the rime‐splintering process, and the second layer is caused by snowflakes that have formed at the cloud top. The measurements presented illustrate the complexity of ice clouds, where several populations of ice particles might coexist and evolve. We show how the multifrequency radar observations that are currently becoming more and more prevalent can be used to get a glimpse into the precipitation processes taking place in and above the melting layer. We also discuss how currently used methods to describe melting layer properties may be biased depending on what radar observations are used.
Key Points
Two‐layer LDR signature is observed by vertically pointing W‐ and C‐band radars in a melting layer of precipitation
Doppler spectra reveal that these layers are caused by the melting of needles, potentially generated by rime‐splintering, and background ice
The melting signal of small needles is used to evaluate current methods for the identification of melting layer boundaries
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