Weather Radar Research Articles

Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

AbstractThis study uses observations from a ground‐based instrument suite to investigate the rain‐snow level (RSL) in stratiform rainfall from January 2014 to April 2020 in the Upper Great Lakes Region. The height above the surface where ice melts to rain, the rain‐snow level, influences microphysical assumptions in remote sensing precipitation retrievals and the ability of space‐based radar to discriminate surface precipitation phase because of ground clutter. The instrument suite is installed at the Marquette, MI (MQT) National Weather Service station adjacent to Lake Superior. Rain events and the RSLs are studied through a ground‐based vertically profiling radar (Micro Rain Radar), a custom NASA‐developed video disdrometer (Precipitation Imaging Package), and reanalysis products from ECMWF ERA5 and NASA MERRA‐2. Distinct macro and microphysical characteristics are observed in precipitation events with shallow RSLs (<1.8 km above ground level [AGL]) and intermediate RSLs (>1.8 km AGL). Intermediate RSLs correspond to rain events with relatively higher rain rates and a higher concentration of small drops in the drop size distributions (DSDs). Shallow RSL DSDs contain relatively higher concentrations of large drops with lower fall speeds suggesting that partially melted snowflakes may be reaching the surface. Reflectivity‐rain‐rate relationships are also impacted by microphysical differences associated with RSL regimes. Radar‐detected RSLs agree with reanalysis‐derived melt levels‐especially at wet‐bulb isotherms of 0.5°C and 1°C. Seasonal differences such as shallow RSLs in winter, fall, and spring have subsequent implications for satellite detectability.

Merging dual-polarization X-band radar network intelligence for improved microscale observation of summer rainfall in south Sweden

Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in Scandinavia for sub-km and minute scale rainfall observations. This study develops a method for merging data from two X-WRs in Dalby and Helsingborg, southern Sweden (operated at five and one elevation angle levels, respectively) to improve the accuracy of rainfall observations. In total, 87 rainfall events from May-September 2021, observed by 38 tipping bucket gauges in the overlapping coverage of the X-WRs, were used for ground truth. The gauges were classified into four zones. An artificial neural network using doppler and dual-polarization variables (ANN) and a regression-based hybrid of RATEs (single-level rainfall products built-in to the X-WRs) based on the Marshall-Palmer equation (RMP) were calibrated for each zone. The calibrated models at 5-min scale significantly outperformed RATEs for all zones verified by Gilbert skill score (GSS), relative bias (rBIAS), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) not using the calibration data. Quantile-quantile plots confirmed a considerable improvement of the statistical distribution of the merged rainfall estimates for Zone I (closest to Dalby), II (mid-way between Dalby and Helsingborg), and IV (similar range as II for Dalby but farthest to Helsingborg) especially using ANN. Zone III (farthest to Dalby and closest to Helsingborg) was problematic for all RATEs, ANN, and RMP. The lowest-level elevation angle for both X-WRs showed the most erroneous RATEs. Consequently, the problems with Zone III can be solved if multiple levels of Helsingborg X-WR at higher levels are available.

Construal of Situational Risk and Outcomes—Exploring the Use of Weather Radar Displays with Residents of the Tampa Bay Region

Abstract A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable. Significance Statement The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.

Attenuation Correction in Weather Radars for Snow

Weather radars play a prominent role in remote sensing of the atmosphere. Various fields, such as meteorology and hydrology, rely on accurate weather radar data as input for their models. Different hydrometeors present during a weather event influence the amount of attenuation encountered by the radar signal. Attenuation correction for dual-polarization weather radar data is necessary to improve the radar products and get accurate measurements. Most of the existing attenuation correction research is associated with rain hydrometeors. Currently, research that addresses the attenuation correction of snow in weather radars is limited. Although it is known that attenuation of radar signals when it encounters rain is much greater than that for snow, attenuation for all hydrometeors needs to be addressed for accurate radar estimates. In this research work, the attenuation of different hydrometeors is studied using signal simulations. Various factors which influence attenuation, such as the elevation angle and particle size distribution, are considered, and the results are presented. An attenuation correction algorithm that uses the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm is introduced. Signal simulations are employed to obtain the relationship between specific attenuation and specific differential phase for different hydrometeors used in the proposed algorithm. The attenuation correction method is applied to X-band and Ku-band radar data. Path integrated attenuation of about 8 dB was observed in the snow case discussed from Ku band radar data. The method proposed for attenuation shows promising results at both frequency bands.

Horus—A Fully Digital Polarimetric Phased Array Radar for Next-Generation Weather Observations

Weather radars have become indispensable to meteorologists and the general public for both understanding and awareness of high-impact weather events and as part of the operational warning infrastructure. In the U.S., the operational weather radar network is composed of approximately 160 WSR-88D radars, which are S-band, dish-based, polarimetric Doppler radars. This work reports on the development of a fully digital phased array weather radar that is being used to assess the potential of such technology as a replacement for the WSR-88D radars. The “Horus” radar is a truck-based, S-band, fully digital polarimetric phased array radar. Fully digital systems hold promise for meeting some of the greatest technical challenges facing the meteorological community, such as the effective integration of dual-polarization capability with phased array beam agility. This paper describes the fully digital Horus phased array weather radar that was recently completed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU). An overview of the advantages and challenges facing fully digital arrays for weather observations is provided along with potential mitigation strategies. Initial weather observations with Horus are given with the goal of assessing the radar scanning capabilities and most importantly the polarimetric quality. Finally, a vision for the future of next-generation weather radar operations is given with an eye toward leveraging the scalable design of Horus for high-resolution weather observations.

Deep-Learning-Based Flying Animals Migration Prediction With Weather Radar Network

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 paper, 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 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.

A Conditional Generative Adversarial Network for Weather Radar Beam Blockage Correction

Missing or low-quality data regions usually happen to weather radars. One of the most common situations is beam blockage or partial beam blockage. Therefore, correction of weather radar observations that are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this article, we propose a deep learning framework based on generative adversarial networks (GANs) for restoring partial beam blockage regions in polarimetric radar observations using local and global contextual information. Due to the diverse precipitation types, blockage conditions, and ground information in different areas, two radars deployed in two different regions characterized by different precipitation types are used to demonstrate the proposed methodology. Both are S-band operational Weather Surveillance Radar – 1988 Doppler (WSR-88D): KFWS located in Fort Worth, northern Texas, and KDAX located in Davis, northern California. For training the GAN model, this article simulates the partial beam blockage situations by manually cropping observation sectors of both KDAX and KFWS radar data. The trained models were tested using independent precipitation events in Texas and California to demonstrate the model effectiveness in inpainting “missing" data. In addition, this paper cross-tested the data with different precipitation features to examine the generalization capacity of the beam blockage correction models. The beam blockage correction performance is also compared with a traditional linear interpolation approach. The results show that for both domains the continuity of precipitation observations is greatly improved after applying the deep learning-based inpainting approach. For the KFWS test data, some visible discrepancies exist between the results from models trained based on convective and stratiform precipitation events in Texas and California, respectively, yet both models outperform the traditional interpolation method. For the KDAX test data, both the model trained using the KFWS data from convective precipitation events in Texas and the model trained using KDAX data from stratiform precipitation events in California render a similar performance. Although ground truth is not available for the real blocked radar data, the repaired observations demonstrated a great potential for improved quantitative applications.

Impacts of Vertical Nonuniform Beam Filling on the Observability of Secondary Ice Production due to Sublimation

Abstract Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase Kdp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar Kdp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these Kdp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and Kdp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed Kdp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and Kdp within the sublimation zone, which results in a Kdp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive Kdp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased Kdp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP. Significance Statement Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars.

Measurement and characterization of infrasound waves from the March 25, 2023 thunderstorm at the near equatorial

<abstract> <p>Thunderstorm activity on March 25, 2023 provided a unique opportunity to study the mechanism of lightning events on changes in air pressure. In particular, this event made it possible to study changes in air pressure during thunderstorms using various instruments. This paper presented comprehensive results of infrasound, satellite data, weather radar and weather measurements at the ground during the storm. Observations of lightning events were confirmed using observational data from the International Space Station's Lightning Imaging Sensor (ISS LIS). This work estimated three spectral percentile values on infrasonic sensor data, time series interpolation of standard meteorology profiles, weather radar reflectivity and total radiant energy of lightning from ISS LIS observations during the day and night periods. As a result, during the investigation, it was seen that the recorded infrasound signal in the 0.6–0.8 Hertz (Hz) range was contaminated by background environmental noise, but in the 1–3 Hz band range it was consistent with the appearance of storms that produce high energy blows. Infrasound detection and electromagnetic lightning detection show good correlation up to a distance of 100 km from the infrasonic station. During a thunderstorm, the ISS LIS flight directly above the observation site detected more than 2,000 lightning events. In addition, the application of lightning detection from several independent instruments can provide a complete picture of the observed event.</p> </abstract>

Generalized Multi-Lag Estimators (GMLE) for Polarimetric Weather Radar Observations

Observations of weather phenomenon by polarimetric pulsed-Doppler weather radars are employed worldwide to monitor impending severe storms, flash-floods, and other weather related public hazards. The basis for processing received meteorological signals from pulsed-radar waveforms relies on stochastic processes where the accurate estimation of radar variables from received signals in additive white noise is essential for meaningful interpretation of weather phenomena and algorithm-derived products. For polarimetric weather radars, these estimates are calculated from signal correlations in time and across the horizontal and vertical polarization channels. Conventional estimators only use 1 or 2 signal correlation time-lags and may not utilize all the available information intrinsic in the received signals. Weather-variable estimates could benefit from the use of all intrinsic characteristics in the received data; accordingly, more complex estimators use multiple lags to extract additional information. However, not all estimates are improved by the use of more lags; in fact, improvement in estimates depends on signal characteristics and requires that the additional correlation lags provide new information. In this article, we derive and examine general multi-lag estimators for reflectivity, differential reflectivity, polarimetric cross-correlation coefficient, and Doppler spectrum width. We compare the performance of these proposed estimators against conventional estimators using Monte-Carlo simulations on different meteorological signal characteristics to find estimators that can improve the quality of certain radar-variable estimates.

Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Pixel-CRN: A New Machine Learning Approach for Convective Storm Nowcasting

The short-term convective storm forecasting (i.e., nowcasting) mainly rely on weather radar which can resolve the 3D structure of convective storms. With the rapid development of numerical models, modern models can produce 3D reanalysis data which gives atmospheric background information of convective storms. Current deep learning nowcasting models use only 2D radar images for nowcasting and often require massive historical data for training. But it may not be operationally feasible to collect long-term radar data to train a new model. Hence, how to establish a nowcasting model using only a small dataset has become an important issue. In addition, existing models do not effectively use the state-of-the-art model reanalysis data, which is a shortcoming of these models. To tackle these problems, this article develops a pixel-wise convolutional recurrent neural network (Pixel-CRN) for precipitation nowcasting. It has three key designs: (1) Through a concise pixel-wise sampling and oversampling technique, Pixel-CRN can be trained using only a small dataset. (2) In spatial learning, Pixel-CRN embeds a spatial convolution subnet into the recurrent unit, which can input raw 3D radar and model reanalysis data, thus valuable atmospheric background information can be learned to assist nowcasting. (3) In spatiotemporal learning, according to the information bottleneck principle, Pixel-CRN builds a heterogeneous encoder-decoder structure to squeeze multi-channel 3D input data into latent space, and recurrently generates 30 and 60 min nowcasts. Compared with existing deep learning nowcasting methods, the experimental results show that Pixel-CRN can provide skillful results with a rather small training dataset.

Extended Polarimetric Observations of Chaff Using the WSR-88D Weather Radar Network

Military chaff is a metallic, fibrous radar countermeasure that is released by aircraft and rockets for diversion and masking of targets. It is often released across the United States for training purposes, and, due to its resonant cut lengths, is often observed on the S-band Weather Surveillance Radar – 1988 Doppler (WSR-88D) network. Efforts to identify and characterize chaff and other non-meteorological targets algorithmically require a statistical understanding of the targets. Previous studies of chaff characteristics have provided important information that has proven to be useful for algorithmic development. However, recent changes to the WSR-88D processing suite have allowed for a vastly extended range of differential reflectivity, a prime topic of previous studies on chaff using weather radar. Motivated by these changes, a new dataset of 2.8 million range gates of chaff from 267 cases across the United States is analyzed. With a better spatiotemporal representation of cases compared to previous studies, new analyses of height dependence, as well as changes in statistics by volume coverage pattern are examined, along with an investigation of the new “full” range of differential reflectivity. A discussion of how these findings are being used in WSR-88D algorithm development is presented, specifically with a focus on machine learning and separation of different target types.

Relative Importance of Radar Variables for Nowcasting Heavy Rainfall: A Machine Learning Approach

Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction (NWP) models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell (CC), mesoscale CC, diagonal squall line (SLD), and parallel squall line (SLP), was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking (FAST), over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via five ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall.

Study on FR4 substrate backed flaring antenna structure for wideband applications

A class of planar antenna which offers better performance over broad band of frequencies that fits itself in wide range of applications are in need of research. The license free band from 3.1 GHz to 10.6 GHz that do not cause interference to narrowband signals is chosen as operating range of frequencies for the chosen Vivaldi antenna. The novelty of the proposed work consists in the remodeling of a mathematical equations for elliptic antenna structure and the study of the Vivaldi antenna operating over the frequency range between 2.63 GHz to 14.58 GHz that lies in Super High Frequency (SHF) bands – S, C and X bands. The designed antenna is fabricated and its radiation characteristics are studied and analyzed. Purpose. Analysis of the designed antenna on the basis of an elliptical curve mathematical model will allow the antenna to operate over wide band of frequencies thus extending its applications over the areas of application in the areas of satellite communication, military, weather radar, surface ship radar. Methods. The antenna is being designed using HFSS software and fabricated antenna is being tested at anechoic chamber. Results. A mathematical model of a structure of Vivaldi antenna was developed and studied. The performance measures of designed antenna such as return loss, gain, VSWR and radiation pattern are simulated and analyzed. The antenna is being fabricated and tested using vector network analyzer and anechoic chamber. Practical value. The analysis of designed antenna over wide band of frequencies is demonstrated. The further research directions of the designed antenna can be extended for the subsequent implementation of the results in suitable applications.

Hydrometeor Storage and Advection Effects in DYNAMO Budget Analyses

Abstract The Dynamics of the Madden–Julian Oscillation (MJO) (DYNAMO) field campaign over the central Indian Ocean captured three strong MJO events during October–December 2011. Using the conventional budget approach of Yanai et al. surface rainfall P0 is computed as a residual from the vertically integrated form of the moisture budget equation. This budget-derived P0 is spatially averaged over the Gan Island NCAR S-PolKa radar domain and compared with rainfall estimates from the radar itself. To isolate the MJO signal, these rainfall time series are low-pass (LP) filtered and a three-MJO composite is created based on the time of maximum LP-filtered S-PolKa rainfall for each event. A comparison of the two composite rainfall estimates shows that the budget rainfall overestimates the radar rainfall by ∼15% in the MJO buildup stage and underestimates radar rainfall by ∼8% in the MJO decay stage. These rainfall differences suggest that hydrometeor (clouds and rain) storage and advection effects, which are neglected in the budget approach, are likely significant. Satellite and ground-based observations are used to investigate these hydrometeor storage and advection effects. While the findings are qualitatively consistent with expectations from theory, they fall short of explaining their full magnitude, suggesting even more refined experimental designs and measurements will be needed to adequately address this issue.

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

Hypothetical Ground Radar-Like Rain Rate Generation of Geostationary Weather Satellite Using Data-to-Data Translation

This study proposes a deep-learning-based data-to-data (D2D) translation framework to simulate a radar-like retrieval of rainfall rates using the advantages of spatial coverage and temporal resolution of geostationary (GEO) satellite observation. The D2D method comprises normalization and denormalization in pre- and post-processing and an adversarial learning structure for an inter-domain conversion between physical values of data such as albedo and brightness temperature (BT) unlike the image-to-image translation using digital number values in image data. The GEO-KOMPSAT-2A (GK2A) and radar hybrid surface rainfall (HSR) datasets over the Korean Peninsula from September 2019 to September 2021 were used as the source and target domains for training and testing the D2D model. The constructed D2D model for ground radar-like rainfall generation was validated using the ground radar-observed rainfall data and compared to the GK2A rainfall rate (RR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) rainfall products. The D2D model exhibited excellent performance for various rain types in the study area compared to the GK2A RR, PERSIANN-CCS, and IMERG data. Consequently, the D2D model can provide valuable and accurate radar-like rainfall intensity and distribution data with a high temporal resolution and complementary rainfall information over lands and oceans without radar observation.

РАЗРАБОТКА МИКРОПОЛОСКОВОЙ РЕШЕТКИ С НАКЛОННЫМ РАСКРЫВОМ И СНИЖЕННЫМ ЧИСЛОМ ЭЛЕМЕНТОВ РЕГУЛИРОВКИ АМПЛИТУДНО-ФАЗОВОГО РАСПРЕДЕЛЕНИЯ ДЛЯ МАЛОВЫСОТНОЙ МЕТЕОНАВИГАЦИИ БПЛА В АРКТИКЕ

Problem statement. The development of hard-to-reach regions in the absence of ground-based navigation systems and data sources on the meteorological situation can cause an increase in the number of aviation accidents. For small Arctic aviation, there is a need to introduce onboard radar stations that solve various tasks: from ensuring flight safety to analyzing the situation at sea in the area of the escorted vessel. In low-altitude flight conditions, re-reflections from the underlying surface coming along the side lobes of the antenna pattern lead to the identification of false areas dangerous for flight. The paper searches for the requirements for a directional pattern focused on solving the problem of meteorological navigation in polar latitudes. The stages of the development of an antenna with an inclined opening and a deflected main beam of the radiation pattern, which guarantees a very low level of lateral radiation in the lower hemisphere, are presented. Research methods. The mathematical apparatus of the theory of antenna arrays and detection of surface and volume extended radar targets was actively used in the work. Purpose. To substantiate the prospects of using antenna arrays with an inclined opening and a beam to solve meteorological navigation problems. To outline the main stages of the synthesis of a patch antenna, which is characterized by a low level of lateral radiation in the lower hemisphere. Results. The results of modeling are presented, which allow us to obtain requirements for the parameters of antenna systems for meteorological navigation onboard radars operating in polar latitudes. The results of a comparative analysis of an inclined patch antenna array and an antenna array with an opening slope and an inclination of the beam of the opposite sign to reduce the level of lateral radiation are presented. Practical significance. The results of the work can be used in determining the requirements for radiation characteristics, as well as in the development of original designs of patch antenna arrays of reduced cost for small-sized airborne radars of low-altitude carriers in order to increase the probability of correct detection of dangerous weather formations for flight.

Convex Estimation of Sparse-Smooth Power Spectral Densities From Mixtures of Realizations With Application to Weather Radar

Convex Estimation of Sparse-Smooth Power Spectral Densities From Mixtures of Realizations With Application to Weather Radar