Radar Products Research Articles

A Bayesian Framework for the Probabilistic Interpretation of Radar Observations and Severe Hailstorm Reports

Abstract Understanding when and where severe hailstorms occur is key to managing the serious risk they pose. Radar products, such as the maximum expected size of hail (MESH), are often used to form a severe hail climatology. However, the challenges of relating reflectivity measured aloft to severe hail at the surface mean these climatologies have unknown uncertainty. Here, we quantify the uncertainty of radar observations of severe hailstorms by deriving a probabilistic interpretation of MESH within a fully Bayesian framework calibrated using reports from Australia’s Severe Storms Archive in southeast Queensland. Moreover, our novel approach accounts for the spatially varying (under)reporting rate in the region. Despite the popularity of using MESH thresholds to distinguish severe hail events, our results suggest that these thresholds are less sharp than previously believed and question their interpretation. Furthermore, we quantify the spatial variability in the severe hail reporting rate and suggest that, even over the most densely populated areas in the region, the reporting rate may be as low as 53%. Finally, we produce a hail climatology that has a similar magnitude to existing radar-based climatologies but with smoother and more realistic spatial gradients. Our method is generalizable to many other datasets within and beyond severe weather by enabling the principled usage of reports even when the absence of a report does not necessarily indicate the event did not occur. Significance Statement Severe hailstorms are one of Australia’s costliest natural perils. This work aims to improve how we use radar to understand when and where these storms occur. Although radar is a popular tool in this pursuit, it is challenging to link reflectivity aloft to hail at the ground. Rather than using a binary threshold on a radar-based parameter to distinguish severe hail, we instead treat radar data as a predictor of the probability of severe hail, estimating this probability by concurrently estimating the reporting rate of severe hail. This work sheds new light on how we might reinterpret radar data to improve our forecasting and understanding of severe hail. Moreover, our method is applicable to other severe weather hazards and even beyond climate science.

Benchmark Dataset Applied to Quantitative Precipitation Estimation and Forecasting of Qinghai

We collected short-duration heavy precipitation events around weather radar since 2020 and get radar base data of these events. Then, we extracted radar products like Composite Reflectivity (CR), echo top height (ET) and Vertically Integrated Liquid Water (VIL) from radar base data after quality control as the input of dataset, and merge the surface precipitation of 6 minutes with background field using multi-grid variation to generate precipitation label by 6 minutes. Thus, we constructed two datasets artificial intelligence application QpefQH for short-duration heavy precipitation. QpefQH dataset has a scale of more than 15,000 images available for radar echo extrapolation and quantitative precipitation estimation tasks based on deep learning. QpefQH dataset also includes multi-source data such as satellite, numerical model, sounding, lightning, land surface temperature, pressure, wind and other observation data. QpefQH can be shared as a benchmark dataset for artificial intelligence research on short-duration heavy precipitation in Qinghai Province.

Advancing Hydrologic Modelling Through Bias Correcting Weather Radar Data: The Valgrosina Case Study in the Italian Alps

ABSTRACTThe urgency of understanding the intricate input–output relationships of the hydrologic cycle is amplified by the accelerating climate change impacts in mountain environments. This study focuses on optimising water resource management of a dammed valley in the Central Alps (Northern Italy). The research aims to integrate radar data and precipitation interpolation techniques (TIN, Copula, cumulative distribution function; CDF techniques, inverse distance weighting; IDW, thin plate spline; TPS, ordinary kriging; OK and detrended kriging; DK) into a semi‐distributed hydrologic model, by utilising hourly precipitation data from 22 rain gauges and a composite weather radar product spanning 2010–2020. Two main objectives were pursued: (i) to develop and evaluate various radar precipitation correction methods against a benchmark dataset and (ii) to calibrate and assess the performance of the GEOFrame hydrologic model forced with corrected precipitation input. Point‐based and spatial correction approaches were evaluated against ground measurements through leave‐one‐out tests. The former derives dependence functions between the biased radar series and those of the closest three rain gauges to the target point applying a triangular irregular network. The latter combines deterministic and geospatial interpolations to the rain gauge/radar residuals to derive a corrected surface by incorporating radar values as trends. Precipitation series exceeding the composite scaled score of the benchmark dataset were used as input for hydrologic modelling. The spatial method combining radar values with ordinary kriging provided the best results for both correction and modelling (hourly KGE > 0.75). The spatial approaches proved easier to apply than the point‐based methods. In addition, correcting precipitation significantly improved low‐flow simulation from negative hourly lnNSE to values greater than 0.25. As a further step, given the overall good performance of the spatial methods, they could be used operationally as an ensemble to analyse management scenarios.

A Rare Tropical Cyclone Associated With Southwest Monsoon Over the Northern Part of the South China Sea—Tropical Storm Maliksi

AbstractThis study examines the characteristics and development of Tropical Storm Maliksi, which is a special case of tropical cyclone developed in the northwestern part of the South China Sea during a southwest monsoon outbreak. Detailed analyses were conducted using observational data and forecast products. Surface observations, radar wind profilers, aircraft data, and satellite products were used to evaluate Maliksi's wind structure, revealing multiple circulation centers and gale force winds. Vertical wind profiles, warm core structure, wind waves, and the influence of sea temperatures and salinity on Maliksi's intensification were investigated. Regarding forecasting, AI‐based models outperformed conventional numerical weather prediction (NWP) models in predicting Maliksi's initial development, though both struggled to capture the persistence of strong winds as the system moved inland. High‐resolution NWP simulations were employed to examine terrain‐induced wind variability around Hong Kong International Airport, revealing the mountain wake effect and uneven wind and turbulence distribution. These findings provide insights into the challenges of forecasting and monitoring such tropical cyclones, and highlight the need for enhanced observational platforms and forecasting tools along coastlines vulnerable to these systems.

Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations

Abstract. The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.

Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R2 ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available.

Enhancing competitive advantage through a strategic product ‎portfolio ‎and design for variety in weather radar products

Enhancing competitive advantage through a strategic product ‎portfolio ‎and design for variety in weather radar products

Bayesian cloud-top phase determination for Meteosat Second Generation

Abstract. A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar–radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods.

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations

Abstract Convective initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, an object-based probabilistic deep learning model is developed to predict CI based on multichannel infrared GOES-16 satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning model significantly outperforms the classical logistic model at lead times up to 1 h, especially on the false alarm ratio. Through case studies, the deep learning model exhibits dependence on the characteristics of clouds and moisture at multiple altitudes. Model explanation further reveals that the contribution of features to model predictions is significantly dependent on the baseline, a reference point against which the prediction is compared. Under a moist baseline, moisture gradients in the lower and middle troposphere contribute most to correct CI forecasts. In contrast, under a clear-sky baseline, correct CI forecasts are dominated by cloud-top features, including cloud-top glaciation, height, and cloud coverage. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights.

Evaluation of WRF Cloud Microphysics Schemes in Explicit Simulations of Tropical Cyclone ‘Fani’ Using Wind Profiler Radar and Multi-Satellite Data Products

Extremely severe cyclonic storm (ESCS) ‘Fani’ formed in the North Indian Ocean and crossed at Puri in Orissa State on the east coast of India on 03 May 2019. In this study, we examine the sensitivity of convection permitting WRF simulations (3 km) of ‘Fani’ to cloud microphysics (CMP) schemes using radar and multi-satellite data products. Five CMP schemes, namely Thompson, Goddard, WSM6, Morrison and Lin are tested in WRF. Results show that the changes in the CMP schemes primarily affect the simulated intensity and have lesser impact on the track predictions. Simulations with Thompson followed by Goddard produced the best predictions for both track and intensity estimates. Our analysis reveals significant variations in vertical motions associated with Fani across different CMP schemes; the WSM6, Goddard and Lin schemes produced relatively stronger vertical motions. The explicit WRF simulations could reproduce the wind profiler radar observed intense convective motions during the transit of Fani between 1 and 2 May 2019 at Gadanki station. Experiments with Thompson and Goddard schemes simulated the mean vertical velocities in lower, middle and upper layers in better agreement with radar data. The Lin, WSM6 and Goddard CMP predicted stronger updraft velocities (~ 0.35 m/s); Thompson produced moderate updraft velocities (~ 0.25 m/s) in the upper troposphere over a relatively wider area of high theta-e (385–390 K) indicating the simulation of a convectively stronger and warmer core compared to Morrison. Our analysis suggests that the differences in vertical motions in various CMP simulations are mainly due to the variations in the warming in simulations. It has been found that WSM6, Lin and Goddard produced a deeper core (up to 200 hPa) with a stronger diabatic heating of ~ 6° C followed by Thompson, which simulated a moderately deep core extending to ~ 250 hPa with moderate heating of ~ 5 °C whereas Morrison produced a relatively weak core with a heating of ~ 4 °C limited to 300 hPa. The stronger simulated diabatic heating in Lin, WSM6 and Goddard produces stronger inflow, moisture convergence in the lower levels and stronger outflow and divergence in the upper levels leading to stronger convection in the core region in these cases. The Lin, WSM6 and Goddard mixed phase schemes with more solid hydrometeors simulated stronger radar reflectivities, and stronger eyewalls, due to more latent heat release leading to the development of a strong warm core in the upper troposphere and thus a stronger TC.

Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg

Abstract. This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30 s), range (60 m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2 (Burgemeister et al., 2024).

Revealing the Precipitation Characteristics of Tropical Cyclone Ockhi from Polarimetric Doppler Weather Radar in Conjunction with Disdrometer Observations

Abstract This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation.

Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data

Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible.

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

A spatio-temporal fusion deep learning network with application to lightning nowcasting

Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network’s ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images.

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

An Exploratory Content Analysis of Two Local Television Stations’ Tornado Warning Broadcasts

Abstract In-depth analysis of the content of broadcast tornado warning coverage is limited. Such analysis is important due to local television’s role as a key source for tornado warning information. This study attempts to fill gaps in our knowledge regarding broadcast coverage of tornado warnings by demonstrating how local television news stations’ coverage of tornadic events can be systematically analyzed to better understand this element of warning communication. We reviewed both visual and verbal content for information such as the prominence of specific radar products, the geographic scale of warning communication, and common themes in verbal communication. A combination of deductive and inductive coding approaches was used to summarize the verbal content of the broadcasts. We found that the stations heavily used radar products with reflectivity and velocity surpassing correlation coefficient. The geographic scale of mapped products (street, city/county, and state level) appeared to be related to the rural or urban nature of the area warned, which may have implications for how readily rural residents would be able to personalize tornado threats. Verbal content was very similar between the two stations. The theme of monitoring and updating conditions, which included processes such as zooming in and out, making adjustments, reinforcing conditions, and providing damage reports was the most frequent communication type, likely because weathercasters use these processes to both communicate the warning and also to help themselves understand the situation. The results can inform future studies examining the influence of specific elements of broadcast warning coverage on risk perception and protective actions. Significance Statement Television is a key source for receiving or confirming tornado warnings, but few studies have examined the content of broadcast warnings in depth. This study examined the visual and verbal content of broadcast tornado warnings on two local television stations. Radar products were used heavily, and street-level coverage was more common when a tornado affected a metropolitan area. Coverage was most common at the city/county level. Verbal content included many elements of effective warning communication but at times included jargon that may not be understood by viewers. The results can serve as a springboard for future research on the impacts of these elements on risk perception and response. It can also serve future research by distinguishing what viewers of severe weather broadcasts are exposed to that nonviewers are not.

A contrastive analysis on the causes of two regional snowstorm processes influenced by the southern branch trough in Hunan in early 2022

AbstractIn early 2022, there were four low‐temperature weather processes with rain and snow in Hunan Province, China. Two processes occurred on January 28–29 (referred to as the “0128” process) and February 6–7 (referred to as the “0206” process), and they have overlapping areas of heavy snowfall and high intensity of short‐term snowfall. Multi‐source observation data and the National Centers for Environmental Prediction (NCEP) reanalysis data are used to analyze the characteristics of circulation background and mesoscale. In addition, the causes of heavy snowfall processes under the influence of the southern branch trough are discussed based on the dual‐polarization radar products at Changsha station. The results show that two processes are characterized by the rapid phase transformation of rain and snow, concentrated snowfall periods, and heavy snowfall at night. The short‐term snowfall intensity of the “0206” process is greater than that of the “0128” process. The high‐latitude blocking high of the “0206” process is stronger than that of the “0128” process, and the water vapor transport of the southerly jet in low levels in the “0206” process is also stronger. The organized development of cold cloud clusters from the meso‐β scale to the meso‐α scale indicates that the snowfall intensifies, and the maximum blackbody temperature gradient corresponds well to the center of heavy snowfall. The propagation that is similar to the train effect is an important reason for the heavy snowfall process. The vertical variation of the ZH and the bright band of dual‐polarization parameters can determine the phase transformation between rain and snow. When the ZH and ZDR bright bands are 1–3 km away from the ground, the phase state is rain if the ZH near the ground is greater than 0 dBZ and the CC is close to 1; the phase state is the rain‐snow mixed phase if the CC is less than 0.95. When the bottom of the ZH bright band decreases, the CC/ZDR bright band disappears, the near‐surface CC is greater than 0.99 and the ZDR is less than 1 dB, the rain turns to snow. Compared with the “0128” process, the characteristics of the bright ring during the rainfall period of the “0206” process are more obvious, the precipitation intensity judged from the larger ZH and KDP is larger, and the phase transformation is faster due to more significant cooling effect caused by precipitation.

High‐resolution inverse synthetic aperture radar imaging of satellites in space

AbstractIn view of the increasing number of space objects, comprehensive high‐quality space surveillance becomes ever more important. Radar is a powerful tool that, in addition to detection and tracking of objects, also enables spatially high‐resolution imaging independent of daylight and most weather conditions. Together with the technique of Inverse Synthetic Aperture Radar (ISAR), very high‐resolution and distance‐independent two‐dimensional images can be obtained. However, advanced high‐performance radar imaging of space objects is a complex and demanding task, touching many technological and signal processing issues. Therefore, besides theoretical work, the Microwaves and Radar Institute of German Aerospace Center (DLR) has developed and constructed an experimental radar system called IoSiS (Imaging of Satellites in Space) for basic research on new concepts for the acquisition of advanced high‐resolution radar image products of objects in a low earth orbit. Based on pulse radar technology, which enables precise calibration and error correction, IoSiS has imaged space objects with a spatial resolution in the centimetre range, being novel in public perception and accessible literature. The goal of this paper is therefore to communicate and illustrate comprehensively the technological steps for the construction and successful operation of advanced radar‐based space surveillance. Besides the basic description of the IoSiS system design this paper outlines primarily useful theory for ISAR imaging of objects in space, together with relevant imaging parameters and main formulae. All relevant processing steps, necessary for very high‐resolution imaging of satellites in practice, are introduced and verified by simulation results. Finally, a unique measurement result demonstrates the practicability of the introduced processing steps and error correction strategies.

Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand.