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Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather. [ABSTRACT FROM AUTHOR]

This study utilizes comprehensive observational data from a stratiform mixed-cloud precipitation event in Liupan Mountains, combined with ground-based millimeter-wave cloud radar (CR), micro rain radar (MRR), and microwave radiometer (MR) data, to study the evolution characteristics and conversion efficiency of precipitation particles in the ice–water mixed layer, melting layer, and below these layers during the formation and dissipation of precipitation. The results show the following: (1) When precipitation particles occupy more than 20% of cloud layers detected by cloud radar, the ice–water mixed cloud layer descends and evolves into a precipitating cloud. (2) During surface precipitation periods, the proportion of raindrops forming precipitation was equivalent to that of small-scale precipitation particles in the cloud layers. The proportion of precipitation particles in the cloud layers with temperatures below 0 °C averaged 25%. Ice-phase particles within the bright band (BB) melted, coalesced, and grew into larger precipitation particles, increasing their proportion to 55%. (3) After surface precipitation ended, the water content and precipitation rate of the cloud layer were 60% and 52% of those during the precipitation process, respectively. The proportion of small-scale precipitation particles in the cloud layers was approximately half of that during the precipitation period. A large number of evaporated small-scale precipitation particles floated in the air layer below the clouds, occupying less than 6.0% of the cloud layers. [ABSTRACT FROM AUTHOR]

Spatial and temporal variation in the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. In this work, the identification, estimation, and compensation of ionospheric path delay phases in PALSAR-3 and NISAR-L interferograms are discussed. Both of these L-band sensors simultaneously acquire SAR data in a main spectral band and in an additional, spectrally separated, narrower second band to support the mitigation of ionospheric path delays. The methods presented permit separating the dispersive and the non-dispersive phase terms based on the double-difference interferogram between the two available spectral bands and the differential interferogram of the main band. The applicability of the proposed methods is demonstrated using PALSAR-3-like data that were simulated based on PALSAR-2 SM1 mode data. [ABSTRACT FROM AUTHOR]

The change in extreme precipitation events in the conterminous United States (CONUS) has been of interest to the research communities in recent years for its intensification under environmental and climate change. Previous studies have not yet used sub-hourly precipitation observations to examine convective precipitation change over the CONUS. This study aims to fill the gap by examining convective precipitation, identified by radar reflectivity, in the CONUS using the state-of-the-art Multi-radar Multi-sensor data, operated at the NOAA/National Severe Storms Laboratory, with an unprecedentedly high spatial (1 km) and temporal (2 min) resolutions. These high-resolution data are expected to better capture the precipitation peak and the precipitation pattern. The results showed that in CONUS, precipitation reflectivity increased both in magnitude and the number of convective days from 2015 to 2021. For example, in 2019, 60% of areas showed an increase in the magnitude of precipitation, and the average number of convective days over CONUS has increased by 19%. Changes in precipitation also vary by season and region. This study highlights the need for continued monitoring and understanding of the evolving pattern of extreme precipitation in the CONUS, especially at sub-hourly frequency, as it exposes significant impacts on various sectors, including agriculture, infrastructure, and human health. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

To evaluate the impact of different momentum control variable (CV) schemes (CV5, the momentum control variable option with ψχ and CV7, the momentum control variable option with UV) on radar data assimilation (DA) in weather research and forecasting model data-assimilation (WRFDA) systems, a heavy snowfall in central and eastern regions of China, which started on 6 February 2022, was taken as a case in this study. The results of the wind-field increments from the single observation tests indicated that the wind-field increments had a larger range of influence when stream function and velocity potential (ψχ) were used as momentum control variables in CV5. Some spurious increments were also generated in the wind-field analysis, since CV5 tended to maintain the integrated value of the wind field. When U-wind and V-wind were used as control variables in CV7, the wind-field increments had a smaller impact range, and there was less dependence among different locations on the wind increments. For the heavy snow case, the CV7 schemes displayed some improvements in simulating the composite reflectivity compared to the other two experiments, since the composite reflectivity in the CV5 and control experiments were overestimated to some level. It was also found that the RMSEs were lower in the CV7 compared to those in the CV5 in the short-term forecasts during the data-assimilation cycles. Results also indicated that the CV7 had a more significant effect on the 6 h accumulated precipitation forecasts. Meanwhile, the experiment Exp_CV7 achieved the best ETS and FSS scores among the three groups of experiments, while Exp_CV5 appeared to be generally superior to the CTRL. In summary, the precipitation of Exp_CV7 yielded the rainfall intensity and location most close to the observation compared to those from both the CTRL and Exp_CV5 experiments. [ABSTRACT FROM AUTHOR]

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Thunderstorms can generate intense electrical activity, hail, and result in substantial economic and human losses. The development of very short-term forecasting tools (nowcasting) is essential to provide information to alert systems in order to mobilize most efficiently the population. However, the development of nowcasting tools depends on a better understanding of the physics and microphysics of clouds and lightning formation and evolution. In this context, the objectives of this study are: (a) to describe the environmental conditions that led to a genesis of a thunderstorm that produce hail on 7 January 2012, in the Metropolitan Area of São Paulo (MASP) during the CHUVA-Vale campaign, and (b) to evaluate the thunderstorm microphysical properties and vertical structure of electrical charge. Data from different sources were used: field campaign data, such as S-band radar, and 2- and 3-dimensional lightning networks, satellite data from the Geostationary Operational Environmental Satellite-13 (GOES-13), the Meteosat Second Generation (MSG), and reanalysis of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The thunderstorm developed in a region of low-pressure due to the presence of a near-surface inverted trough and moisture convergence, which favored convection. Convective Available Potential Energy (CAPE) of 1053.6 J kg−1 at the start of the thunderstorm indicated that strong convective energy was present. Microphysical variables such as Vertically Integrated Liquid water content (VIL) and Vertically Integrated Ice (VII) showed peaks of 140 and 130 kg m−2, respectively, before the hail reached the surface, followed by a decrease, indicating content removal from within the clouds to the ground surface. The thunderstorm charge structure evolved from a dipolar structure (with a negative center between 4 and 6 km and a positive center between 8 and 10 km) to a tripolar structure (negative center between 6 and 7.5 km) in the most intense phase. The first lightning peak (100 flashes in 5 min−1) before the hail showed that there had been a lightning jump. The maximum lightning occurred around 18:17 UTC, with approximately 350 flashes 5 min−1 with values higher than 4000 sources 500 m−1 in 5 min−1. Likewise, the vertical cross-sections indicated that the lightning occurred ahead of the thunderstorm's displacement (maximum reflectivity), which could be useful in predicting these events. [ABSTRACT FROM AUTHOR]

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. [ABSTRACT FROM AUTHOR]

Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model's ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects. [ABSTRACT FROM AUTHOR]

Gravity wave (GW) activities play a prominent role in the complex coupling process of wave–wave and wave–background circulation around mid-low-latitude and equatorial areas. The wavelengths are wide, from about 10 m to 100 km, with a period from minutes to hours. However, the oscillations of GW activities are apparently different between the period bands of 0.1 to 1 h (HF) and 1 to 5 h (LF). To further understand the characteristics of GW activities, the neutral winds during 2008–2009 with a resolution of 3 min obtained from a medium-frequency (MF) radar in Kunming (25.6° N, 103.8° E) were analyzed. Using two numerical filters, the HF and LF GWs were estimated. Interestingly, the power spectral density grows larger as the frequency increases. It linearly falls with decreasing frequency when the period is less than 2 h. The seasonal variations in both HF and LF GWs are strongly demonstrated in August–September, November, and February–March with maximum meridional variances of 1100 m2 s−2 and 500 m2 s−2 and maximum zonal variances of 800 m2 s−2 and 350 m2 s−2 in, respectively. The turbulent velocity was also calculated and shows similar oscillations with GW activities. Furthermore, the GW propagation direction exhibits strong seasonal variations, which may be dependent on the location of the motivating source and background wind. [ABSTRACT FROM AUTHOR]

Hail is a type of severe convective weather disaster characterized by abundant water vapor and strong updrafts, resulting in intense and high reflectivity echoes in hail clouds, often accompanied by an overhanging form. Although hail research has made great progress, it is still challenging to achieve accurate identification of hail. Compared with traditional radar, dual-polarization radar can output a variety of polarization parameters and provide information about the shape and phase of precipitation particles, which is conducive to the identification of hail particles. In this study, dual-polarization radar data are used to explore more hail features from various perspectives, starting with the morphological characteristics of hail clouds and using common feature extraction methods in the field of image processing. A comprehensive approach to high-dimensional features is developed. Using machine learning methods, hail identification models are constructed in both the traditional mechanism feature space and the new feature space constructed in this study. Experimental results strongly confirm the significant effectiveness of the five-dimensional new mechanism features developed in this paper for hail identification. [ABSTRACT FROM AUTHOR]

So far, the least is known about the D-region ionosphere out of the entire ionosphere due to the lack of a conventional detecting method and continuous data accumulation. Medium frequency (MF) radar is an important conventional tool for understanding the D-region ionosphere by measuring the electron density (Ne) within the height range of 60–90 km. To investigate the statistical variation of the D-region, especially at the mid-low latitude area, this study presents the statistical variations in the D-region Ne with the solar zenith angle (SZA), season, and altitude observed by Kunming MF radar (25.6° N, 103.8° E) under low solar activity (2008–2009). The diurnal variation of Ne behaves like typical diurnal changes, which are closely consistent with the SZA. The outstanding feature, the diurnal asymmetry phenomenon, significantly appears in different seasons and at different altitudes. The Ne has obvious semi-annual characteristics, and is larger in summer and fall and the smallest in winter. Compared to other seasons, the variation in the Ne with altitude is the most stable in summer. Due to the impacts of the highest SZA, the value of Ne in winter is the smallest, with a maximum value of less than 300 electrons/cm3, and the largest in summer and fall, with a maximum of 472 electrons/cm3. Particularly, the peaks of Ne above 76 km do not always appear at the time when the SZA is the smallest (at noon). Both the simulations by the International Reference Ionosphere (IRI2016) and observations using MF radar present a strong positive correlation with solar radiation. Meanwhile, it cannot be ignored that there were still large differences between the simulations and observations. To quantitatively analyze the differences between the observations and simulations, the observed value was subtracted from the simulated value. The results show that the maximum value between them was up to 350 electrons/cm3, and the minimum difference appeared at around 72 km, with a value less than 100 electrons/cm3. However, below 66 km, the observations were larger than the simulations, which were, on the contrary, above 76 km. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for meteorological analysis. However, the coverage of a single radar is limited, leading to the use of satellite data as a complementary source. Consequently, how to bridge the gap between radar and satellite data has become a growing research focus. In this paper, we present MAFormer, a novel model for reconstructing radar reflectivity using satellite data within the Transformer framework. MAFormer consists of two modules: the Axial Local Attention Module and the Mixup Global Attention Module, which extract both local saliency and global similarity. Quantitative and qualitative experiments demonstrate the effectiveness of our proposed method. Specifically, the MAFormer model exhibits notable advancements when compared to state-of-the-art deep learning techniques. It demonstrates an improvement ranging from 0.01 to 0.05 in terms of the Heidke skill score, indicating its superior performance. Additionally, MAFormer effectively mitigates false alarm rates by approximately 0.016 to 0.04, which further highlights its enhanced accuracy and reliability. [ABSTRACT FROM AUTHOR]

Dual-polarization radar data are useful for numerical models to improve precipitation forecasts. For an extremely heavy precipitation event that occurred in Central China on 11 August 2021, the hydrometeor concentration and water vapor content used in the initial field of the Weather Research and Forecasting (version 4.1) model are retrieved by the statistical relationship of relative humidity with dual-polarization radar reflectivity in Suizhou City of Central China. Three experiments are conducted, and the simulation results are compared after assimilating the radar data. The results indicate that the multiple factors contributing to this extreme heavy precipitation event included the divergence of upper-level airflows, the middle- and low-level low vortex/shear, the easterly jet stream in front of the low vortex, and the continuous intrusion of cold air on the ground. In addition, with the retrieval of the hydrometeor concentration and water vapor content, the composite reflectivity forecast results are more similar to the observations. Also, the location and intensity of the short-term extremely heavy precipitation event are less different from the observations. In addition, by cyclically adjusting the hydrometeor concentration and water vapor content in the initial field, we can obtain better forecasts of the reflectivity and short-term extremely heavy precipitation, and this improvement can be maintained for approximately 3 h. [ABSTRACT FROM AUTHOR]

The measurement of global precipitation is important for quantifying and understanding the Earth's systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes. [ABSTRACT FROM AUTHOR]

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset. [ABSTRACT FROM AUTHOR]

Using a network of meteor radar observations, observational evidence of polar-to-tropical mesospheric coupling during the 2018 major sudden stratosphere warming (SSW) event in the northern hemisphere is presented. In the tropical lower mesosphere, a maximum zonal wind reversal (−24 m/s) is noted and compared with that identified in the extra-tropical regions. Moreover, a time delay in the wind reversal between the tropical/polar stations and the mid-latitudes is detected. A wide spectrum of waves with periods of 2 to 16 days and 30–60 days were observed. The wind reversal in the mesosphere is due to the propagation of dominant intra-seasonal oscillations (ISOs) of 30–60 days and the presence and superposition of 8-day period planetary waves (PWs). The ISO phase propagation is observed from high to low latitudes (60° N to 20° N) in contrast to the 8-day PW phase propagation, indicating the change in the meridional propagation of winds during SSW, hence the change in the meridional circulation. The superposition of dominant ISOs and weak 8-day PWs could be responsible for the delay of the wind reversal in the tropical mesosphere. Therefore, this study has strong implications for understanding the reversed (polar to tropical) mesospheric meridional circulation by considering the ISOs during SSW. [ABSTRACT FROM AUTHOR]

Three facts characterise the hailstorm of 30 August 2022 in the Catalan village of La Bisbal d'Empordà and its surroundings: first, the most dramatic, the death of a child hit by a hailstone; second, the damage to most of the roofs and cars in the town; finally, the highest recorded amount of hail (more than 10 cm) in Catalonia in at least the last 30 years. This research focuses on the radar field comparison and the observations provided by an electronic survey of the study area. The results reveal that weather radar underestimated the hail size because of different factors. Conversely, some reporters provided an inaccurate hour. The difference of three months between the hail event and the electronic survey is the probable cause of this mistake in the time estimation. However, the survey delay helped to avoid answers with larger hail sizes than those provided by the official spotters. [ABSTRACT FROM AUTHOR]

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method. [ABSTRACT FROM AUTHOR]

The intraseasonal oscillations (30–100 days, ISO) in the MLT (mesosphere and lower thermosphere) horizontal wind are investigated based on observations from the Mengcheng meteor radar. There is a clear seasonal variation in ISO in the horizontal wind at 80 km, which is strongest during the winter and weakest during the summer. At 100 km, ISO occurs throughout most of the year except winter, and there are significant differences in periods and amplitudes from year to year. From 2015 to 2016, ISOs with periods of 40–60 days were present in the 100 km horizontal wind, whereas none were simultaneously observed in the 80 km horizontal wind. Cross wavelets were used to study the relationship between ISO in the MLT region and ISO in the lower atmosphere. Some of the ISO activity is linked to tropospheric tropical convective activity, but the ISO connections with that in tropospheric convection are not consistent in the upper mesosphere and in the lower thermosphere. [ABSTRACT FROM AUTHOR]

X-band dual-polarization phased array radar (XPAR-D) possesses high resolution and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during 16–17 May 2020 from three precipitation stages after quality control. Attenuation coefficients were calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Next, an attenuation correction algorithm (MZH-KDP method) was proposed for the radar reflectivity factor (ZH) according to different raindrop types and compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations, which are challenging to resolve using attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather. [ABSTRACT FROM AUTHOR]

Short-term extrapolation by weather radar observations is one of the main tools for making weather forecasts. Recently, deep learning has been gradually applied to radar extrapolation techniques, achieving significant results. However, for radar echo images containing strong convective systems, it is difficult to obtain high-quality results with long-term extrapolation. Additionally, there are few attempts and discussions to incorporate environmental factors governing the occurrence and development of convective storms into the training process. To demonstrate the positive effect of environmental factors on radar echo extrapolation tasks, this paper designs a three-dimensional convolutional neural network. The paper outlines the processing steps for matching radar echo images with environmental data in the spatio–temporal dimension. Additionally, it develops an experimental study on the effectiveness of seven physical elements and their combinations in improving the quality of radar echo extrapolation. Furthermore, a loss function is adopted to guide the training process of the model to pay more attention to strong convective systems. The quantitative statistical evaluation shows the critical success index (CSI) of our model's prediction is improved by 3.42% (threshold = 40 dBZ) and 2.35% (threshold = 30 dBZ) after incorporating specific environmental field data. Two representative cases indicate that environmental factors provide essential information about convective systems, especially in predicting the birth, extinction, merging, and splitting of convective cells. [ABSTRACT FROM AUTHOR]

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. [ABSTRACT FROM AUTHOR]

The all-sky meteor radar is an important means to detect 70–110 km wind fields. Previous studies have shown that the wind field retrieved by all-sky meteor radars is closely related to the meteor count detected by the radar. However, the precision of the wind field is still unclear. In this paper, the influence of the meteor counts detected by two all-sky meteor radars operating simultaneously at Kunming station on wind fields is analyzed based on the observations of the two radars from 1 November 2013 to 31 December 2014. First, the meteor counts detected by the two meteor radars are approximately 100–3000 per hour, and the meteor count detected by the 37.5 MHz meteor radar is more than that according to the 53.1 MHz meteor radar. The meteor counts detected by the two radars vary with the local time and altitude. The meteor counts detected from 20 UTC to 02 UTC are the largest in the altitude range of 84–92 km, while the meteor counts detected from 09 UTC to 15 UTC are the lowest at other altitudes. Second, the more meteors detected by the two radars, the smaller the wind field differences retrieved by the two radars, and the closer the wind fields are to the real average wind field. Third, because the performance of the two radars is basically identical, except that the meteor counts detected by the two radars are different due to their different operating frequencies, the meteor count is the main system error of the wind fields retrieved, and the errors can be determined by the correlation coefficients of the wind fields retrieved by the two radars. Finally, in the altitude range of 76–100 km, the mean wind field differences of the two radars are less than 5 m/s. [ABSTRACT FROM AUTHOR]

For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study. [ABSTRACT FROM AUTHOR]

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor "frozen" turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance. [ABSTRACT FROM AUTHOR]

Three-body scatter spike (TBSS) is one of the most common signatures associated with hailstorms using weather radar. It consists of an appendage appearing behind the storms in the radar imagery. The cause is the multi-scattering between hail particles and the ground. The TBSS is detected in plan position indicator (PPI) reflectivity fields at the elevation coinciding with the hail core. Surveillance staff usually analyzes low-level PPI in real-time, but the signature can appear at mid or high elevations. This study takes advantage of the three-dimensional maximum reflectivity (MAX) product for detecting the presence of the TBSS in thunderstorms more easily, instead of the usual way of analyzing the PPI fields. The new technique is more direct and immediate, crucial in real-time surveillance tasks. This preliminary analysis has focused on the hail days with ground registers that occurred in Catalonia between January and August of 2022, observing this signature in the MAX product in 96% of days with large hail and 72% of small hail events. This fact indicates that MAX can be useful for identifying large hail in thunderstorms, but the efficiency decreases for hail smaller than 2 cm. [ABSTRACT FROM AUTHOR]

Based on the ion continuity equation solved under chemical equilibrium, a physical model of the D-region ionosphere (60–90 km) is established. The model involves 145 ion chemical reactions and includes 23 kinds of positive ions, 11 kinds of negative ions, and electrons. The simulation results show that molecular ions, such as NO+, NO+(H2O)n, H+(H2O)n, CO3−, and O3−, are the main components of ions in the D-region. The diurnal change of electron density at low latitudes is more obvious than at high latitudes. Preliminary comparisons with the International Reference Ionosphere (IRI) model and observed data of Medium Frequency (MF) radar at Kunming Radio Wave Observation Station show that the model is able to describe the basic features of D-region parameters. In addition, the results show that the minimum height of the D-region lower boundary in the low latitude is approximately 65 ± 1 km, and the height during the daytime is strongly correlated with local time. Furthermore, the results also reveal that the asymmetry of electron density is observed, with higher electron density during sunset than during sunrise at 75–85 km altitude. These above results are helpful for better understanding the variation of the D-region. [ABSTRACT FROM AUTHOR]

This study presents the algorithm ACTION, defined as automatic center detection of tropical cyclone (TC) using image processing based on the operational radar network. Based on the high visibility of weather radar imagery, a TC's motion vector is calculated from the continuous image change using optical flow, producing the TC's rotation center. The algorithm performance was verified by analyzing the typhoons (TCs in the northwestern Pacific) that affected the Korean Peninsula from 2018–2021, demonstrating a high detection rate of 80.8% within an error distance of 40 km against the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC's initial entry inside the observation radius even in the absence of uniform radar data. As ACTION is capable of performing real-time calculations that are directly applied to rapidly generated weather radar images, it is currently being utilized by the KMA. The high temporal resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting. [ABSTRACT FROM AUTHOR]

By computer simulation, the main consequences of the "slice" approach to assessing the power of a radar return signal from an atmospheric water cloud were verified. The cloud was modeled as a set of slices, which are thin layers coinciding with the wavefront of the probing radar radiation. The return signal was calculated as the convolution of the probe pulse with the cloud. The simulation was based on the previously derived "slice" radar equation, which determines the received power taking into account the statistical characteristics of fluctuations in the number of slice droplets and their water content. Estimates of the magnitude of the simulated return signal were compared with the theoretical formula for various statistical characteristics of the ensemble of droplets that make up the cloud model. The simulation result confirmed the validity of using the slice approach to interpret radar measurements of water content in clouds. [ABSTRACT FROM AUTHOR]

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast. [ABSTRACT FROM AUTHOR]

Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively. [ABSTRACT FROM AUTHOR]

A hailstorm with an inclined structure occurred in the western part of the South China coast on 27 March 2020. This study investigates the detailed evolution characteristics of this inclined structure using the Doppler radar data assimilation system (VDRAS) and the improved fuzzy logic hydrometeor classification algorithm (HCA). Obvious differential reflectivity (often referred to as ZDR) arc characteristics, ZDR column characteristics, and the specific differential phase (often referred to as KDP) of the column are observed using dual-polarization radar prior to hailfall. Both the ZDR column and KDP column reached their strongest intensities during the hailfall phase, with their heights exceeding the height of the −20 °C layer (7.997 km above ground level), displaying a cross-correlation coefficient (CC) valley during this phase. Meanwhile, two centers of strong reflectivity were found, with one (C1) being located at 2–4 km, and the other (C2) being located at 6–8 km. The maximum horizontal distance between the two centers is 8 km, suggesting a strongly inclined structure. This inclined structure was closely related to the interaction between upper-level divergent outflows and ambient horizontal winds. The updraft on the front edge of the hailstorm continued to increase, keeping C2 at the upper level. At the same time, large raindrops at the lower part of C2 are continuously lifted, leading to ice formation. These ice particles then fell obliquely from their high altitude, merging with C1. [ABSTRACT FROM AUTHOR]

The impact of radar and surface data assimilation on the forecast of a nocturnal squall line initiated above the stable boundary layer in the Yangtze–Huaihe River is investigated by the Weather Research and Forecasting (WRF) model and its three-dimensional variational assimilation system (WRFDA 3DVar). Results show that the assimilation of radar and surface data can improve the prediction of the convection initiation time, height and vertical ascending motion during the early stage of the squall line formation by adjusting the thermodynamic structure, circulation patterns, water vapor conditions and hydrometeor mixing ratios. Although the radar and surface data assimilation can improve the forecast of the location of the squall line to a certain extent, the squall line is stronger in the radar data assimilation than that in the surface data assimilation, leading to stronger radar reflectivity and heavier precipitation. The assimilation of both radar and surface data has a more positive impact on the forecast skill than the assimilation of either type of data. Moreover, during the mature stage of the squall line, radar and surface data assimilation can enhance the intensity of the surface cold pool. Specifically, radar data assimilation or assimilating the two data simultaneously can produce a stronger cold pool than only assimilating surface data, which is more conducive to the maintenance and development of the squall line. [ABSTRACT FROM AUTHOR]

Nighttime F-region field-aligned irregularities (FAIs) associated with equatorial plasma bubbles (EPBs) are impacted by terrestrial factors, such as solar irradiance and geomagnetic activity. This paper examines the impact of the planetary-scale periodic variability of terrestrial processes on EPB activity. Continual observations of the Equatorial Atmosphere Radar (EAR) have been utilized to derive the intra-seasonal variability of nighttime F-region FAIs in the context of the terrestrial factors mentioned above. A periodicity analysis using wavelet and Lomb–Scargle (LS) spectral analysis indicated significant amplitudes of the long-period planetary-scale variability in the F-region FAI signal-to-noise ratio (SNR), 10.7 cm flux, and geomagnetic indices, as well as a shorter period of variability. Interestingly, a careful inspection of the time series indicated the planetary-scale variability of F-region FAIs to be reasonably out of phase with the periodic geomagnetic variability. EPB occurrence and the FAI signal-to-noise ratio presented a systematic decrease with an increase in the level of geomagnetic activity. Non-transient quiet-time geomagnetic activity has been found to suppress both the occurrence as well as the strength of F-region FAIs. The impacts of planetary-scale geomagnetic activity appear to be non-identical in the summer and equinoctial EPBs. The results highlight the importance of periodic terrestrial processes in driving the planetary-scale variability of EPBs. [ABSTRACT FROM AUTHOR]

A global navigation satellite system (GNSS)-based multistatic radar is explored for target localization and kinematic state estimation. Since any point on the earth can be illuminated by a minimum of four satellites of each GNSS constellation at any time, GNSS-based passive radars can be inherently considered multistatic radars. In this paper, a method for jointly estimating the target position and velocity by utilizing both the time delays and Doppler shifts has been proposed, and an analytical accuracy analysis is also provided. In the new method, the bistatic range and Doppler for each path are firstly estimated by the range-Doppler (RD) method, and then by using the bistatic ranges and Doppler shifts. The least squares method is applied to estimate the target position and velocity simultaneously. Compared with the precedent target localization and velocity estimation method, the proposed method achieves a better estimation result with simple procedures. Simulation results are provided to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Data collected over a period of 18 months (December 2019–May 2021) at the Bucharest–Măgurele Cloudnet station were analysed for the first time to determine the macrophysical and microphysical cloud properties over this site. A total number of 1,327,680 vertical profiles containing the target classification based on the Cloudnet algorithm were analysed, of which 1,077,858 profiles contained hydrometeors. The highest number of profiles with hydrometeors (>60%) was recorded in December 2020, with hydrometeors being observed mainly below 5 km. Above 5 km, the frequency of occurrence of hydrometeors was less than <20%. Based on the initial Cloudnet target classification, a cloud classification scheme was implemented. Clouds were more frequently observed during winter compared with other seasons (45% of all profiles). Ice clouds were the most frequent type of cloud (468,463 profiles) during the study period, followed by mixed phases (220,280 profiles) and mixed phased precipitable clouds (164,868 profiles). The geometrical thickness varied from a median value of 244 m for liquid clouds during summer to 3362 m for mix phased precipitable clouds during spring. [ABSTRACT FROM AUTHOR]

Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have much higher spread over convective regions. The distribution of Kdp is almost uniform across the vertical over the stratiform regions. The mean profile of Zdr over stratiform regions shows a distinct local maxima near melting level. A comprehensive analysis has been done on an isolated deep convective system on 13 May 2018. Plan position indicator (PPI) diagrams and satellite measured cloud top temperature demonstrate that pre-monsoon deep convective systems can develop very rapidly within a very short span of time over the region. Heavy precipitation near the surface is reflected in the high value of Kdp (>5° km−1). High values of Zdr (>3 dB) were measured at lower levels indicating the oblate shape of bigger raindrops. A fuzzy logic-based hydrometeor identification algorithm has been applied with five variables (Zh, Zdr, ρhv, Kdp, and T) to understand the bulk microphysical properties at different heights within the storm. The presence of bigger graupel particles near the melting layer indicates strong updrafts within the convective core regions. The vertical ice hydrometeor signifies the existence of a strong electric field causing them to align vertically. Numerical simulation with the spectral bin microphysics (SBM) scheme could produce most of the features of the storm reasonably well. In particular, the simulated reflectivity, graupel mixing ratio and rainfall were in good agreement with the observed values. [ABSTRACT FROM AUTHOR]

Some thunderstorms in Cordoba, Argentina, present a charge structure with an enhanced low-level positive charge layer, and practically nonexistent upper-level positive charge. Storms with these characteristics are uncommon in the United States, even when considering regions with a high frequency of anomalous charge structure storms such as Colorado. In this study, we explored the microphysical and kinematic conditions inferred by radar that led to storms with this unique low-level anomalous charge structure in Argentina, and compared them to conditions conducive for anomalous and normal charge structures. As high liquid water contents in the mixed-phase layer lead to positive charging of graupel and anomalous storms through the non-inductive charging mechanism, we explored radar parameters hypothesized to be associated with large cloud supercooled liquid water contents in the mixed-phase layer and anomalous storms, such as mass and volume of hail and high-density graupel, large reflectivity associated with the growth of rimed precipitation to hail size, and parameters that are proxies for strong updrafts such as echo-top and Zdr column heights. We found that anomalous storms had higher values of mass and volume of hail in multiple sub-layers of the mixed-phase zone and higher frequency of high reflectivity values. Low-level anomalous events had higher hail mass in the lower portion of the mixed-phase zone when compared to normal events. Weaker updraft proxies were found for low-level anomalous events due to the shallow nature of these events while there was no distinction between the updraft proxies of normal and anomalous storms. [ABSTRACT FROM AUTHOR]

In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student's, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators. [ABSTRACT FROM AUTHOR]

Polarimetric radar data and total lightning data are used to develop lightning cessation guidance for isolated cells in the Washington, D.C. area. A total of 23 non-severe thunderstorms during the 2015–2017 warm seasons are analyzed. Radar and lightning data are superimposed using the Warning Decision Support System–Integrated Information software to develop cessation algorithms. This includes using the hydrometeor classification algorithm to locate graupel for each convective cell. Results show that the three best-performing cessation algorithms use thresholds of (1) ZH ≥ 40 dBZ at −5 °C, (2) ZH ≥ 35 dBZ at −10 °C, and (3) graupel at −15 °C. Lightning is not expected 15 min after the threshold is no longer met for each algorithm. These algorithms are recommended only for isolated cells in the Washington, D.C. area. Further study needs to be completed to draw conclusions for other convective cell types and different geographic regions. [ABSTRACT FROM AUTHOR]

The Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on board the ICON satellite provides effective measurement of horizontal winds in the mesosphere and lower thermosphere (MLT) region. In order to verify the measurement accuracy of the horizontal wind, this study uses the measurements of the meteor radar in Wuhan and the simulation results of a horizontal wind field model (HWM14) to compare and analyze the measurement results of MIGHTI/ICON in the whole year of 2020. The comparative analysis indicated that two datasets from MIGHTI/ICON and meteor radar are strongly correlated (r = 0.65, 0.76) with an RMS difference of 39.21 m/s (30.31 m/s). The consistency for meridional wind from MIGHTI/ICON, meteor radar, and HWM14 is worse than that of zonal wind. The accuracy of horizontal wind observations is influenced by altitude, diurnal, and seasonal patterns. [ABSTRACT FROM AUTHOR]

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method.

Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions. [ABSTRACT FROM AUTHOR]

The rainfall regime is changing in the Catalan territory, likely in most areas in the Mediterranean Basin. This variability, spatial and temporal, means that there may be periods of severe drought combined with periods of heavy rainfall and floods. In this way, the management of water resources is complicated and can produce a high impact on different social aspects. The high convective activity leads to investigating the relationship between the electric discharges and radar parameters (reflectivity, echo top, vertically integrated liquid, and accumulated rainfall). The correlation allows identifying some elements that may be significant in terms of changes in rainfall regimes. Besides, using several radar parameters apart from precipitation accumulation reveals interesting explicit patterns of the previously known. These patterns can help better understand the precipitation behavior and the changes associated with it. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the structural and evolutionary characteristics of supercell tornadoes that occurred in eastern China on 14 May 2021, observations from the S-band dual-polarization radars, soundings and other instruments are used to investigate the evolutionary process of the tornado formation by the mergering and strengthening of supercell storms. The results are described as follows. The updraft by upper divergence and vertical thermal instability induced by the cold source at the tropopause provided the environmental conditions suitable for tornado formation. The tornado event involved three storm merger processes, each of which was associated with an increase in the echo intensity, vertical rising speed, and vertical vorticity of the supercell. Furthermore, during the last merger, the merging of the two vortices resulted in the reduction of the rotation radius of the new vortex, which also provided a favorable condition for tornadogenesis. A schematic was proposed to describe storm mergers. The characteristics of the velocity spectrum width were indicative of the occurrence and evolution of the tornado in this case. During the tornado stage, distinct polarimetric variable signatures (e.g., a tornado debris signature and a differential reflectivity arc) and radial velocity signatures (i.e., a tornadic vortex signature) were observed. [ABSTRACT FROM AUTHOR]

The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores. [ABSTRACT FROM AUTHOR]

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere. [ABSTRACT FROM AUTHOR]