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The dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere‐Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case‐I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case‐II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large‐scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case‐II, while higher spectral width (>2 m s−1) is associated to Case‐I. The microphysical processes assessed by the WRF model pointed out that Case‐I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region. Plain Language Summary: This study examined two different weather events in the Himalayan region. The first event occurred during the monsoon season and mostly involved liquid precipitation, while the second event took place in winter and had both solid and liquid precipitation. To understand how these weather patterns formed in the context of their dynamical and microphysical processes, we analyzed the data obtained from ASTRad technology and the WRF model. The radar data played a crucial role in observing and analyzing the vertical structure and temporal evolution of the precipitation systems. The modeling data provided valuable insights of the orographic effects and microphysical processes behind the formation of solid and liquid phase precipitation. Understanding these processes is essential for predicting weather in the Himalayan region, especially the influence of mountains on liquid and solid precipitation. Key Points: Two distinct precipitation systems, one associated with monsoon and the other with western disturbance are investigated over the central HimalayaThese systems show distinct characteristics in terms of synoptic meteorological conditions, cloud processes, and spatio‐temporal scalesIntegrated radar and model data emphasize the importance of understanding the precipitation evolution influenced by orography over the region [ABSTRACT FROM AUTHOR]

Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach. Plain Language Summary: Pressure is an important atmospheric variable. A radar‐based remote sensing techinque to measure surface pressure is proposed. The methodology is based on the darkening of the surface return when moving closer to the center of the 60 GHz oxygen absorption line. Results demonstrate that measurements of surface pressure with errors of few hPa are feasible. Key Points: Differential absorption radars with multiple tones within the oxygen band to enable surface pressure measurements with few hPa accuracyCloud, rain and water vapor produce biases in the surface pressure that must be correctedErrors can be minimized by adopting tunable frequencies and frequency diversity [ABSTRACT FROM AUTHOR]

This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies. Key Points: Demonstration database of individual thunderstorm‐like features combines independent data from multiple moving and fixed observing platformsPreliminary features include precipitation and lightning data collected from Global Precipitation Measurement and Lightning Imaging Sensor on board the International Space Station satellite instruments and ground‐based radarsDatabase enables cross‐platform, broad statistical thunderstorm‐centric analyses as well as detailed single‐storm case study reconstruction [ABSTRACT FROM AUTHOR]

Earth's ionosphere has long been targeted as a medium for remote sensing of explosive terrestrial events such as earthquakes, volcanic eruptions, and nuclear/conventional weapon detonations. Until now, the only confirmed ionospheric detections have been of very large events that were easily detectable through other traditional global sensor systems (e.g., seismic). We present the first clear, confirmed detections of relatively low yield 1‐ton TNT‐equivalent chemical explosions using pulsed Doppler radar observations of isodensity layers in the ionospheric E region. The shape and spectra of the detected waveforms closely match predictions from the acoustic ray tracing and weakly nonlinear waveform propagation models. The explosions were roughly three orders of magnitude lower yield than any previous confirmed ionospheric detection and represent the first conclusive evidence that explosions of this size can have clear impacts on the ionosphere. This technique could improve the remote detection of both anthropogenic and natural explosive events. Plain Language Summary: Explosions, even of modest yield, generate copious amounts of acoustic waves across a wide range of frequencies. While the high frequencies are quickly absorbed by the atmosphere, the lowest frequencies, in the sub‐audible infrasound part of the spectrum, are capable of traveling to great distances. Typically this infrasound is detected on the ground using highly sensitive listening equipment such as microbarometers. We present a different method, however, where we utilize the fact that the upper atmosphere is ionized (the ionosphere) and is therefore a good target for a radar. As the sound wave passes through the ionosphere it sloshes the bulk ions such that it shows up an oscillating radar target. We use this new technique to clearly detect explosions with yields of 1‐ton TNT equivalent. These detections are 500 times smaller than previous confirmed detections using the ionosphere as a sensor for explosions. Key Points: Infrasound from 1‐ton TNT‐equivalent chemical explosions are clearly detectable using pulsed Doppler radar of the ionosphereObservations match the modeled temporal and spectral predictions of infrasound propagationStrong azimuthal differences in waveform shape and strength could be caused by uneven terrain or complex wind fields [ABSTRACT FROM AUTHOR]

Most previous current spatiotemporal sequence neural networks used for radar extrapolation have difficulties in learning long‐term spatiotemporal memories. This is because the spatiotemporal sequential neural networks only use the information from the previous time step node to update the prediction state, and the networks tend to rely on the convolution layers to capture the spatiotemporal features, which are local and inefficient. In order to capture the long‐term temporal characteristics and local abrupt spatial characteristics of radar echo sequence, we propose a new framework, Global Memory Attention (GMA), which has two contributions. The first is that we establish a global information flow between calculation units, extract the key historical memory from the global information flow, calculate the correlation between the historical key memory and the current time frame prediction state, and determine how much historical memory participates in the prediction state update. It alleviates the current network's difficulty in learning the long‐term spatial and temporal characteristics of radar echo sequences and the problem of short‐term dependence, and reduces the interference of image noise in the process of radar extrapolation prediction. The second is that the GMA module is a flexible additional module that can be applied to most radar extrapolation algorithms based on timing networks. GMA has reached the highest level on radar extrapolation tasks, and we have provided ablation studies to verify the effectiveness of GMA. Plain Language Summary: Spatiotemporal sequence neural networks are widely used in radar echo extrapolation tasks. However, in the past, most spatiotemporal sequence networks have short‐term dependence and long‐term dilemma. In this study, we propose a new framework, which can be applied to any radar extrapolation algorithm based on spatiotemporal sequence network. It establishes the global information flow and updates the prediction state by extracting the key historical memory strongly associated with the current prediction state through the attention module. It improves the utilization rate of long‐term information and reduces the interference of radar echo image noise, and effectively improves the problem that the extrapolation effect decreases with the increase of extrapolation time. Key Points: A new spatiotemporal sequence neural network framework is proposed for radar echo extrapolationUsing a series of attention calculations to effectively improve the accuracy of the extrapolationIt can be applied to any radar extrapolation model based on spatiotemporal sequence neural network [ABSTRACT FROM AUTHOR]

In this study, multi‐source data, especially weather radar data, were used in the analysis of structural features and evolution of a squall line‐like rainband (SLLR) of a linear mesoscale convective system (MCS) in the eastern slope of the Taihang Mountains on 12 and 13 August 2018 and reached the following conclusions: (a) The SLLR, located in north of the linear MCS, exhibited a significant horizontal gradient of reflectivity and large vertical extension. The front‐to‐rear flow that appeared between the surface and the stratosphere was lifted upward immediately in front of a cold pool that formed rearward‐tilting updrafts. (b) As the SLLR passed over, surface meteorological quantities changed drastically, coinciding with the heavy precipitation. (c) The SLLR, when propagating northeastward, showed a complex convective development of sequential intensification, weakening, and re‐intensification within a 2.5‐hr period. The temporal intensification was related not only to the development of a low‐level rear‐inflow jet but also to the dynamic interaction between the vertical environmental wind shear and the cold pool. The lifted rear‐inflow jet, which formed over a vertically stacked horizontal vorticity couplet behind the leading edge, provides a reasonable explanation for the intensification of the SLLR, resulting in the reduction of the cold pool circulation and enhancement of the convective updraft. Topographic forcing might play a crucial role in the re‐intensification of the SLLR, which suggests the importance of the cold pool and topography in structural features and convective evolution of the squall line‐like rain bands. Key Points: The squall line‐like rainband (SLLR) in a linear mesoscale convective system exhibited the characteristics of the typical squall lineThe SLLR was associated with a complex convective development of sequential intensification, weakening, and re‐intensificationThe cold pool and topography play crucial roles in structural features and convective evolution of the SLLR [ABSTRACT FROM AUTHOR]

This paper describes a technique for identifying hydrometeor particle types using airborne HIAPER Cloud Radar (HCR) and High Spectral Resolution Lidar (HSRL) observations. HCR operates at a frequency of 94 GHz (3 mm wavelength), while HSRL is an eye‐safe lidar system operating at a wavelength of 532 nm. Both instruments are deployed on the NSF‐NCAR HIAPER aircraft. HCR is designed to fly in an underwing pod and HSRL is situated in the cabin. The HCR and HSRL data used in this study were collected during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES). Comprehensive observations of the vertical distributions of liquid and mixed‐phase clouds were obtained using in situ probes on the aircraft and remote sensing instruments. Hydrometeor particle types were retrieved from HCR, HSRL, and temperature fields with a newly developed fuzzy logic particle identification (PID) algorithm. The PID results were validated with in situ measurements collected onboard the HIAPER aircraft by a 2D‐Stereo (2D‐S) cloud probe. Particle phases derived from the PID results compare well with those obtained from the 2D‐S observations and agree in over 70% of cases. Size distributions are also consistent between the two methods of observation. Knowledge of the particle type distribution gained from the PID results can be used to constrain microphysical parameterizations and improve the representation of cloud radiative effects in weather and climate models. Key Points: A fuzzy logic hydrometeor particle identification method is presented that uses airborne cloud radar and lidar observationsThe method classifies cloud and precipitation particles into eleven types of different phases and sizesA comparison of hydrometeor classifications with in situ measurements of Southern Ocean clouds show agreement in over 70% of cases [ABSTRACT FROM AUTHOR]

A knowledge of melting layer (ML) is crucial for the accurate estimation of rainfall and the study of microphysical characterization of the cloud. Based on 5 years (2016–2020) of measurements of cyclones over the Western North Pacific from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, the characteristics and microphysical processes of ML are investigated. Our research shows that the height of the top boundary of ML is higher than the freezing level (0 degrees centigrade level, FL), and the Ze of freezing level is larger than that of the top of ML. The height of ML is the result of the combined effect of latitude and stage of cyclone. When the ice particles pass through the ML, the mass‐weighted mean diameter (Dm), the generalized intercept parameter (Nw), and the distributions of the reflectivity factor (Ze) increase, indicating that growth mechanism and breakup process. Key Points: The height of melting layer in the cyclons region is higher than the 0°C isotherm heightThe melting layer in the cyclons region is different in different stages and latitudeThe microphyscics process in near the melting layer [ABSTRACT FROM AUTHOR]

A knowledge of melting layer (ML) is crucial for studying precipitation and hydrometeor. Based on the measurements of dual‐frequency ratio (DFRm) from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, we use the covariance wavelet transform (CWT) method to identify the ML top height (MLTH) through the DFRm profiles. Due to the influence of disturbance, the directly retrieved MLTH could be estimated mistakenly. To improve the reliability of the retrieved MLTH, a quality‐control algorithm based on the CWT method is developed. By comparing the MLTH with the corresponding 0°C isothermal heights, the retrieved MLTH are more reliable than the dual‐frequency precipitation radar (DPR) product, showing great potential in deploying the Global Precipitation Measurement DPR and the CWT method in the identification of the MLTH. Key Points: The profile of dual‐frequency ratio contains the information of the melting layer (ML)The covariance wavelet transform can be used to identify the MLA quality‐control algorithm was developed to improve the reliability of the results [ABSTRACT FROM AUTHOR]

Utilizing the measurements and retrievals from the Global Precipitation Measurement dual‐frequency precipitation radar data during the summer of 2014–2020, this study analyzes the precipitation microphysics for summer rainfall over the Yangtze‐Huai River Valley (YHRV) region in China with respect to the precipitation efficiency index (PEI). The results show that the mean near‐surface rain rate and PEI for convective precipitation are generally larger than that for stratiform precipitation. There are larger raindrops in near‐surface rainfall for convective precipitation than that for stratiform precipitation. The High PEI precipitation is characterized by the larger ratio of convective precipitation samples compared to that for the other two categories of precipitation. The mean mass‐weighted mean diameter (Dm) of the near‐surface raindrops increase along with the precipitation top height (PTH) in Moderate PEI precipitation for convective and stratiform precipitating clouds. The larger PEI indicates the larger occurrence of the heavy raindrops and strong radar echoes near the ground. The growth process of raindrops falling below the melting layer in the High PEI precipitation is dominated by collision‐coalescence while the collisional break up is the dominant process below the melting layer in Low PEI precipitation. It is further verified that the microphysical characteristics and mechanisms of summer rainfall over the YHRV region are similar to that of precipitation derived from tropical cyclones over the ocean during the monsoon period. Key Points: The near‐surface raindrops increase along with the precipitation top height in the moderate precipitation efficiency index (PEI) precipitationThe higher PEI indicates the larger occurrence of near‐surface heavy raindrops and strong radar echoesThe microphysical mechanisms in the summer precipitation process are analyzed in terms of various PEI [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability. Plain Language Summary: Second‐order statistics like atmospheric wave‐induced variances or fluxes of physical parameters calculated from lidar or radar data would have positive biases if detection‐noise‐induced variances or fluxes were not properly eliminated. If such biases remained, not only would atmospheric variances be overestimated, implying unrealistic wave activity and growth rates, but certain phenomena could be concealed, presenting misleading pictures of the atmospheric observations. While various methods have been developed to account for this, all have limitations to their use. This study summarizes the development, theory, and application procedures for three different methods, namely conventional variance subtraction, the spectral proportion method, and the interleaved method, which can be used to correct such biases. The newest of these methods is adapted from operating on the data time‐wise into an altitude‐wise approach, improving its applicability. The performances of the three methods are compared by calculating the variance‐based gravity wave potential energy density derived from lidar data taken at McMurdo, Antarctica. Their accuracies are assessed by using a forward model. This study aims to guide future research by providing information on how and when to apply each of these methods in order to enhance the outcomes of existing and future lidar/radar systems and datasets. Key Points: We demonstrate for the first time an altitude‐interleaved method for eliminating random‐noise‐induced biases from second‐order statisticsThe interleaved method is compared to the spectral proportion and variance subtraction methods to reveal strengths and limitations of eachLidar data from Antarctica & a forward model are used to assess accuracies and precisions of the three methods under different conditions [ABSTRACT FROM AUTHOR]

Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail. Key Points: For the first time, the stochastic multi‐cloud model in the Climate Forecast System version 2 model is tuned by the radar observationsThe new simulation improves the mean state climateThe necessity to use better transition time scales for improving climate simulation [ABSTRACT FROM AUTHOR]

Strong convection nowcasting has been gaining importance in operational weather forecasting. Recently, deep learning methods have been used to meet the increasing requirement for precise and timely nowcasting. One of the promising deep learning models is the convolutional gated recurrent unit (ConvGRU), which has been proven to perform better than traditional methods in strong convection nowcasting. Despite its encouraging performance, ConvGRU tends to produce blurry radar echo images and fails to model radar echo intensities that have multi‐modal and skewed distributions. To overcome these disadvantages, we tested the structural similarity (SSIM) and multiscale structural similarity (MS‐SSIM) indexes as loss functions. The SSIM and MS‐SSIM loss functions are composed of luminance, contrast, and structure and provide more information about the intensity, grade, and shape of the radar echo, which can reduce blurring. Due to multi‐layer downscaling, MS‐SSIM extracted more radar echo characteristics, and its extrapolation was the most realistic and accurate among all of the loss function schemes. Only the MS‐SSIM scheme successfully predicted strong radar echoes after 2 h, especially those at the rainstorm level. Plain Language Summary: The Convolutional Recurrent Neural Network model has been considered a pioneering work in the field of nowcasting. However, this model tends to predict blurred radar echo images and fails to model the radar echo intensity that has a multi‐modal and skewed distribution. In this paper, we adopt two measures of structural similarity (SSIM and MS‐SSIM) as loss functions to reduce the blurring problem caused by the mean square error (MSE) loss function for precipitation nowcasting. We have shown that the MS‐SSIM scheme is more efficient in capturing the spatiotemporal correlations, and has the best prediction performance, especially for heavier precipitation. Even for rotating moving radar echoes, the MS‐SSIM scheme has the best forecast performance Key Points: Convolutional gated recurrent unit model was selected to solve the problem of strong convective nowcastingUsing the structural similarity index and multiscale structural similarity (MS‐SSIM) index as a loss function can effectively reduce the blurring problem of extrapolation resultsOnly the MS‐SSIM scheme successfully predicted the strong radar echo after 2 h, especially for the radar echo at the rainstorm level [ABSTRACT FROM AUTHOR]

In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location‐specific precipitations. In contrast to QPE methods based on the Z–R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013–2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume‐to‐point framework performed comparably well in general cases and excelled in detecting heavy‐rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios. Plain Language Summary: Quantitative precipitation estimation (QPE) is a method of approximating the amount of rain that has fallen at a location or across a region. In most use cases, weather service providers use radar signals to estimate the amount of precipitation through the formula describing the relationship between radar reflectivity and the size of raindrop particles, Z–R relation. The state‐of‐the‐art QPE methods with adjusted Z–R relation are robust and accurate in general. Yet, they consider only the radar signal at a given location, which represented a point‐to‐point framework. In this study, we proposed a volume‐to‐point alternative that estimates the amount of rain by considering the signals in a broader spatial region and a longer time‐span. By using a deep neural network to process the large data volume, we demonstrated that the proposed method performed comparably well in general cases and excelled in detecting heavy‐rainfall events. This method could improve advanced warning for flash flooding and make water resource management more effective. In ongoing research, we seek to extend our approach to integrate the heterogeneous data sources and to be applied to precipitation forecasting. Key Points: We proposed a novel deep learning approach for estimating precipitation from a large data volume and demonstrated it with QPE from aggregated radar dataIn comparison to the operational QPE scheme, the proposed framework performed comparably well in general cases and excelled in detecting heavy‐rainfall eventsThe proposed framework can be further extended to integrate heterogeneous data sources and potentially improve the QPF in extreme precipitation scenarios [ABSTRACT FROM AUTHOR]

This paper presents for the first time results on winds, tides, gradients of horizontal winds, and momentum fluxes at mesosphere and lower thermosphere altitudes over southern Patagonia, one of the most dynamically active regions in the world. For this purpose, measurements provided by SIMONe Argentina are investigated. SIMONe Argentina is a novel multistatic specular meteor radar system that implements a Spread‐spectrum Interferometric Multistatic meteor radar Observing Network (SIMONe) approach, and that has been operating since the end of September 2019. Average counts of more than 30,000 meteor detections per day result in tidal estimates with statistical uncertainties of less than 1 m/s. Thanks to the multistatic configuration, horizontal and vertical gradients of the horizontal winds are obtained, as well as vertical winds free from horizontal divergence contamination. The vertical gradients of both zonal and meridional winds exhibit strong tidal signatures. Mean momentum fluxes are estimated after removing the effects of mean winds using a 4‐h, 8‐km window in time and altitude, respectively. Reasonable statistical uncertainties of the momentum fluxes are obtained after applying a 28‐day averaging. Therefore, the momentum flux estimates presented in this paper represent monthly mean values of waves with periods of 4 h or less, vertical wavelengths shorter than 8 km, and horizontal scales less than 400 km. Key Points: First observations of mesosphere and lower thermosphere dynamics over one of the most dynamically active regions in the worldEstimates of mean horizontal winds and their gradients are possible, thanks to the multistatic configurationMean momentum fluxes are estimated with vertical velocity estimates free of horizontal divergence contamination [ABSTRACT FROM AUTHOR]

The mesosphere and lower thermosphere (MLT) region is dominated globally by dynamics at various scales: planetary waves, tides, gravity waves, and stratified turbulence. The latter two can coexist and be significant at horizontal scales less than 500 km, scales that are difficult to measure. This study presents a recently deployed multistatic specular meteor radar system, SIMONe Peru, which can be used to observe these scales. The radars are positioned at and around the Jicamarca Radio Observatory, which is located at the magnetic equator. Besides presenting preliminary results of typically reported large‐scale features, like the dominant diurnal tide at low latitudes, we show results on selected days of spatially and temporally resolved winds obtained with two methods based on: (a) estimation of mean wind and their gradients (gradient method), and (b) an inverse theory with Tikhonov regularization (regularized wind field inversion method). The gradient method allows improved MLT vertical velocities and, for the first time, low‐latitude wind field parameters such as horizontal divergence and relative vorticity. The regularized wind field inversion method allows the estimation of spatial structure within the observed area and has the potential to outperform the gradient method, in particular when more detections are available or when fine adaptive tuning of the regularization factor is done. SIMONe Peru adds important information at low latitudes to currently scarce MLT continuous observing capabilities. Results contribute to studies of the MLT dynamics at different scales inherently connected to lower atmospheric forcing and E‐region dynamo related ionospheric variability. Plain Language Summary: The mesosphere and lower thermosphere (MLT) region is dominated by neutral wind dynamics with structure scales ranging from a few thousands of kilometers down to a few kilometers. In this work, we present a new state‐of‐the‐art ground‐based radar system using multistatic meteor scattering that allows tomographic studies of MLT wind dynamics at scales not possible before. Given the location of the radar network at the magnetic equator, its focus is on wind dynamics peculiar to equatorial latitudes. Two methods for estimating the mesospheric neutral wind field are used. One takes into account wind gradients in addition to mean wind (gradient method). The other estimates a spatially resolved wind vector field and uses an additional mathematical constraint that produces smooth wind field solutions (regularized wind field inversion method). Using the gradient method, the vertical wind estimate is improved. For the first time at MLT equatorial latitudes, parameters familiar to meteorologists, such as horizontal divergence and relative vorticity are obtained. Measurements from this new system have the potential to contribute to coupling studies of the atmosphere and the ionosphere at low latitudes. Key Points: Measurements of horizontal wind gradients at low‐latitude mesosphere and lower thermosphere altitudesThese gradients of the horizontal winds show strong temporal and altitude variability that are not observed at high latitudesImproved vertical winds are obtained using a gradient wind field method inherently free from horizontal divergence contamination [ABSTRACT FROM AUTHOR]

Reflectivity measurements from X‐band ground‐based radar (GR) are assessed using observations from the Ku‐band space‐borne radars (SRs) onboard the Tropical Rainfall Measuring Mission and Global Precipitation Measurement satellites as a reference. The GR is at Mandhardev (18.04°N, 73.86°E) in the Western Ghats region, and the evaluation period is from June to September of 2014 and 2018. At X‐band, the rainfall‐induced attenuation leads to significant signal loss. The rain attenuation correction of GR is performed based on the relationship between specific attenuation and reflectivity. Using T‐matrix scattering simulations, the specific attenuation and reflectivity are derived from the disdrometer data. Further, the systematic differences between reflectivity measured by GR and SR are minimized by performing the frequency scaling using scattering simulation. The comparison between corrected reflectivities of GR and SR is achieved by matching the resolution volumes and viewing geometry of both radars. The evaluation between corrected reflectivities of GR and SR shows reasonable agreement between them, indicating that the attenuation correction performs reasonably for GR. In rain regions, the GR bias lies between −2.6 and −1.8 dB for stratiform cases, while for convective samples, the GR bias varies from −2.4 to −0.7 dB relative to SR observations. The GR shows smaller reflectivity compared to SR at all heights. This study aims to implement the attenuation correction to long‐term X‐band radar data available in the Western Ghats region to study the orographic precipitation and convective system during monsoon. Key Points: For the first time, assessment of reflectivities between IITM's X‐band radar and TRMM‐GPM Ku‐band radars is carried outThe bias in GR attenuated corrected reflectivity is within −2.7 dB relative to SR measurementsThis study provides a framework to have attenuated corrected reflectivity for better quantitative application of reflectivity data [ABSTRACT FROM AUTHOR]

Ground‐based radar sounders are used to characterize the dynamics and chemistry of Earth's upper atmosphere by using measurements of ionospheric peak electron density (NmF2) and its associated altitude (hmF2). Continuous sounder observations of the E and F regions of the ionosphere have been carried out regularly at dozens of stations worldwide since the midtwentieth century. A deep understanding of short‐ and long‐term upper atmospheric variability depends on a fundamental understanding of these observational data. The manual analysis of historical analog (predigital age) ionograms to derive the plasma frequency profiles and the ionospheric parameters hmF2 and NmF2 is a tedious procedure and susceptible to human error. In order to better understand this human error, a study is conducted in which ionograms from vertical sounders are manually scaled by a team of ionospheric researchers. The results of the study are then used to estimate the variability of the hand‐scaled ionospheric parameters foF2 and foE. Those results are then used to estimate the downstream impact on ionospheric models that use foF2 and foE as input. The results demonstrate that there can be large variability in the manual scaling of foF2 and fmaxE from vertical incidence ionograms. However, the participants did typically better than 5% uncertainty for benign ionograms. A long‐term analysis of hmF2 modeling exhibits low sensitivity to statistical errors imposed on foF2 and foE, but a short‐term analysis showed that modeled hmF2, neutral winds, and electron densities can be very different when small adjustments are made to foF2 and fmaxE. Key Points: Human‐introduced error in older ionospheric data sets can be important over short time periods, but not as important over long time periodsInclude a historical record of the experts who generated each level of data; all low‐level data products should be archivedThoroughly document all potential sources of experimental uncertainty, including data lineage/provenance [ABSTRACT FROM AUTHOR]

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES. Key Points: New method is proposed to represent model error of background error statisticsPerformance of proposed method improves forecast skill for an extreme eventBest results are obtained when using zonal and meridional momentum control variables [ABSTRACT FROM AUTHOR]

This study examines the impacts of humidity adjustment in a cloud analysis system on the analysis and forecast of a squall line that occurred in southeast China on 23–24 April 2007. Radial velocity data are assimilated using the ARPS three‐dimensional variational system while reflectivity data are assimilated by a cloud analysis system. Experiments with two different humidity adjustment schemes are performed, with the original and enhanced versions. Another experiment does not adjust moisture. Both schemes generally decrease the humidity in front of the convective line and increase the humidity within the convective and stratiform regions of squall line compared to no humidity adjustment, and the original scheme produces the higher humidity within precipitation regions, especially the stratiform region. Both schemes improve the forecast of squall line structure, including the leading convective line, a transition zone, and a trailing stratiform region. Among the three experiments, the enhanced scheme produces the highest precipitation forecast skill. The latent heating rates are also diagnosed to investigate the microphysical responses to the humidity adjustment. The cooling outside of the observed precipitation regions corresponding to the humidity reduction also acts to suppress spurious precipitation. Water vapor condensation into cloud water and cloud water evaporation generally dominate the latent heating/cooling below the freezing level. Compared to the enhanced scheme, the original scheme releases much more latent heat in the middle troposphere, causing more warming. This is linked to the higher cloud water condensation rate, due to the higher amount of moisture addition/adjustment by the original scheme. Key Points: An enhanced vertical velocity‐dependent moisture adjustment scheme produces better squall line structures in both analysis and forecastThe "overwarming" issue associated with the original scheme is linked to more latent heat release associated with higher condensation rateCondensation into and evaporation of cloud water dominates latent heating/cooling below the freezing level [ABSTRACT FROM AUTHOR]

Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar instrument—installed on the National Aeronautics and Space Administration satellite CloudSat—allow the retrieval of snowfall rates in high‐latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat‐derived terrestrial snow estimates is presented at four Environment and Climate Change Canada weather stations situated in the Arctic for the common period 2007–2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5‐mm snow water equivalent annually. Monthly time series exhibit correlations above 0.5 and root‐mean‐square error below 10‐mm snow water equivalent at the two highest‐latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70°N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat's accumulation estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high‐latitude regions above 70°N.Key Points: Taking repeated in situ snowfall measurements across the Arctic is a challenging and expensive taskCloudSat provides spaceborne retrievals of snowfall rates throughout the ArcticAggregating CloudSat overpasses onto a spatial grid allows us to gain new insights into Arctic snow [ABSTRACT FROM AUTHOR]

All‐sky interferometric meteor radar has been operated worldwide for the measurements of neutral winds in the mesopause region. In this paper, we employ an all‐sky meteor radar and an ionospheric radar, which are situated at Ledong (18.4°N,109°E) and Sanya (18.3°N,109.6°E), respectively, and both have the interferometry capability, to study the zonal structure and drifts of ionospheric E region irregularities producing continuous and quasiperiodic echoes. We use the collocated optical meteor observations to calibrate the radar system phase offsets for interferometry measurements. A good correspondence between the irregularity measurements from both radars was found. The observations show that the quasiperiodic striations with both negative and positive slopes in radar range‐time‐intensity maps were produced by spatially separated irregularity structures, which drift through the radar field of view. The continuous echoes were due to irregularities generated locally. Specifically, comparing with ionospheric radar, all‐sky meteor radar has a much wider field of view that can provide E region irregularity information over a large zonal region of more than 300 km. It is suggested that all‐sky meteor radar provides a capability to probe ionospheric E region irregularities and to trace their movements. Plain Language Summary: The interferometry observations of ionospheric E region irregularities from a conventional all‐sky meteor radar and an ionospheric radar are reported. The results show the feasibility of making ionospheric E region irregularity measurements and tracing their movements over a large zonal field of view of more than 300 km with conventional meteor radars. Since there are tens of such meteor radars continuously operated at middle and low latitudes, the interferometry observations with these meteor radars would contribute to a better understanding of worldwide ionospheric E region irregularities. Key Points: Interferometry observations of ionospheric E region irregularities are made with an all‐sky meteor radarThe zonal structure and movements of E region irregularities over a zonal field of view of more than 300 km are revealed by meteor radarComparison of meteor radar E region irregularity observations with ionospheric radar shows a good correspondence [ABSTRACT FROM AUTHOR]

This paper proposes an improved algorithm which combines neural networks with diverse 3‐D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X‐band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high‐resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self‐diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state‐of‐art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F‐score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real‐time weather analysis for the precipitation classification. Plain Language Summary: In recent years, networked radar become the popular way to overcome some of the inherent shortcomings of single radar. There are different scanning strategies for convective and stratiform clouds, and it is difficult to accurately distinguish between convective clouds and stratiform clouds, and when the accuracy is high, there will be a problem of low‐recognition rate. Our algorithm utilizes a variety of features derived from radar products that have a good distinction between the two categories. At the same time, combined with neural network, its powerful nonlinear fitting and high speed make the recognition speed fast and the recognition result is high. The recognition result of our algorithm is higher than the recognition result of the popular fuzzy logic algorithm, and its recognition speed is even several times that of the fuzzy logic algorithm. Key Points: Six distinguishable features are extracted from radar echoes in order to well describe the horizontal and vertical structure of the cloudThe neural network is carefully diagnosed according to analysis of bias and variance of classifier to avoid overfitting and underfittingThe algorithm is evaluated by comparing the classification performance with those of three state‐of‐art algorithms subjectively and statistically [ABSTRACT FROM AUTHOR]

This study presents a simulated simultaneous retrieval of mass mean cloud ice particle effective diameter, ice water content, water vapor, and temperature profiles using a combination of a 94‐GHz cloud radar and multifrequency (118, 183, 240, 310, 380, 664, and 850 GHz) millimeter‐ and submillimeter‐wave radiometers from a space platform. The retrieval capabilities and uncertainties of the combined radar and microwave radiometers are quantified. We show that this combined active and passive remote sensing approach with SmallSat technologies addresses a gap in the current state‐of‐the‐art remote sensing measurements of ice cloud properties, especially deriving vertical profiles of ice cloud particle sizes in the atmosphere together with the ambient thermodynamic conditions. Therefore, this new approach can serve as a plausible candidate for future missions that target cloud and precipitation processes to improve weather forecasts and climate predictions. Key Points: This study presents a simulated ice cloud retrieval by a radar and multifrequency microwave radiometer space platformThis combined active and passive remote sensing approach outperforms current state‐of‐the‐art remote sensing of ice cloud propertiesIt serves as a plausible candidate for future missions that target cloud and precipitation processes [ABSTRACT FROM AUTHOR]

The capability to detect the state of snowflake riming reliably from remote measurements would greatly expand the understanding of its global role in cloud‐precipitation processes. To investigate the ability of multifrequency radars to detect riming, a three‐dimensional model of snowflake growth was used to generate simulated aggregate and crystal snowflakes with various degrees of riming. Three different growth scenarios, representing different temporal relationships between aggregation and riming, were formulated. The discrete dipole approximation was then used to compute the radar backscattering properties of the snowflakes at frequencies of 9.7, 13.6, 35.6, and 94 GHz. In two of the three growth scenarios, the rimed snowflakes exhibit large differences between the backscattering cross sections of the detailed three‐dimensional models and the equivalent homogeneous spheroidal models, similarly to earlier results for unrimed snowflakes. When three frequencies are used simultaneously, riming appears to be detectable in a robust manner across all three scenarios. In spite of the differences in backscattering cross sections, the triple‐frequency signatures of heavily rimed particles resemble those of the homogeneous spheroids, thus explaining earlier observational results that were compatible with such spheroids. [ABSTRACT FROM AUTHOR]

The Super Dual Auroral Radar Network (SuperDARN) is an international low‐power high‐frequency (HF) radar network, which provides continuous observations of the motion of plasma in the ionosphere. Over the past 15 years, the network has expanded dramatically in the middle latitudes of the Northern Hemisphere to improve the observation capabilities of the network during periods of strong geomagnetic disturbance. However, a large coverage gap still exists in the middle latitudes. A newly deployed middle‐latitude HF radar in China (the Jiamusi radar) is about to join the network. This paper presents the first observation of the ionospheric convection from the Jiamusi radar during the strong geomagnetic storm on 26 August 2018. The Jiamusi measurements are compared with the simultaneous measurements from the SuperDARN Hokkaido East radar. The features of the high‐velocity westward flows including the equatorward expansion and variation tendency of the line‐of‐sight velocities observed by the two radars are consistent with each other. According to joint analysis with auroral images, we can confirm that the westward flows observed by the two radars are sunward return flows of the duskside convection cell in the auroral region. The impact the Jiamusi data had on the calculation of SuperDARN convection patterns is also examined. The results show that the inclusion of the Jiamusi data can increase the number of gridded line‐of‐sight velocity measurements by up to 24.42%, the cross‐polar cap potential can be increased by up to 13.90% during the investigated period. Key Points: A new middle‐latitude HF coherent scatter radar was deployed in Jiamusi, ChinaThe first observation of the ionospheric convection from the radar during a strong storm is presented and validatedThe inclusion of the data into calculation of SuperDARN convection increases the cross‐polar cap potential [ABSTRACT FROM AUTHOR]