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Abstract In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

This study presents dual polarization spectral analysis over updraft environments in thunderstorms. The composition and interaction of particle in updrafts environments are analyzed using the expansion that the spectral polarimetric provides in radar variables. Updrafts are identified using dual Doppler analysis and polarimetric signatures such as $Z_{\text{dr}}$ columns. Spectral techniques are presented to estimate the spectrum in updraft and mixed-phase environments. A separation in the spectrum of melting ice and rain was observed in the analyzed updrafts. This separation in the spectrum allows us to study the polarimetric characteristics and interaction of melting ice and rain. Furthermore, scattering simulations were carried out to validate the spectral signatures attributed to melting ice. It was found that small ice particles mixed with rain can produced similar polarimetric characteristics as the one observed in the spectral analysis. Conceptual models based on the spectral observations are presented to illustrate the interaction of ice particles with a recirculation and a vigorous updraft.

Antibiotic development is not new to the medical community, but the emergence of antibiotic resistance has become the most serious threat to global health and food security. It results in longer hospital stays, more expensive medical treatments, and greater mortality rates around the world. The basic categories used to describe antibiotic resistance originating from both natural and genetically driven processes are natural, acquired, cross-resistance, multidrug, and pan-drug resistance. Bacterial intrinsic resistance is characterized by continued improvement of resistance mechanisms via cell wall structure and other cellular features. Resistant drug uptake, target site change, efflux pump mechanism, and target site mutation are examples of resistance mechanisms. The presence of resistant bacteria and antibiotic residues in the environment requires urgent global action to combat antimicrobial resistance (AMR). Discovering new antibiotics is not the only prerequisite for dealing with this scenario; continuous national surveillance of antibiotic resistance gene level dissemination is also necessary. This review provides the most up-to-date status of the mechanisms of AMR against several antibiotics on the market and how the resistant bacteria have evolved with diverse mechanisms for their survival. It also discusses the significant clinical consequences and the impact of resistant bacteria exposure on public health. Further, it puts together a global understanding of the prevalence of resistant genes and offers recommendations to stop the further spread with some guidelines.

Abstract National Aeronautics and Space Administration's Investigations of Convective Updrafts (INCUS) mission aims to document convective mass flux through changes in the radar reflectivity (ΔZ) in convective cores captured by a constellation of three Ka‐band radars sampling the same convective cells over intervals of 30, 90, and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface‐based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent ΔZ structure with measurable signal (>5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable ΔZ signals. This unique demonstration of reflectivity time‐lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations.

The objective of this study is to cross-validate observations over precipitating systems by microwave radiometers on the temporal experiment for storms and tropical systems demonstration (TEMPEST-D) CubeSat mission and the global precipitation measurement microwave imager (GMI). The purpose of this article is twofold: first, to show consistency between TEMPEST-D and GMI observations, and second, to demonstrate the potential to enhance temporal sampling when TEMPEST-D and GMI observations are merged. Two cross-validation methodologies were employed. The first cross-validation methodology is to quantitatively compare TEMPEST-D and GMI brightness temperature (TB) observations over precipitation systems using a priori spatiotemporal constraints. The comparative analysis showed that the two instruments’ TB observations have similar probability distributions, with a mean absolute difference of 2.9 K. The second cross-validation methodology is to quantitatively compare TEMPEST-D and GMI TB observations over tropical cyclone systems. Three storm cases were analyzed in this comparative study. The analysis showed that the structure and intensity of the storms are similar in TEMPEST-D and GMI TB observations, and the overall average correlation coefficient (r) is 0.9. Combining TEMPEST-D and GMI TB observations over the hurricane systems increased the sampling frequency by a factor of 2.5, compared to using the GMI data alone.

In this article, the melting ice attenuation in $C$-band radars is studied. Dual-$C$-band radar observations from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes With Adaptive Ground Observations (RELAMPAGO) campaign are used. Melting ice regions in the radar data are identified using spectral decomposition, Doppler winds, and hydrometeor classification. The attenuation in regions that show polarimetric radar signatures of melting ice is computed using a network-based approach. The network-based approach to computing the parameters for attenuation correction is validated by comparing with disdrometer data over rain. In addition, the attenuation correction parameterization over melting was compared with previous studies.

The International Collaborative Experiment during the PyeongChang Olympics and Paralympic winter games 2018 took place in the PyeongChang region of South Korea. The main goal of this field campaign was to study winter precipitation in an environment that has complex terrain. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was calibrated and deployed in this field campaign. The positioning error of the radar was calibrated to be within 0.1°. The D3R was deployed for more than four months and was able to capture many interesting snowfall events along with a few rain events. In this article, the deployment and performance of the D3R during the campaign are discussed. The snowfall events captured by the D3R are discussed in detail to interpret the microphysics from a radar's perspective. The reflectivity–snowfall rate relationship is derived at the Ku band, and the snow accumulation computed is in good agreement with a precipitation gauge that was deployed near the radar. The benefit of the dual-frequency ratio for identifying the precipitation particle types is briefly introduced using the data from a large snow event on 28th February 2018. The vertical profile D3R data for this snow event are studied for detecting the presence of pristine-oriented ice crystals in the mixed hydrometeor phase conditions. Various other instruments, such as X-band radar and disdrometers, were deployed in the campaign. The D3R data are compared with the MxPOL X-band radar, and the reflectivity values match within a couple of dB in the common volume region.

North East Monsoon (NEM) is the major source of rainfall for the south-eastern parts of peninsular India. Short time rainfall prediction data (i.e., nowcasting) are based on the observations from Doppler weather radars which has a high spatial and temporal resolution. This study focuses on the short-term ensemble prediction system using weather radar data to predict precipitation during the NEM and is the first of its kind in the Indian region to make an assessment of the operational performance of the prediction system. Six rainfall events have been studied for the assessment of short-term prediction system where the precipitation systems are different and include a tropical storm observed over different days during the 2022 NEM season. To assess the performance of the system, Fractional Skill Scores (FSS) at a 1 km window have been computed for a lead time of 0–2 h for all the rainfall events with more than 750 samples using different optical flow methods and ensemble sizes. The best average skill score and maximum skill score obtained at a 2 h lead time is 0.65 and 0.78 for tropical storms, 0.5 and 0.78 for stratiform and 0.15 and 0.38 for convective precipitation. It has found that the performance of the model is best for precipitation systems that are widespread and have a longer life period.

The research article demonstrates the role of fillers in the evolution of the microstructure and mechanical integrity of dissimilar joints of stabilized austenitic stainless steels. Pulsed direct current (PDC) gas tungsten arc welding (GTAW) technique was adopted to obtain the joints of dissimilar grades of stabilized austenitic stainless steels such as AISI 321 and AISI 347. The joints of these dissimilar grades of stainless steels were achieved using a ferritic-austenitic stainless-steel filler ER2553 and Ni-rich austenitic fillers ERNiCrMo-4, ERNiCrMo-10. Delta ferrite was observed in heat-affected areas (HAZ) on both sides of stainless steels, regardless of joints. Precipitation of Mo–Nb, Mo–W rich phases was noticed in the fusion zones (FZ) while adopting Ni-based austenitic fillers. During the tensile load, the fractures occurred on the base metal side of the AISI 321 for the Ni-based fillers and on the FZ while using the ER2553 fillers. The joint efficiencies achieved by these dissimilar joints were 103.2%, 128.11% and 116.16% for ER2553, ERNiCrMo-4 and ERNiCrMo-10 fillers respectively. The joints established with the ERNiCrMo-4 filler improved the notch's impact resistance in ambient temperature and below zero conditions compared to other weld seams.

This article presents cross validation of nearly simultaneous observations between the Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) and Radar in a CubeSat (RainCube) satellite microwave sensors over precipitation systems. RainCube senses the atmosphere using a Ka-band nadir-pointing radar, and TEMPEST-D senses using multifrequency millimeter-wave radiometers. Nine precipitation systems were used in this cross-validation study. Occurring over nearly a two-year period, these storms were scattered over many regions of the world from the Tropics to the midlatitudes. A shift correction algorithm was developed to remove the uncertainty between the two sensors’ observations due to the time difference and the storm motion between the two instrument overpasses. Correlation coefficients were calculated between self-normalized, inverted RainCube cumulative reflectivity and self-normalized TEMPEST-D brightness’ temperatures. As expected, these correlation coefficients are consistently higher for the four TEMPEST-D high-frequency channels than for the single low-frequency channel. The shift correction algorithm improved the average correlation coefficient by 19% for the four high-frequency channels and by 58% for the single low-frequency channel. The average correlation coefficient after shift correction is 0.76 for TEMPEST-Ds four high-frequency channels and 0.54 for the single low-frequency channel. The comparisons demonstrated high consistency between the TEMPEST-D and RainCube observations, even though the two microwave sensors’ fundamental physics is different; one is passive, and the other is active.

In this study, a machine-learning model was used to produce surface rainfall estimates from Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) microwave radiance observations from a CubeSat. The machine-learning model is based on an artificial neural network (ANN). The space-borne TEMPEST-D sensor performed brightness temperature (TB) observations at five frequencies (i.e., 87, 164, 174, 178, and 181 GHz) during its nearly three-year mission. The TEMPEST-D TBs were used as inputs, and the multiradar/multisensor system (MRMS) radar-only quantitative precipitation estimation product at the surface was used as the ground truth to train the ANN model. A total of 19 storms were identified that were simultaneously observed by TEMPEST-D and ground weather radar over the contiguous United States. The training dataset used 14 of the 19 storm cases. The other five storm cases, consisting of three continental storms and two land-falling hurricanes, were used for independent testing. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the ground radar measurement of the storm. This study showed that the TEMPEST-D TBs captured storm features as well as current-generation satellite sensors, such as the global precipitation mission microwave imager. The results of this study demonstrated that the rainfall estimated from TEMPEST-D matches well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The average structural similarity index measure score for the five independent test cases is 0.78.

Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

An EF1 tornado associated with tropical cyclone (TC) Ewiniar hit Dali Town, Foshan, Guangdong Province at 06:03 UTC on June 8, 2018, and the first special tornado warning was issued to five towns at 05:05 UTC. This article utilizes minute-scale observations from an X-band dual-polarization radar and measurements from an S-band Doppler radar to resolve the polarization characteristics associated with this tornado. In addition, second-scale atmospheric pressure data obtained from micropressure gauge and NCEP FNL (Final) Operational Global Analysis data are used to investigate the synoptic conditions and features of gravity waves (GWs). The conspicuous features of the descending reflectivity core, Doppler velocity couplet, Z$_{\text{DR}}$ arc, K$_{\text{DP}}$ foot, and the separation of the Z$_{\text{DR}}$ arc and K$_{\text{DP}}$ foot are detailed to quantify the tornadic evolution. The amplitude fluctuation of the GWs suddenly increased to 77.3 Pa, 2 h before the tornado occurred. Two focus regions with K$_{\text{DP}}$ values greater than 6$^\circ$/km are discussed by combining the Doppler velocity couplet and Z$_{\text{DR}}$ arc. The separation distance of the Z$_{\text{DR}}$ arc and K$_{\text{DP}}$ foot was approximately 2.1 km. The appearance of these features may be indicative of fundamental processes intrinsic to tornado storms.

Passive microwave satellite observations provide critical information for global forecast models, particularly in cloudy and/or precipitating conditions. The limited temporal sampling provided by current operational polar orbiters cannot capture rapidly changing conditions such as the development of convective storms. This is a significant issue for open-ocean weather systems such as tropical cyclones and hurricanes that can only be effectively monitored from satellites. The recent development and demonstration of miniaturized microwave radiometers on-board low-cost CubeSat satellites has the potential to dramatically improve the temporal and spatial sampling of all-sky microwave observations by deploying a substantial constellation of satellites in low Earth orbit. Two constellations of 60 CubeSats in 550 km orbits are compared to the current operational microwave sensors. One approach employs all polar orbiters, while the other approach uses multiple inclination orbits for increased sampling over convective storm regions. Both approaches reduce average revisit times to approximately 20–30 min globally, and the multi-inclination approach also provides irregular 5–10 min sampling over selected latitudes. Improved global temporal sampling would provide all-sky observations to global forecast models over rapidly changing environments, while millimeter-wave observations over convective storm regions would be valuable for both forecasting and studying the development of convective storms. This article demonstrated that a constellation of low-cost CubeSats with microwave radiometers has the potential to provide equivalent temporal resolution to that observed from sensors on geostationary orbit .

The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (Dm), the shape factor (mu), the normalized intercept parameter (Nw), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall.

Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

Study region: This study region is the Napa River basin in California whose antecedent soil moisture states and precipitation magnitudes are primary drivers to occur extreme floods. Study focus: This study assessed the influence of antecedent moisture condition on flood frequency, based on an experimental application scheme and pre-processing. For this purpose, T-year flood simulations were conducted using a distributed hydrologic model. Distributed precipitation patterns which have an amount of precipitation corresponding to a specific T-year return period were generated by representative radar-based precipitation fields and precipitation frequency analysis. Dry, normal, and wet of antecedent moisture condition were applied to each T-year flood simulation to reflect variable initial soil moisture states. New hydrological insights for the region: The relationship among flood frequency, antecedent moisture condition, and precipitation frequency was derived for a specific target storm event. For normal antecedent moisture states, the relation showed that T-year precipitation could generate floods having return intervals nearly identical to those derived using gage records. For saturated soil conditions, a 7-year precipitation event could trigger a 100-year flood. Conversely, a 200-year precipitation event with dry soil conditions might generate only a 15-year flood event. The results emphasize the importance of soil moisture to flood runoff and suggest that soil-moisture monitoring could aid in improving flood forecasting. Keywords: Flood frequency, Antecedent moisture condition, Precipitation frequency, T-year flood simulation, Distributed hydrologic model, Radar-based precipitation data

High-spatial-resolution weather radar observations are of primary relevance for hydrological applications in urban areas. However, when weather radars are located within metropolitan areas, partial beam blockages and clutter by buildings can seriously affect the observations. Standard simulations with simple beam propagation models and digital elevation models (DEMs) are usually not able to evaluate buildings' contribution to partial beam blockages. In recent years airborne laser scanners (ALSs) have evolved to the state-of-the-art technique for topographic data acquisition. Providing small footprint diameters (10–30 cm), ALS data allow accurate reconstruction of buildings and forest canopy heights. Analyzing the three weather C-band radars located in the metropolitan area of Helsinki, Finland, the present study investigates the benefits of using ALS data for quantitative estimations of partial beam blockages. The results obtained applying beam standard propagation models are compared with stratiform 24 h rainfall accumulation to evaluate the effects of partial beam blockages due to constructions and trees. To provide a physical interpretation of the results, the detailed analysis of beam occultations is achieved by open spatial data sets and open-source geographic information systems.

Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel2 disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (ρ), snowfall rate (S), and ice water content (IWC) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (Dm, log10Nw, and μ) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The Dm decreases and log10Nw and μ increase as the density increases. The Dm and log10Nw increase and μ decreases with the increase of snowfall rate. The power-law relationship between ρ and Dm is obtained and the relationship between S and IWC is also derived.

An extremely heavy rainfall event hit Guangdong province, China, from 27 August to 1 September 2018. There were two different extreme rain regions, respectively, at the Pearl River estuary and eastern Guangdong, and a record-breaking daily precipitation of 1056.7 mm was observed at Gaotan station on 30 August. This paper utilizes a suite of observations from soundings, a gauge network, disdrometers, and polarimetric radars to gain insights to the two rainfall centers. The large-scale meteorological forcing, rainfall patterns, and microphysical processes, as well as radar-based precipitation signatures are investigated. It is concluded that a west-moving monsoon depression played a critical role in sustaining the moisture supply to the two extreme rain regions, and the combined orographic enhancement further contributed to the torrential rainfall over Gaotan station. The raindrop size distributions (DSD) observed at Zhuhai and Huidong stations, as well as the observed polarimetric radar signatures indicate that the rainfall at Doumen region was characterized by larger raindrops but a lower number concentration compared with that at Gaotan region. In addition, the dual-polarization radars are used to quantify precipitation intensity during this extreme event, providing timely information for flood warning and emergency management decision-making.

This study aims to address hydrological processes and impacts of an atmospheric river (AR) event that occurred during 15−18 February 2004 in the Russian River basin in California. The National Water Model (NWM), a fully distributed hydrologic model, was used to evaluate the hydrological processes including soil moisture flux, overland flow, and streamflow. Observed streamflow and volumetric soil water content data were used to evaluate the performance of the NWM using various error metrics. The simulation results showed that this AR event (15−18 February 2004) with a long duration of precipitation could cause not only deep soil saturation, but also high direct runoff depth. Taken together, the analysis revealed the complex interaction between precipitation and land surface response to the AR event. The results emphasize the significance of a change of water contents in various soil layers and suggest that soil water content monitoring could aid in improving flood forecasting accuracy caused by the extreme events such as the AR.

Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting.

Flooding is one of the most common natural hazards that produce substantial loss of life and property. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. The US National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is dedicated to revolutionizing our ability to observe, understand, predict, and respond to hazardous weather events, especially in the lower atmosphere. CASA's technology enables precipitation observation close to the ground and QPE is one of the important products generated by the system. This paper describes the CASA QPE system built on the various underlying technologies of networked X-band radar systems providing high-resolution (in space and time) measurements, using the rainfall products from the radar. Evaluation of the networked rainfall product using 5 yr of data from the CASA IP-1 test bed is presented. Cross validation of the product using 5 yr of data with a gauge network is also provided. The validation shows the excellent performance of the CASA QPE system with a standard error of 25% and a low bias of 3.7%. Examples of various CASA rainfall products including instantaneous and hourly rainfall accumulations are shown.

The Global Precipitation Measurement (GPM) mission Core Observatory is equipped with a dual-frequency precipitation radar (DPR) with capability of measuring precipitation simultaneously at frequencies of 13.6 GHz (Ku-band) and 35.5 GHz (Ka-band). Since the GPM-DPR cannot use information from polarization diversity, radar reflectivity factor is the most important parameter used in all retrievals. In this study, GPM’s observations of reflectivity at dual-frequency and instantaneous rainfall products are compared quantitatively against dual-polarization ground-based NEXRAD radars from the GPM Validation Network (VN). The ground radars, chosen for this study, are located in the southeastern plains of the U.S.A. with altitudes varying from 5 to 210 m. It is a challenging task to quantitatively compare measurements from space-based and ground-based platforms due to their difference in resolution volumes and viewing geometry. To perform comparisons on a point-to-point basis, radar observations need to be volume matched by averaging data in common volume or by re-sampling data to a common grid system. In this study, a 3-D volume matching technique first proposed by Bolen and Chandrasekar (2003) and later modified by Schwaller and Morris (2011) is applied to both radar data. DPR and ground radar observations and products are cross validated against each other with a large data set. Over 250 GPM overpass cases at 5 NEXRAD locations, starting from April 2014 to June 2018, have been considered. Analysis shows that DPR Ku- and Ka-Band reflectivities are well matched with ground radar with correlation coefficient as high as 0.9 for Ku-band and 0.85 for Ka-band. Ground radar calibration is also checked by observing variation in mean biases of reflectivity between DPR and GR over time. DPR rainfall products are also evaluated. Though DPR underestimates higher rainfall rates in convective cases, its overall performance is found to be satisfactory.

Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors.

A new model-based iterative technique to correct for attenuation and differential attenuation and retrieve rain rate, based on a neural-network scheme and a differential phase constraint, is presented. Numerical simulations are used to investigate the efficiency and accuracy of this approach named NIPPER. The simulator is based on a T-matrix solution technique, while precipitation is characterized with respect to shape, raindrop size distribution and orientation. A sensitivity analysis is performed in order to evaluate the expected errors of this method. The performance of the proposed methodology on radar measurements is evaluated by using one-dimensional Gaussian shaped rain cell models and synthetic radar data derived from disdrometer measurements. Numerical results are discussed in order to evaluate the robustness of the proposed technique.