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Historical data discovery is a challenging task for any study in radar meteorology when the region of interest is recorded in weather observations. Weather radars exist in overlapping networks around the globe and are observing the atmospheric conditions around the clock. The observations are stored according to date, time, and location, as opposed to an indexing scheme based on the phenomena present in the scans themselves. Performing feature-based searches in these voluminous data sets is, at current, impossible. This research seeks to enable users seeking to study such phenomena as a mechanism for locating events of interest by leveraging recent progress from the fields of transfer learning and computer vision. Specifically, this work illustrates a methodology for performing image classification of precipitation regimes on colormapped weather radar scan images, as opposed to the raw data itself. This system reduces the data needed to perform this classification by two orders of magnitude, increasing throughput and democratizing usage of the deep learning tools for this task by allowing training and testing of the models on modest compute systems.

It still remains a challenge to obtain accurate rainfall estimates for weather radar on extreme events. In this letter, a hierarchical Bayesian approach is proposed for bias correction of the weather service surveillance radar (WSR-88D) dual-polarization (Dual-Pol) rainfall estimates in a hurricane. Based on various assumptions of the probabilistic likelihood function of hourly rainfall and spatial dependences between residual errors, four specific models are developed and intercompared to quantify the bias of the existing WSR-88D Dual-Pol rainfall product. The case of Hurricane Irma in Melbourne, Florida, is used to demonstrate the proposed technique. It is found that Bayesian analysis is capable of improving the Dual-Pol rainfall estimates and quantifying the associated predictive uncertainty: both root-mean-square error and normalized mean absolute error are declined by at least 30% after the Bayesian correction at the validation sites. Additionally, the consideration of the spatial Gaussian process in the spatial error model achieves a better skill score compared to the model without the spatial Gaussian process, and the usage of spatial Gaussian process has a positive impact on the Bayesian rainfall estimates at short distance from ground reference sites in regions of interest.

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas–Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.

Nowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.

Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes. However, the long-range, S-band operational radar network has severe limitations in providing high-resolution observations in the lower atmosphere due to the earth curvature. This paper presents an innovative technique of using shorter-range, low-power, fast scanning X-band radar for monitoring wildfires. Sample observations from wildfire events in southern China show that the polarimetric radar correlation coefficient of wildfire echoes varies from 0.55 to 0.75, with an average value of 0.61; the differential reflectivity varies from 2.28 to 5.08 dB with an average value of 3.99 dB; the differential propagation phase varies from 40.27° to 69.27°, with an average value of 56.66°; and all observables exhibit subtle changes at different scanning elevation angles. In addition, changes in echo intensity observed by higher elevation scans lag behind those observed by lower elevation scans, and the change of wildfire area lags behind the variation of echo intensity. Given the emerging interest in deploying such compact, cheaper X-band systems for urban hydrometeorological applications, it is expected that these radars, customized with flexible scan strategy, will provide new opportunities and advantages in wildfire detection and mitigation.

The characteristics of microphysical processes of a severe winter storm that occurred on the Korean Peninsula on 12 December 2013 was studied in this work for the first time via X-band dual-polarization weather radar observations. A new range–height indicator (RHI) scan-based quasi-vertical profile methodology, in which polarimetric radar variables were averaged at each height of the RHI scan, was introduced to investigate the snow microphysics, and the obtained polarimetric radar signatures served as fingerprints of the dendritic growth, aggregation, and riming processes. Enhanced differential reflectivity (Zdr) and specific differential phase shift (Kdp) bands were detected near the −15°C isotherm, which signified the growth of dendrites or platelike crystals. The observed correlation between the increases in the reflectivity factor at horizontal polarization Zh and copolar correlation coefficient ρhv and the decreases in Zdr and Kdp magnitudes at lower heights suggested the occurrence of the aggregation process. The combination of high Zh and low Zdr values with turbulent atmospheric conditions observed at the ground level indicated the occurrence of the riming process. In addition, the negative Kdp and Zdr values combined with high Zh and ρhv magnitudes (observed near the end of the snow event) indicated the formation of graupel particles. The polarimetric radar signatures obtained for the snow growth processes were evident from ground observations and agreed well with the results of the Weather Research and Forecasting Model and Modern-Era Retrospective Analysis for Research and Applications data. Furthermore, the spatial variability of Zh methodology was implemented to describe both aggregates and rimed ice particles.

The CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X band with coaxial beams. This radar system offers a unique environment to develop and/or validate algorithms that cut across its wavelengths and polarizations. This paper presents a method to retrieve resonance scattering regions from the difference in intrinsic reflectivities after attenuation correction, which is performed using measured reflectivity fields only. The algorithm to retrieve these regions dominated by non-Rayleigh scattering is applied to different storm events, and the obtained data field capturing the difference in S- and X-band reflectivities due to resonance effects (which we will call Mie signal for convenience) is compared to the collocated dual-polarization fields. The obtained Mie signal is also compared to hail reports. In both cases, the retrieved Mie signal is found to be consistent with the rest of the dual-polarization data fields, and in some situations, it is shown to bring information not directly discernible from the usual dual-polarization radar variables.

Atmospheric rivers (AR) contribute a significant portion of the precipitation for the United States’ west coast. Flooding events caused by ARs can create millions of dollars in damages. Identifying probable ARs and early warning of potential flooding events can provide state and local agencies the time to prepare for such events. The Bay Area Advanced Quantitative Precipitation Information (AQPI) system links users with varied meteorological data ranging from weather radar observations, short term forecasts (Nowcast), 12-hour forecasts, and coastal inundation modeling data. The range of data types presents an opportunity for proactive responses by the users; however, it also presents the challenge of ensuring the correct data is available to the appropriate user. Through Model Based Systems Engineering (MBSE) Behavioral Analysis, the AQPI team analyzes the appropriate requirements for each of the different types of AQPI users. MBSE behavioral analysis leads to the development of a system that services the broad user community’s needs while giving each user group a specific interface tailored for them. This engineering approach also allows for the separation of the processing and weather model execution from the user interface. This separation allows for development and advancements in processing without being tied to the user interfaces.

Removing turbine clutter from weather radar observations has become an essential problem in the community since wind turbine clutter signals (WTC) cannot be filtered using traditional clutter filtering. This paper addresses the problem of mitigation of WTC using knowledge of local precipitation and WTC signals to retain the maximum amount of precipitation and retrieve the filtered radar IQ data. The proposed algorithm uses the Generalized Likelihood Ratio Test (GLRT) to detect the range gates affected by WTC, and signal subspace estimation to mitigate the turbine clutter. The performances of the turbine clutter identification and suppression algorithms are also studied by a common evaluation technique of combining clear air wind turbine data and precipitation data.

Microphysical retrievals in snow storms is a topic of active research. It gives us the opportunity to map the weather events correctly and make future predictions better. The primary objective of the ICE-POP field campaign in South Korea was to capture and understand winter precipitation. The dual-frequency, dual-polarization Doppler weather radar (D3R) radar along with several other weather remote sensing instruments were deployed in the campaign. Since D3R is a dual-frequency radar operating at Ku and Ka frequency bands, the dual frequency ratio (DFR) derived from them can be used to study ice hydrometeors. The DFR varies based on the axis ratio of the ice hydrometeor and this information can be used to link the amount of nonsphericity of the ice hydrometeors. The trend of reflectivity at Ku band versus DFR varies for different hydrometeors based on which we can infer the type of ice particle present. Simulations studies carried out using T-matrix method were used to study the changes in DFR when properties of the ice hydrometeor varied. Observations from D3R and other ground instruments are used to interpret the properties of ice particles based on the theoretical study.

Modern ground-based weather radars are mostly dual-polarized, and they are rich in information content in multiple dimensions, and are ideal candidates for effective artificial intelligence (AI) applications. There are a large number of dual-polarization radars around the world. In addition, space borne weather radars such as Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Global Precipitation Measurement (GPM) mission Dual-frequency Precipitation Radar (DPR) have also produced rich observations that are great to be analyzed using AI. AI has already been used with weather radar information long before it became popular in mainstream, such as the use of neural networks for rainfall estimation through ingesting vertical reflectivity profiles and the application of neuro-fuzzy systems for hydrometeor classification. The primary goal of this paper is to introduce some of the major application areas for weather radar AI, namely, quantitative precipitation estimation (QPE), precipitation classification, and nowcasting. These three application domains have engaged very different types of AI technologies.

A new interpulse frequency diverse technique is introduced for weather radar second-trip suppression and retrieval. Interpulse coding is widely used for second-trip suppression or cross-polarization isolation. Here, a new interpulse scheme is discussed, taking advantage of frequency diverse waveforms. The simulations and performance tests are evaluated, keeping in mind NASA dual-frequency, dual-polarization, Doppler radar (D3R). A new method is discussed to recover velocity and spectral width despite the incoherence in samples due to the change of frequency from pulse to pulse. This technique can recover the weather radar moments over a much higher dynamic range of the second-trip contamination than the popular interpulse phase codes, for second-trip suppression and retrieval under specific phase noise conditions. And it has a bigger recovery region of second-trip velocity if the region is drawn with increasing spectral width (compared to other interpulse codes).

Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude (−1) and Zdr was lower for a given Kdp or Zh, consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(Kdp), R(Ah), R(Kdp, ζdr), R(z, ζdr), and R(Ah, ζdr), which use linear versions of Zdr and Zh, namely ζdr and z. Except for R(Kdp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(Kdp, ζdr), followed by R(Ah, ζdr) and R(z, ζdr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζdr), R(Kdp), and R(Kdp, ζdr) depending on Zdr > 0.25 dB and Kdp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Zdr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

The atmospheric state evolution is an inherently highly complex three-dimensional problem that numerical weather prediction (NWP) models attempt to solve. Although NWP models are being successfully employed for medium- and long-range forecast, their short-duration forecast (or nowcast) capabilities are still limited because of model initialization challenges. On the lower end of the complexity scale, nowcasting by extrapolation of two-dimensional weather radar images has long been the most effective tool for nowcasting precipitation. Attempts are being made to take advantage of both approaches by blending extrapolation and numerical model forecasts. In this work a different approach is presented, relying on the additional Doppler radar wind information and a simplified modeling of basic physical processes. Instead of mixing the outputs of different forecasts as in blended approaches, the idea behind this study is to combine extrapolation and precipitation modeling in a new technique with a higher level of complexity with respect to conventional nowcasting methods, although still much simpler than NWP models. As a preliminary step, the Variational Doppler Radar Analysis System (VDRAS) is used to provide an initial analysis exploiting all the available dual-polarization and Doppler radar observations. The rainwater and wind fields are then advected using an optical flow technique that is subject to simplified physical interactions. As a result precipitation and wind nowcasting are obtained and are successively validated up to a 1-h lead time, showing potential improvement upon standard extrapolation.

Unwanted radar echoes, colloquially referred to as “clutter,” impede the mission effectiveness of radar systems. Depending on radar’s application, clutter examples can include weather, buildings, vegetation, and more. Techniques to mitigate clutter and improve the performance of radar systems are continuously being developed and refined. In this paper, wind turbines used for commercial power generation, which present a Doppler velocity signature that is time-varying, are considered a source of radar clutter. A wind turbine clutter mitigation technique is developed for fixed-pointing weather radar applications, approximating the turbine’s radar signature as a cyclostationary process. The cyclostationary model for the wind turbine and the suppression technique is then validated using observations of wind turbines and precipitation.

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

In this paper, a spectral domain signal processing technique is demonstrated to filter out sea clutter from data of ship-borne weather radar. A procedure is shown for correcting the velocity errors in the spectrographs that is due to ship’s motion during radar scanning. A novel thresholding criterion named as spectral signal quality index (SSQI) is developed to suppress noise and unwanted interference in the spectral domain processing. It is shown that the application of SSQI based thresholding leads to a better discrimination of weather signals against noise and sea clutter. This technique is demonstrated using data from SEAPOL a C-band shipborne dual polarimetric weather radar to effectively remove sea clutter.

Phased array weather radars allow for fast and agile scanning which can improve the temporal resolution of meteorological data and allows for the development of multifunction radars. However, electronic scan angle dependent power and phase introduced by electronic beam steering as well as dual-polarization performance of phased array antennas is a challenge in developing polarimetric phased array weather radars. Front-X is an X-band dual-polarization phased array weather radar capable of both electronic and mechanical beam steering which can be used to validate electronic scan data against mechanical scan data. Additionally, the system can be used to explore and measure the differences between electronic and mechanical scan data. This paper presents a description of Front-X and an automated electronic scan calibration mode implemented in the system which generates corrections that account for power and phase differences between electronic and mechanical scan data. Finally, the system and the electronic scan corrections are demonstrated through a comparison of mechanical scan and electronic scan data, as well as a comparison to the co-located and proven CHILL X-band radar.

The NASA dual-frequency dual-polarization Doppler radar (D3R) serves as the ground validation tool for the Global Precipitation Measurement (GPM) mission. It is important to maintain the quality of the data from the radar. The D3R which operates at high frequency bands, has an unambiguous range of approximately 40 km. This unambiguous range of the radar is more prone to encounter second trip echoes in the received signal. In this work the method to detect and estimate the second trip signal parameters using the random phase codes is described. The D3R’s Ku band data is considered here to show the performance of the random phase codes to detect and estimate the second trip echoes in the received signal.

The increase in renewable energy has led to the expansion of wind farms in various locations and terrain. Accordingly, more wind turbines create more complex clutter targets for weather radars. Over the last few years, researchers have presented various methods to mitigate this problem of wind turbine clutter interfering with atmospheric observations. This paper investigates different methods as proposed by Keranen at el. [1], Y. Li et al. [2] and S.P.Sira et al. [3]. Performances of both fuzzy logic approach [1] and Generalized Likelihood Ratio Test [2] to detect the range gates affected by turbine clutter are discussed. There after algorithms based on spatial averaging and least squares approximation are proposed to mitigate the clutter while effectively maintaining the precipitation signal. The solutions presented are computationally efficient and works with common scanning strategies. An evaluation approach is also presented which combines clear air wind turbine data and precipitation data to provide a quantitative reference for the efficacy of the wind turbine clutter filtering.

This paper describes the deployment and features of CSU-CHIVO radar during the RELAMPAGO campaign in Argentina. Intercomparison with GPM-DPR is done using Volume Matching. Vertical profile analysis of storms is also shown and a list of GPM overpasses and summary of the tallest storms during the campaign are also included. The results show that CHIVO agree well with GPM in terms of reflectivity and microphysical structure of clouds and show the value of CHIVO for ground validation.

Weather radars are experiencing an increasing amount of strong interferences caused by artificial radio sources, such as telecommunications systems, with a negative impact on radar quantitative measurements. In most European countries, operational weather radars operate at the C-band, in the 5.6 GHz frequency band, which is shared with Radio Local Area Networks (RLAN) and Wireless Local Area Networks. The impact of these telecommunications systems in weather radar measurements has been already discussed in literature.

Traditionally, radar rainfall algorithms are derived through nonlinear regression of rain rates and simulated radar observables from raindrop size distribution (DSD). The performance of such empirical relations is highly dependent on the physical model of DSD and the parametric relation between the physical model and radar parameters. Such algorithms also have large uncertainties that need to be adaptively adjusted based on local DSD properties. In this research, we propose an alternative approach to dual-polarization radar rainfall estimation. In particular, a non-parametric machine learning model is designed and trained using simulated radar data based on DSD measurements in different climatological regimes. The trained model is applied to real radar measurements to produce rain rate estimates. Preliminary results show the promising performance of this novel method compared to traditional parametric rainfall relations.

The presence of sea clutter introduces error in weather radar observations over sea. Since the characteristics of sea clutter are different from stationary ground clutter, conventional clutter mitigation techniques produce unsatisfactory results. Hence, study of sea clutter properties and its mitigation is important. Over the last few decades, various research communities have studied and modelled sea clutter using different statistical distributions. This paper discusses identification of sea clutter in a weather radar using fuzzy logic technique. Time series data from an C-band dual-pol weather radar has been used to identify sea clutter in presence of precipitation. Spectral properties and distributions of sea clutter are also studied and a modified GMAP technique has been implemented for the sea clutter suppression.

This paper describes the design and implementation of intra-pulse polyphase codes for a weather radar system. Algorithms to generate codes with good correlation properties are discussed. Thereafter, a new design framework is described, which optimizes the polyphase code and corresponding mismatched filter, using a cost/error function, especially for weather radars. It establishes the performance of these intra-pulse techniques with specific application towards second trip removal. The developed code is implemented on NASA D3R, which is a dual-frequency, dual-polarization, Doppler weather radar system., Comment: to be published in IEEE transactions of geoscience and remote sensing

Vertical profiles of vertical air motion and raindrop size distributions (DSDs) within stratiform rain are estimated using two collocated vertically pointing radars (VPRs) operating at 3 and 35 GHz. Different raindrop backscattering cross sections occur at 3 and 35 GHz with Rayleigh scattering occurring for all raindrops at 3 GHz and Mie scattering occurring for larger raindrops at 35 GHz. This frequency-dependent backscattering cross section causes differently shaped reflectivity-weighted Doppler velocity spectra leading to radar transmit frequency-dependent radar moments of intrinsic reflectivity factor, mean Doppler velocity, and spectrum variance. The retrieval method described herein uses four radar moments as inputs to retrieve four outputs at each height within a precipitation column. The inputs include 3-GHz VPR mean Doppler velocity and unattenuated reflectivity factor and 35-GHz VPR mean Doppler velocity and spectrum variance. The outputs include vertical air motion and three parameters of a gamma-shaped DSD. To account for different VPR sample volumes, radar observations were accumulated over 45 s and over several range gates to represent time-space scales larger than either VPR sample volumes. Observed variability over this common time-space scale is used to estimate retrieval uncertainties. The retrieved air motions and DSD parameters compare well against retrievals from a collocated 449-MHz VPR that estimated air motions from Bragg scattering signals and DSD parameters from Rayleigh scattering signals.

The demand for renewable power production has fostered an exponential increase in the size and number of wind turbines. This expanding source of power generation is sometimes at odds with maintaining the effective operation of radar systems in air traffic control, defense, weather prediction, and severe-storm-tracking applications. With the recent upgrade of the NEXRAD weather radar system to enable dual-polarization observations, a dual-polarization characterization effort of wind turbines is warranted. Focusing on weather radar applications, a characterization of ground clutter, precipitation, and wind turbines is presented here using a consistent unified treatment. This characterization effort directly compares the dual-polarization radar signatures of these three classes of scatterers. The physical characteristics of wind turbines (particularly their cyclostationary behavior) are exploited to identify unique dual-polarization radar signatures.

A raindrop size distribution (DSD) retrieval method for a weather radar network consisting of several X-band dual-polarization radars is proposed. An iterative maximum likelihood (ML) estimator for DSD retrieval in a single radar was developed in the authors’ previous work, and the proposed algorithm in this paper extends the single-radar retrieval to radar-networked retrieval, where ML solutions in each single-radar node are integrated based on a Bayesian scheme in order to reduce estimation errors and to enhance accuracy. Statistical evaluations of the proposed algorithm were carried out using numerical simulations. The results with eight radar nodes showed that the bias and standard errors are −0.05 and 0.09 in log(Nw); and Nw (mm−1 m−3) and 0.04 and 0.09 in D0 (mm) in an environment with fluctuations in dual-polarization radar measurements (normal distributions with standard deviations of 0.8 dBZ, 0.2 dB, and 1.5° in ZHm, ZDRm, and ΦDPm, respectively). Further error analyses indicated that the estimation accuracy depended on the number of radar nodes, the ranges of varying μ, the raindrop axis ratio model, and the system bias errors in dual-polarization radar measurements.

The CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X bands with coaxial beams. One of the capabilities of this radar system is the possibility of developing and/or validating algorithms across dual wavelengths and dual polarizations. This paper presents one such instance, showing how the rainfall field can be estimated either from the S- and X-band reflectivities or from the differential propagation phase at X band. To do so, the paper first presents a dual-wavelength attenuation correction method that uses the reflectivity measured at S band, as the constraint for the correction of the reflectivity measured at X band, and it describes how Mie scattering regions at X band may be detected from the retrieved path-integrated attenuation field. Then, the paper describes how the resulting specific attenuation field relates to rainfall and specific phase at X band, which can be obtained from dual-polarization data at a single wavelength as well, and shows examples. Finally, the paper looks at the relation between attenuation and the differential phase as a function of elevation angle for a few cases, which may be related to the drop size distribution and mean diameter, as well as temperature.

The Cloud-Hosted Real-Time Data Services for the Geosciences project (CHORDS) seeks to enable researchers collecting data in field experiments by providing a lightweight, cloud-based, and easy-to-use way to transfer, store, and visualize data in real-time from multiple time series sensor nodes. In preparation for the upcoming release of version 1.0, a set of specifications has been agreed upon for the ingest, storage, standards, and visualization of real-time image data, such as weather radar scans. This work serves to provide an update on CHORDS development and use cases, specifically focusing on the addition of two-dimensional image data and its applications in real-time visualization of the complex observation space, along with its integration with existing time series sensor functionality.

SPID5194569. Northwestern Italy is no exception to this problem. Considering the evolution of the frequency allocation, defined by the International Telecommunication Union (ITU), it appears that the coexistence of weather radars and telecommunication systems is fundamental for reliable and high-quality weather radar observations.

The stability of weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically calibration using the Sun and calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between dual-polarization power and phase measurements in rain. This technique allows a reference absolute value to be provided for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) for each radar scan, allowing the polarimetric variables to be monitored and hardware failures to promptly be recognized. The Sun calibration allows monitoring the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is applied using observations collected during the standard operational scans but requires long integration times (several days) in order to accumulate a sufficient amount of useful data. Finally, an intercalibration technique is developed and performed to compare colocated measurements collected in rain by two radars in overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July–October 2014. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further data analysis or on-site measurements.

Summary The Dallas–Fort Worth (DFW) urban radar network consists of a combination of high resolution X band radars and a standard National Weather Service (NWS) Next-Generation Radar (NEXRAD) system operating at S band frequency. High spatiotemporal-resolution quantitative precipitation estimation (QPE) is one of the important applications of such a network. This paper presents a real-time QPE system developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center for the DFW urban region using both the high resolution X band radar network and the NWS S band radar observations. The specific dual-polarization radar rainfall algorithms at different frequencies (i.e., S- and X-band) and the fusion methodology combining observations at different temporal resolution are described. Radar and rain gauge observations from four rainfall events in 2013 that are characterized by different meteorological phenomena are used to compare the rainfall estimation products of the CASA DFW QPE system to conventional radar products from the national radar network provided by NWS. This high-resolution QPE system is used for urban flash flood mitigations when coupled with hydrological models.

Accurate noise-power estimation in dual-polarization weather radar is necessary for the correction of all power-based moments in low signal-to-noise ratio environments. A method of real-time noise-power estimation for dual-polarization radar systems using copolar correlation and receiver power is introduced. This real-time noise-power estimation algorithm is implemented in the NASA dual-frequency dual-polarization Doppler radar, and the performance results are presented.

The light rain (less than or equal to a few mm hr-1) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

The Dynamic Radar Tracking of Storms (DARTS) model is a Lagrangian persistence-based nowcasting model that has previously shown utility in nowcasting a variety of weather radar data in severe weather and aviation decision support applications. DARTS is based on the discrete Fourier transform and thus provides an inherent means to perform interpolation. In this context, the model is modified such that interpolation can be accurately and efficiently performed by appropriately windowing the input data and evaluating an interpolating polynomial using the fast Fourier transform. The utility of this interpolation methodology for operational use is demonstrated, and its performance is compared with linear and cubic spline interpolation methods. The use of the original DARTS model to perform advection-based interpolation is also investigated. Rainfall rates derived from data collected by the Weather Service Radar-1988 Doppler S-band radar and the X-band radar at the Dallas-Fort Worth test bed were used for the analyses. The results show that the modified DARTS technique yielded normalized standard error values that were close to those of the forward-backward advection approach using the original DARTS model and ran about 2–4 orders of magnitude faster in terms of computation time. The error structure of the interpolation methods in the context of spatial variability and sampling of atmospheric scales represented by the data is also presented. In this sense, utility of the 1–2-km scales was shown, and the modified DARTS-based approach showed the ability to effectively utilize the value in these scales.

The presence of nonmeteorological radar signals, such as sea clutter, birds, and chaff, is a continuous challenge for meteorological services in different regions. In this paper, we assign membership functions to these signals using spectral decompositions of copolar correlation coefficient, differential reflectivity, and differential phase. Additionally, we apply the dual-polarization spectral decomposition technique to identify and suppress nonmeteorological echoes present in radar observations. The performance of the polarimetric spectral filter is illustrated in observations from the C-band Helsinki University Kumpula radar. The results show that the spectral polarimetric filter may be a suitable solution for the mitigation of these nonmeteorological signals.

Most of the recent hydrometeor classification schemes are based on fuzzy logic. When the input radar observations are noisy, the output classification could also be noisy, since the process is bin based and the information from neighboring radar cells is not considered. This paper employs cluster analysis, in combination with fuzzy logic, to improve the hydrometeor classification from dual-polarization radars using a multistep approach. The first step involves a radar-based optimization of an input temperature profile from auxiliary data. Then a first-guess fuzzy logic processing produces the classification to initiate a cluster analysis with contiguity and penalty constraints. The result of the cluster analysis is eventually processed to identify the regions populated with adjacent bins assigned to the same hydrometeor class. Finally, the set of connected regions is passed to the fuzzy logic algorithm for the final classification, exploiting the statistical sample composed by the distribution of the dual-polarization and temperature observations within the regions. Example applications to radar in different environments and meteorological situations, and using different operating frequency bands—namely, S, C, and X bands—are shown. The results are discussed with specific attention to the robustness of the method and the segregation of the data space. Furthermore, the sensitivity to noise and bias in the input variables is also analyzed.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) [1] was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission [2]. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPM's dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. The D3R radar is shown in Figure 1. To support the GPM GV mission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

This paper introduces an algorithm of radar quantitative precipitation estimation (RQPE) derived from a S-band dual-polarization weather radar network over southern China. The observations obtained from Zhuhai radar was used to demonstrate the performance of the RQPE algorithm. This initial regional study on RPQE in southern China provides reference on RPQE algorithm over China in near future when all weather radars will be upgraded with dual-polarization capability throughout China.

Advances in sensing, hardware, and wireless communications are allowing for an ever-increasing array of previously unmeasured weather phenomena at large scales. Integrating these new data with existing technologies and paradigms is of tantamount importance, in order to be able to respond quickly to emerging weather phenomena such as flash floods, tornadoes, and hail storms in real-time. Currently, integrating the massive amount of available data from various types of sensors, including rain gauges, hail sensors, radar, UAV, etc, is challenging and at times in possible, to analyze rapidly developing systems in real-time. This paper addresses this problem and presents a concept for integrating real-time weather radar data with ground sensors using CHORDS portals, allowing for the integration of large volumes of data in real-time using cloud computing technologies.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is a weather radar operating at 13.91 GHz (Ku-band) and 35.56 GHz (Ka-band). The operational range of the D3R is 40 km, this relatively short operating range, along with the high sensitivity of D3R, are susceptible to increased observation of range ambiguous echoes, also referred to as “second-trip echoes”. In this work, by leveraging a staggered pulse repetition period and random transmitted phase codes, two methods are developed to detect and dealias the second trip contamination for the operational use with the D3R. Using these methods, the overlapping echoes are separated, and unambiguous range of the D3R is increased. Weather signals are simulated and used to quantitatively characterize the performance of moment estimator over a wide range of realistic weather scenarios. The D3R's Ku-band observations are presented to demonstrate the performance of algorithm with and without overlapping echoes from different ranges.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is an important ground validation tool for the global precipitation measurement (GPM) missions dual-frequency precipitation radar (DPR). In this work, we start with the hardware modifications done to accomplish this upgrade, although the focus is not much on the hardware modifications. Overall system architecture is presented. The focus is more on the system sensitivity and spatial resolution. Also, the performance of the pulse compression waveforms is presented and compared with simulated results. Various trade-offs are discussed in context of range resolution, sensitivity and side lobe performance of these waveforms.

An important goal of combining weather radar with rain gauge data is to provide reliable estimates of rainfall rate and accumulation and to further identify intense precipitation and issue flood warnings. Scanning radars provide the ability to observe precipitation over wider areas within shorter timeframes compared to rain gauges, leading to improved situational awareness and more accurate and reliable warnings of future precipitation and flooding events. The focus of this study is on evaluating the performance of the multi-radar multi-sensor (MRMS) system with and without the impact of a local gap filling radar. The challenge of using radar and rain gauges to provide accurate rainfall estimates in complex terrain is investigated. The area of interest is the Russian River basin north of San Francisco, CA, which lies within the National Oceanic and Atmospheric Administration (NOAA) Hydrometeorology Testbed (HMT). In this complex mountainous terrain, the challenge of obtaining reliable quantitative...

Quantitative precipitation estimation (QPE) for flash flood forecasting is one of the most commonly used applications of modern meteorological radars. The low-power low-cost small X-band dual-polarization radar system is gaining more interest for urban weather monitoring. This case study presents the installation and application of the first routinely operating dual-polarization X-band radar deployed by the Korea Institute of Civil Engineering and Building Technology (KICT) in South Korea. An overview of this KICT radar’s observations and attenuation correction is presented. The self-consistency criterion is used to examine the dual-polarization data quality. R(Kdp)-based rainfall relations are developed to perform rainfall estimation. The Kdp field is robustly obtained using an adaptive estimation methodology. Cross comparisons between rainfall products from the KICT X-band dual-polarization radar, ground rain gauges, and traditional Korea Meteorological Administration (KMA) S-band radar are cond...

Rainfall estimation based on radar measurements has been addressed by using parametric algorithms such as Z-R relation. However, such empirical relations may not be sufficient to capture the space-time variability of precipitation. In this paper, we introduce a DMLP-based machine learning model for rainfall estimation by using multiple layers to capture the complex abstractions of radar reflectivity at different attitude levels. The radar data collected by the Weather Surveillance Radar - 1988 Doppler (WSR-88DP) in Melbourne, Florida (i.e., KMLB radar) are used for demonstration purposes, while the rain gauge data are used for training purposes. The rainfall product derived from the DMLP model is compared against an independent rain gauge dataset, which shows excellent performance of the new machine learning based rainfall model.

A novel analytical method is presented for evaluating the electrical performance of a radome for a dual-polarized phased-array antenna under rain conditions. Attenuation, reflections, and induced cross polarization are evaluated for different rainfall conditions and radome types. The authors present a model for estimating the drop size distribution on a radome surface based on skin surface material, area, inclination, and rainfall rate. Then, a multilayer radome model based on the transmission-line-equivalent circuit model is used to characterize the radome’s scattering parameters. Numerical results are compared with radar data obtained in the Next Generation Weather Radar (NEXRAD) and Collaborative Adaptive Sensing of the Atmosphere (CASA) systems, and good agreement is found.

In operational weather radar, precipitation echoes are often weak when compared to the underlying noise. Coherence properties of dual polarization can be used for enhancing the detection and for the improved estimation of weak echoes of precipitation. The enhanced detectability results from utilizing coherent averages of precipitation signals, while the uncorrelated noise vanishes asymptotically, explicit in the off-diagonal element Rhv of the echo covariance matrix. In finite sums, the noise terms as well as the uncertainties associated with them are suppressed. A signal can be detected in weaker echo by an analytically derived censoring policy. The coherent sums are readily available as the cross-correlation function of the antenna voltages H and V, which estimates Rhv in the mode of simultaneous transmission and reception. The magnitude of Rhv is a consistent estimate of the copolar echo power, leading to the copolar radar reflectivity of precipitation, which refers to the geometric mean of the reflectivities in H and V polarizations. Because of the intrinsic noise suppression, estimates of the copolar reflectivity are, in relative terms, more precise and more accurate than the corresponding estimates of reflectivity in specific channels, for weak signals of precipitation. These aspects are discussed quantitatively with validation of the key features in real conditions. The advances suggest for dedicated dual-polarization surveillance scans of weak echo of precipitation.

The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm’s validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.