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Rain gauge networks provide direct precipitation measurements and have been widely used in hydrology, synoptic-scale meteorology, and climatology. However, rain gauge observations are subject to a variety of error sources, and quality control (QC) is required to ensure the reasonable use. In order to enhance the automatic detection ability of anomalies in data, the novel multi-source data quality control (NMQC) method is proposed for hourly rain gauge data. It employs a phased strategy to reduce the misjudgment risk caused by the uncertainty from radar and satellite remote-sensing measurements. NMQC is applied for the QC of hourly gauge data from more than 24,000 hydro-meteorological stations in the Yangtze River basin in 2020. The results show that its detection ratio of anomalous data is 1.73‰, only 1.73% of which are suspicious data needing to be confirmed by experts. Moreover, the distribution characteristics of anomaly data are consistent with the climatic characteristics of the study region as well as measurement and maintenance modes of rain gauges. Overall, NMQC has a strong ability to label anomaly data automatically, while identifying a lower proportion of suspicious data. It can greatly reduce manual intervention and shorten the impact time of anomaly data in the operational work. HIGHLIGHTS Quantitative and fusion application of radar and satellite data were addressed for the quality control (QC) of hourly rain gauge data.; Precipitation quality anomaly events were defined to improve the traceability of QC.; A phased QC strategy was adopted to reduce the misjudgment risk caused by the uncertainty of remote-sensing measurements.;

This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today. [ABSTRACT FROM AUTHOR]

The applicability of high-frequency (HF) radar systems for wave measurement in an estuary was explored by extracting the significant wave height (${H_{sr}}$ H sr ) using a traditional Barrick equation from the Doppler spectra observed by three radar systems installed in Ise Bay, Japan. The minimum value of ${H_{sr}}$ H sr estimated around each grid point was relatively consistent with the wave height observed with a wave gauge/buoy, except for a deterioration of wave measurement accuracy caused by a decrease in seawater conductivity from the freshwater inflow after flooding. Furthermore, the relationship between the accuracy and the signal-to-noise ratios for the first- and second-order peaks (SNR1 and SNR2, respectively) highlighted the difficulty in determining the threshold values of SNRs in the bay. Thus, we suggest the use of ${H_{sr}}$ H sr as a criterion for quality control when applying a nonlinear inversion method for estimating ocean wave spectra based on the Bayesian possibility theorem (BIM). Our suggestion is to select the appropriate Doppler spectra and increase the acquisition rates of wave data with low relative error compared to BIMs using SNR1 and SNR2. These results can promote the applicability of the nonlinear inversion in estuary regions. [ABSTRACT FROM AUTHOR]

Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. This is certainly the case for the pan-European radar precipitation products. The density of (near) real-time rain gauge accumulations from official networks is often relatively low. Crowdsourced rain gauge networks have a much higher density than conventional ones and are a potentially interesting (complementary) source to merge with radar precipitation accumulations. Here, a 1-year personal weather station (PWS) rain gauge dataset of ∼ 5 min accumulations is obtained from the private company Netatmo over the period 1 September 2019–31 August 2020, which is subjected to quality control using neighbouring PWSs and, after aggregating to 1 h accumulations, using unadjusted radar data. The PWS 1 h gauge accumulations are employed to spatially adjust OPERA radar accumulations, covering 78 % of geographical Europe. The performance of the merged dataset is evaluated against daily and disaggregated 1 h gauge accumulations from weather stations in the European Climate Assessment & Dataset (ECA&D). Results are contrasted to those from an unadjusted OPERA-based radar dataset and from EURADCLIM. The severe average underestimation for daily precipitation of ∼ 28 % from the unadjusted radar dataset diminishes to ∼ 3 % for the merged radar–PWS dataset. A station-based spatial verification shows that the relative bias in 1 h precipitation is still quite variable and suggests stronger underestimations for colder climates. A dedicated evaluation with scatter density plots reveals that the performance is indeed less good for lower temperatures, which points to limitations in observing solid precipitation by PWS gauges. The outcome of this study confirms the potential of crowdsourcing to improve radar precipitation products in (near) real time. [ABSTRACT FROM AUTHOR]

Chinese lunar landing mission Chang'E-4 reached the far side of the Moon in January 2019 and has been providing unprecedented Lunar Penetrating Radar data able to explore the lunar subsurface down to more than 40 m (with its more resolutive high frequency band). Data are periodically released to the scientific community in raw PDS4 format. Here we provide different versions of the radar dataset after editing (i.e. pre-processing), partial, and full processing in order to provide a complete ready-to-use dataset to end-users (data collected since 4th January 2019 until 27th March 2023) which can be directly exploited for analysis, interpretation, inversion, as well as integration with imagery or other information. In particular, we implemented an efficient and objective way to remove duplicated traces representing more than 90% of original data, as well as a processing flow able to retain all the original data information, while avoiding redundancies. The provided datasets can be implemented with future data releases and straightforwardly exploited for any future analysis. [ABSTRACT FROM AUTHOR]

Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s -1 for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s -1 for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s -1 for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations. [ABSTRACT FROM AUTHOR]

High-frequency radars (HFRs) are important for remote sensing of the marine environment due to their ability to provide real-time, wide-coverage, and high-resolution measurements of the ocean surface current, wave height, and wind speed. However, due to the intricate multidimensional processing demands (e.g., time, Doppler, and space) for internal data and effective suppression of external noise, conducting quality control (QC) on radar-measured data is of great importance. In this paper, we first present a comprehensive quality evaluation model for both radial current and synthesized vector current obtained by direction-finding (DF) HFRs. In the proposed model, the quality factor (QF) is calculated for each current cell to evaluate its reliability. The QF for the radial current depends on the signal-to-noise ratio (SNR) and DF factor of the first-order Bragg peak region in the range–Doppler (RD) spectrum, and the QF for the synthesized vector current can be calculated using an error propagation model based on geometric dilution of precision (GDOP). A QC method is then proposed for processing HFR-derived surface current data via the following steps: (1) signal preprocessing is performed to minimize the effect of unwanted external signals such as radio frequency interference and ionospheric clutter; (2) radial currents with low QFs and outliers are removed; (3) the vector currents with low QFs are also removed before spatial smoothing and interpolation. The proposed QC method is validated using a one-month-long dataset collected by the Ocean State Monitoring and Analyzing Radar, model S (OSMAR-S). The improvement in the current quality is proven to be significant. Using the buoy data as ground truth, after applying QC, the correlation coefficients (CCs) of the radial current, synthesized current speed, and synthesized current direction are increased by 4.33~102.91%, 1.04~90.74%, and 1.20~62.67%, respectively, and the root mean square errors (RMSEs) are decreased by 2.51~49.65%, 7.86~27.22%, and 1.68~28.99%, respectively. The proposed QC method has now been incorporated into the operational software (RemoteSiteConsole v1.0.0.65) of OSMAR-S. [ABSTRACT FROM AUTHOR]

We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of ice microphysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer, and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter, and range folding. We finally present an analysis of the temperature dependence of Doppler, dual-wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in and is freely available at: 10.5281/zenodo.7803064. [ABSTRACT FROM AUTHOR]

Accurate measurements of ocean waves underpin efficient offshore operations and optimal offshore structure design, helping to ensure the offshore industry can operate both safely and economically. Popular instruments used by the offshore industry are the Rosemount WaveRadar (Radar) and the Waverider Buoy. The Optech Laser has been used at some locations for specific studies. Recent reports indicate systematic differences of order 10% among the wave measurements made by these instruments. This paper examines the relative performance of these instruments based upon various time-domain comparisons, including results from a quality control (QC) procedure, capabilities of measuring the wave surface profile (skewness), and crest heights for varying wind sea and swell conditions. The QC check provides good-quality data that can be further investigated with an assurance of error-free data, suggesting that the Waverider produces the bestquality data with the lowest failure rate compared to the Laser and Radar. A significant number of theWaverider surface elevation records have negative skewness, particularly at higher sea states, affecting its crest height measurements, which are lower than those from the Laser and Radar. Additionally, the significant wave height (H1/3) estimates of the Radar are lower than the Laser and Waverider, but its zero-crossing wave periods (TZ), on average, are longer than the Laser and theWaverider. The significant heights (H1/3) of Laser and Waverider are in good agreement for all three datasets, but the Waverider's zero-crossing wave period (TZ) estimates are significantly longer than the Laser. [ABSTRACT FROM AUTHOR]

Observations of vertical wind in Chongqing, a typical mountainous city in China, are important, but sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products with WPR data was then performed. The conclusions can be summarized as follows: (1) A clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 69.92%. Their root-mean-square deviation increased with height but decreased by 3 - 4 km. (2) After quality control of Gaussian filtering (GF) and empirical orthogonal function construction (EOFc, G = 87.23%) of the WPR data, the R between the WPR and RS reached 76.00% and 95.44%, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations, but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations, but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0% and 67.8% deviations were observed between ± 5 m/s for Rayleigh-clear and Mie-cloudy winds vs. WPR winds, respectively. Vertically, the mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing, such that the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences of 4-8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere, influenced by the topography of the Tibetan Plateau. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9% and 59.6% were concentrated between ±5m/s for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1% and 70.2% of deviations appeared between ± 5 m/s for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations. [ABSTRACT FROM AUTHOR]

This paper explores the possibility of using multi-source precipitation 13 estimates for climatological applications. A data processing algorithm (RainGRS Clim) has been developed to work on precipitation accumulations such as daily or monthly totals, which are significantly longer than operational accumulations (generally between 5 min and 1 h). The algorithm makes the most of additional opportunities, such as the possibility to complement with delayed data, access to high-quality data that are not operationally available, and the greater efficiency of the algorithms for data quality control and merging on longer accumulations. Verification of the developed algorithms was carried out on monthly accumulations through comparison with precipitation from manual rain gauges. As a result, monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally. This improvement is particularly noticeable for the winter months, when precipitation estimation is much more difficult due to less reliable radar estimates. [ABSTRACT FROM AUTHOR]

The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission is a joint effort by the European Space Agency (ESA) and the Japanese Aerospace Exploration Agency (JAXA). The EarthCARE mission features the first spaceborne 94 GHz cloud-profiling radar (CPR) with Doppler capability. The raw CPR observations and auxiliary information are used as input to three Level-2 (L2) algorithms: (1) C-APC: Antenna Pointing Characterization; (2) C-FMR: CPR feature mask and reflectivity; (3) C-CD: Corrected CPR Doppler Measurements. These algorithms apply quality control and corrections to the CPR primary measurements and derive important geophysical variables, such as hydrometeor locations, and best estimates of particle sedimentation fall velocities. The C-APC algorithm uses natural targets to introduce any corrections needed to the CPR raw Doppler velocities due to the CPR antenna pointing. The C-FMR product provides the feature mask based on only-reflectivity CPR measurements and quality-controlled radar-reflectivity profiles corrected for gaseous attenuation at 94 GHz. In addition, C-FMR provides best estimates of the path-integrated attenuation (PIA) and flags identifying the presence of multiple scattering in the CPR observations. Finally, the C-CD product provides the quality-controlled, bias-corrected mean Doppler velocity estimates (Doppler measurements corrected for antenna mispointing, non-uniform beam filling and velocity folding). In addition, the best estimate of the particle sedimentation velocity is estimated using a novel technique. [ABSTRACT FROM AUTHOR]

Information about precipitation extremes is of vital importance for many hydrological planning and design purposes. However, due to various sources of error, some of the observed extremes may be inaccurate or false. The purpose of this investigation is to present quality control of observed extremes using space–time statistical methods. To cope with the highly skewed rainfall distribution, a Box–Cox transformation with a suitable parameter was used. The value at the location of a potential outlier is estimated using the surrounding stations and the calculated spatial variogram and compared to the suspicious observation. If the difference exceeds the threshold of the test, the value is flagged as a possible outlier. The same procedure is repeated for different temporal aggregations in order to avoid singularities caused by convection. Detected outliers are subsequently compared to the corresponding radar and discharge observations, and finally, implausible extremes are removed. The procedure is demonstrated using observations of sub-daily and daily temporal resolution in Germany. [ABSTRACT FROM AUTHOR]

We develop, utilize, and validate techniques to produce a global data set of accurate coastal ocean surface vector winds. The dataset extends as near to the coast as 5 km and includes 10 years of SeaWinds on QuikSCAT ocean scatterometer data obtained from 1999 to 2009. We demonstrate improved retrievals over other large land-locked bodies of water as well, such as the Caspian Sea and the Great lakes. To determine the coastal winds we quantify the extent of land contamination in each scatterometer backscatter measurement and to the extent possible remove that contamination. After the measurements are thus corrected we retrieve winds with the corrected measurements using a previously published algorithm which has been extensively used for JPL scatterometer wind products. The coastal processing vastly increases the number of wind vector cells near coasts. We have ten times the number of wind vectors within 10 km of coast as without coastal processing, and over twice as many at 20 km from coast. These new wind vectors are high-quality, and have zero effect on non-coastal wind vectors. The effect of residual land contamination is quantified by comparing to buoys at varying distance from the coast and comparing coastal wind vector cells to oceanward neighbors. We show that the non-coastal QuikSCAT processing has very few good wind vectors nearer to the coast than about 22.5 km. In comparison to buoys, and oceanward neighbors, we find a small increase in speed errors of these new coastal wind vectors versus the performance of non-coastal QuikSCAT at 22.5 km, indicating the high-quality of these new coastal wind vectors. A quality control scheme is employed that flags regions where the coastal wind retrieval is poor due to the assumptions inherent in the technique being locally invalid. The coastal winds retrieved in this manner have been publicly distributed to the oceanography community and utilized in other published works. [ABSTRACT FROM AUTHOR]

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

In this work, wave measurements from a Radac wave radar installed on the TetraSpar Demonstrator floating wind turbine are analysed and compared to measurements from an adjacent Fugro Midi wave buoy and MET Norway model forecasts. The wave radar is found to agree well with the wave buoy and model forecasts in most cases, giving confidence to use the data for engineering calculations and design. Some discrepancy in the measured spreading is observed, but analysis of raw wave data indicates that the wave radar measures similar spreading values to the pitch, roll and heave wave buoy. Wave diffraction by the local structure is found to have a small impact on the high-frequency tail of the measured spectra, but has minimal impact on the measured bulk wave parameters. Measurements of spurious large wave crest events appear to be associated with sea-spray from the centre column for a particular wave direction. Quality control procedures implemented in the wave radar software remove these spurious large crest events. • Wave radar measurements agree well with Midi buoy measurements and model forecasts. • Wave radar directional spreading measurements are similar to those from a Midi buoy. • The effect of diffraction by the floater structure is small but measurable. • Spurious crest measurements are observed and are likely associated with sea spray. • The wave radar should be positioned carefully to avoid the effect of sea spray. [ABSTRACT FROM AUTHOR]

Ideally echoes in radar reflectivity data correspond to precipitating particles, however they do not, and as a result, automated weather radar products that use these data are drastically affected when conditions are not ideal. Weather radar data of the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) is one such case that often suffer contamination, in particular by electromagnetic interference and the identification and mitigation of interference echoes is an ongoing problem in radar meteorology in these regions. In order to improve the quality of the data and consequently the automated products, especially for the radar quantitative precipitation estimation (QPE), a fuzzy logic algorithm is applied upon the radar reflectivity data to provide a probability guidance for segregating interference-contaminated echoes from precipitating echoes. Specifically, adequate features to highlight interference characteristics are required for the algorithm to be effective based on prior experiences. This approach is presented in this study to derive membership functions and their relatively objective weights are determined based on the superior result of sensitivity test from interference cases. The result of which produced a value that quantifies the possibility of each bin being affected by interference. Cases that highlighted the interference were examined and demonstrated the ability of the fuzzy logic approach to remove interference echoes from radar reflectivity map. Moreover, the presented method can be feasibly implemented in real-time multi-radar operations as a quality control (QC) aid. [ABSTRACT FROM AUTHOR]

The Central Weather Bureau in Taiwan successfully implemented a version of WRF coined as TWRF (Typhoon WRF) as the operational typhoon prediction system. The TWRF has two nested domains with 15/3 km resolution covering large areas over the western North Pacific, which has led to significant improvements of typhoon predictions over the previous version with coarser resolutions. Built upon this success, the WRF Four-Dimensional Data Assimilation (FDDA) system has been implemented in TWRF. This study investigates the impact of assimilating dual-Doppler radar retrieval winds from eight sets of dual-Doppler radars using FDDA on the prediction of Typhoon Nesat (2017) that passed over Taiwan. The wind field retrieved with dual-Doppler radars has a vertical extent from 1 to 10 km and the horizontal resolution is 1 km. After quality control and data thinning, the radar retrieval winds are assimilated using FDDA for two update cycles in addition to the existing hybrid 3DEnVar for all other observations in TWRF. Furthermore, we assimilate the additional radar data that become available during the 4-hour waiting period for the completion of the global model prediction to be used as the lateral boundary condition for TWRF. For Typhoon Nesat, the wind structure and rainfall forecasts are improved with the assimilation of radar-retrieval winds. The overall improvements demonstrated by this case study suggest potentially high impacts for improving the prediction of typhoon-related rainfall with assimilation of dual-Doppler radar data. [ABSTRACT FROM AUTHOR]

Airborne observation is an important approach to collect data in the remote, hostile Antarctica and study the relationship between the Antarctica and global climate. During airborne observations, it is necessary to conduct data processing and quality control on site, which can help to timely evaluate the status of airborne instruments, provide scientific clues, and develop ideal schemes for following airborne observations. As one critical component of airborne instruments, airborne ice sounding radar can delineate sub-ice bedrock topography and internal layers, which cannot be realized by other instruments. In this study, we present an on-site data processing algorithm for high-resolution and high signal-to-noise ratio (SNR) ice sounding radar data acquired by the "Snow Eagle 601", the first fixed-wing airplane deployed by China for the Antarctic expeditions. In addition, the algorithm is further optimized in terms of static pre-allocated memory and parallel and block processing of data to enhance processing speed and meet the requirements for quality control and analysis of on-site data. Finally, we test the optimized algorithm with different volume of ice sounding radar data through implementing on different computer configurations, including i7, i5 CPU and 8G, 16G memory with the same disk. The results show that the average processing speed of the optimized algorithm is 5.143 times faster than the non-optimized algorithm on different computer configurations. [ABSTRACT FROM AUTHOR]

In this work, we present a new quantitative precipitation estimation (QPE) quality-control (QC) algorithm for the U.K. weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground-clutter mitigation [using Clutter Environment Analysis using Adaptive Processing (CLEAN-AP)] and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated "truth" with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data. [ABSTRACT FROM AUTHOR]

A new method has been developed to directly assimilate volume scans of radar reflectivity data with 4D‐Var in the Met Office Unified Model. The method has been demonstrated in the convective‐scale hourly‐cycling UKV forecast model. Reflectivity observations from 18 C‐band radars in the British Isles are assimilated. This article describes the method of observation processing and quality control, the observation operator, and assimilation method. The assimilation method uses a minimum threshold rainwater content in the forward operator to give sensitivity to reflectivity observations where there is no rain in the background. Furthermore, the use of the Huber norm in the observation penalty function allows the use of observations with large innovations in the assimilation. A change was made to the precipitation efficiency in the microphysics scheme of the linear perturbation forecast model to ensure stability of the scheme. A case‐study is presented which demonstrates how the inclusion of reflectivity observations enhances convergence through analysis increments to the wind field, leading to improvements to the location of convective precipitation features in the forecast. Two‐month trials for summer and winter seasons demonstrate significant improvements to rain forecasts in the nowcasting range. [ABSTRACT FROM AUTHOR]

Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP). [ABSTRACT FROM AUTHOR]

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at reading, processing and visualizing polar data from individual weather radars as well as composite Cartesian products both off-line and in real time. The processing flow is controlled by three simple configuration files. This allows the construction of reproducible data processing chains. In the off-line mode, data from multiple radars can be ingested. It is written in the Python programming language. Most of the signal processing and part of the data visualization is performed by a MeteoSwiss-developed version of the Py-ART radar toolkit, which contains enhanced features. Thanks to the broad types of input files accepted and its flexibility it can be easily adapted and used by any member of the weather radar community. The source code is available on GitHub. Compiled versions are also available on PyPI and conda-forge. They are distributed under a BSD license.

The direct assimilation of space‐borne cloud radar and lidar observations into a global numerical weather prediction model has not previously been attempted for several reasons. Firstly, the modification of a data assimilation system to handle space‐borne profiling observations is a technical challenge. Secondly, the relationship between model‐scale control variables and the relatively narrow footprint of the radar and lidar instruments were thought to be unrepresentative and too nonlinear for a variational assimilation system that is based on assumptions of linearity. However, motivation was provided when previous experiments assimilating cloud radar and lidar profiles showed, using an intermediary step to generate pseudo‐observations of temperature and humidity, that there was potential for the observations to improve both the analysis and the subsequent forecast. This article presents the developments made to facilitate the direct assimilation of cloud radar and lidar observations into the four‐dimensional variational assimilation (4D‐Var) system of the European Centre for Medium‐Range Weather Forecasts (ECMWF). A review of the observation operators shows how they have been optimised for data assimilation with an emphasis on efficiency, consistency and differentiability. A key part of this work is the specification of a new fully flow‐dependent characterisation of the observation error. By taking an error inventory approach, we account for different sources of error in a physical way. Also, to avoid degrading the analysis, careful screening, quality control and bias correction is necessary and is described herein. Finally, an initial assessment of the impact of the observations is achieved through single‐analysis tests that show that the analysis fit to the new observations is improved. In‐depth results demonstrating the impact of these observations are shown in the second part of this two‐part series. [ABSTRACT FROM AUTHOR]

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014. [ABSTRACT FROM AUTHOR]

The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient a. In this study, a variational approach is proposed to optimize the coefficient a for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized a consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged a values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize a for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized a for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweepaveraged α. [ABSTRACT FROM AUTHOR]

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at reading, processing and visualizing polar data from individual weather radars as well as composite Cartesian products both off-line and in real time. The processing flow is controlled by three simple configuration files. This allows the construction of reproducible data processing chains. In the off-line mode, data from multiple radars can be ingested. It is written in the Python programming language. Most of the signal processing and part of the data visualization is performed by a MeteoSwiss-developed version of the Py-ART radar toolkit, which contains enhanced features. Thanks to the broad types of input files accepted and its flexibility it can be easily adapted and used by any member of the weather radar community. The source code is available on GitHub. Compiled versions are also available on PyPI and conda-forge. They are distributed under a BSD license. [ABSTRACT FROM AUTHOR]

A radar‐based hail climatology, with broad coverage and high resolution, is possible using the Next‐Generation Weather Radar (NEXRAD) Reanalysis through application of the multiradar multisensor (MRMS) algorithm and maximum expected size of hail (MESH). Using 12 years of MESH data, we define a "severe hail outbreak day" and analyse the characteristics and frequency of severe hail and severe hail outbreaks, including an analysis of hail swaths. Thresholds are set to signify severe hail in terms of MESH, and automated quality control measures are implemented. When comparing severe hail days in MESH to reports, we find a linear relationship between MESH and reports. Several case studies are also included to highlight the utility of MESH when studying outbreaks of severe hail, specifically regarding outbreak events that occur in low‐population areas. With the caveat that this is a relatively short‐time period, we find that severe hail days decrease while severe hail outbreak days increase over the period 2000–2011. The increase in outbreaks is happening primarily in the month of June, where the number of severe hail days stays fairly constant over the 12 years. This suggests that the increase in outbreaks is mainly taking place on days when severe hail already occurs. When examining hail swath characteristics, we find that there are a greater number of hail swaths (with a major‐axis‐length [MAL] of at least 15 km) on outbreak versus nonoutbreak days. Additionally, hail swaths with the largest MALs occur on outbreak days. A severe hail and severe hail outbreak day are defined in terms of the hail proxy Maximum Expected Size of Hail (MESH). This figure shows all severe hail MESH occurrences for 2005. We analyse characteristics and trends in severe hail, outbreaks, and hail swaths. We find a linear relationship between MESH and reports. Case studies are included to highlight the utility of MESH when studying outbreaks. We find that severe hail days decrease, while outbreak days increase from 2000–2011. [ABSTRACT FROM AUTHOR]

In this paper, the authors made an attempt to detect the fibre content and fibre spacing in a steel fibre reinforced concrete (SFRC) industrial floor. Two non-destructive testing (NDT) methods, an electromagnetic induction technique and a radar-based technique, were applied. The first method allowed us to detect the spacing in subsequent layers located in the thickness of the slab. The result of the second method was a 3D visualization of the detected fibre in the volume of concrete slab. The conducted tests showed aptitude and limitations of the applied methods in estimating fibre volume and spacing. The two techniques also allowed us to locate the areas with relatively low fibre concentration, which are very likely to be characterized by low mechanical properties.

BALTRAD software exchanges weather-radar data internationally, operationally, and in real-time, and it processes the data using a common toolbox of algorithms available to every node in the decentralized radar network. This approach enables each node to access and process its own and international data to meet its local needs. The software system is developed collaboratively by the BALTRAD partnership, mostly comprising the national Meteorological and Hydrological institutes in the European Union’s Baltic Sea Region. The most important sub-systems are for data exchange, data management, scheduling and event handling, and data processing. C, Java, and Python languages are used depending on the sub-system, and sub-systems communicate using well-defined interfaces. Software is available from a dedicated Git server. BALTRAD software has been deployed throughout Europe and more recently in Canada. Funding statement: From 2009–2014, the BALTRAD and BALTRAD+ projects were part-financed by the European Union (European Regional Development Fund and European Neighbourhood and Partnership Instrument), with project numbers #009 and #101, respectively.

Using China New Generation Weather Radar (CINRAD) level-II data, the original algorithms for removing isolated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveillance Radar-1988 Doppler (WSR-88D) in the USA and Severe Weather Automatic Nowcast (SWAN) system in China, are modified and improved. To remove isolated non-meteorological echoes, the new algorithm introduces a constraint parameter (Po) to distinguish whether a window of 5 × 5 points is isolated as external echoes. A statistical analysis of 150 radar scans (5 cases, with each case comprising 30 scans) under three different echo types (small-scale convection, typhoon, and large-scale synoptic system) shows that the constraint parameter Po ⩽ 0.167 is suitable for removing isolated non-meteorological echoes while preserving the edge of meteorological echoes. A new parameter, NDZ, which promotes the ability of the algorithm to identify the ground clutters appearing at two adjacent elevation angles, is constructed based on the vertical continuity of reflectivity. These improved algorithms are tested for four cases (three cases of isolated non-meteorological echoes and one case of ground clutters). Based on the statistics of 232 volume scans of radar data (on a temporal resolution of 1 h) measured at Nanchang station from 0000 UTC 5 to 1600 UTC 14 March 2015, it is found that the improved algorithms not only eliminate most (over 95% under clear-sky conditions) of the isolated non-meteorological echoes and ground clutters (including those appearing at two adjacent elevation angles), but also well preserve the structure of meteorological echoes (storms). Key words: radar, isolated non-meteorological echoes, ground clutter, quality control [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide an overview of the present status and procedures related to surface precipitation observations at Environment and Climate Change Canada (ECCC). This work was done to support the ongoing renewal of observation systems and networks at the Meteorological Service of Canada. The paper focusses on selected parameters, namely, accumulated precipitation, precipitation intensity, precipitation type, rainfall, snowfall, and radar reflectivity. Application-specific user needs and requirements are defined and captured by World Meteorological Organization (WMO) Expert Teams at the international level by Observing Systems Capability Analysis and Review (OSCAR) and WMO Integrated Global Observing System (WIGOS), and by ECCC user engagement initiatives within the Canadian context. The precipitation-related networks of ECCC are separated into those containing automatic instruments, those with human (manual) observers, and the radar network. The unique characteristics and data flow for each of these networks, the instrument and installation characteristics, processing steps, and limitations from observation to data distribution and storage are provided. A summary of precipitation instrument-dependent algorithms that are used in ECCC's Data Management System is provided. One outcome of the analysis is the identification of gaps in spatial coverage and data quality that are required to meet user needs. Increased availability of data, including from long-serving manual sites, and an increase in the availability of precipitation type and snowfall amount are identified as improvements that would benefit many users. Other recognized improvements for in situ networks include standardized network procedures, instrument performance adjustments, and improved and sustained access to data and metadata from internal and external networks. Specific to radar, a number of items are recognized that can improve quantitative precipitation estimates. Increased coverage for the radar network and improved methods for assessing and portraying radar data quality would benefit precipitation users. [ABSTRACT FROM AUTHOR]

Surface velocity data from two WERA high-frequency (HF) ocean radar systems, deployed as part of the Australian Integrated Marine Observing System (IMOS), are compared with near-surface currents obtained from drifters and acoustic Doppler current profilers (ADCPs). We evaluate data from two contrasting locations in the first detailed evaluation of the IMOS HF radar surface velocities. HF radar measurements are generally robust but demand quality-control procedures to eliminate obvious errors and outliers that appear temporarily or systematically in the data. A number of different quality control procedures and filters are applied and assessed including Taylor diagrams and Hampel and Savitzky–Golay filters. In addition, the need for and effect of averaging are discussed. The radar measurements of surface current agreed better with the near-surface drifter currents than with the subsurface ADCP currents. Nonetheless, the ADCP comparisons are consistent with those previously reported in other regions. The value of the Taylor diagram for comparing different surface current data sets and processing approaches is demonstrated. Noise levels in the radar current spectra are used to estimate the error in the measurements and in some cases, these errors were found to approach the precision of the radar estimates. Our results give guidance on the most useful temporal sampling resolution. In particular we show that at these sites and these operating frequencies, using 10-min sampling without further averaging does not provide additional information because the higher frequencies are dominated by noise. Averaging the radials over 30 min may be sufficient for many applications. [ABSTRACT FROM AUTHOR]

Min, Y.; Jeong, J.-Y.; Min, I.-K.; Kim, Y.S.; Shim, J.-S., and Do, K., 2018. Enhancement of wave radar observation data quality at the Socheongcho ocean research station. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 571–575. Coconut Creek (Florida), ISSN 0749-0208. The Ocean Research Stations are constructed in the Yellow and East China Seas to conduct observations on oceanic, meteorological, and environmental components. For ocean waves, the stations have utilized the Miros Wave and Current Radar (MWR), which is a kind of remote-sensing wave observation instrument. Under mild weather conditions, however, the MWR tends to produce noisy measurements. To overcome this shortcoming, Miros Ltd. offers various types of filters including Reduce Noise Frequency, Correlation Check, Direction Filter, Reduce White Noise and Phillips Check. This study assesses the performance of each filter by comparing MWR measurements with the Directional Waverider Buoy (DWB) time series from the Socheongcho ORS for the period of January to April 2015. A statistical analysis reveals that the quality of the MWR data can be substantially improved by applying some of the filters to the MWR raw time series. From this assessment, we suggested that the combined adaptation of Correlation Check, Direction Filter, and Phillips Check are most suitable for the MWR observations in the Yellow and East China Seas. This study would be useful for scientific purposes such as sea area characterization and high wave generation mechanism. The improved MWR time series could be used for a scientific study on the spatial and temporal characteristics of waves in terms of the generation and propagation of waves particularly. [ABSTRACT FROM AUTHOR]

Quality control procedures based on nonvelocity parameters for use with a short-range radar system are applied with slight modification to long-range radar data collected offshore of North Carolina. The radar footprint covers shelf and slope environments and includes a segment of the Gulf Stream (GS). Standard processed and quality controlled (QCD) radar data are compared with 4 months of acoustic Doppler current profiler (ADCP) time series collected at three different sites within the radar footprint. Two of the ADCP records are from the shelf and the third is on the upper slope and is frequently within the GS. Linear regression and Bland-Altman diagrams are used to quantify the comparison. QCD data at all sites have reduced scatter and improved correlation with ADCP observations relative to standard processed data. Uncertainty is reduced by approximately 20%, and linear regression slopes and correlation coefficients increase by about 0.1. At the upper slope site, the QCD data also produced a significant increase in the mean speed. Additionally, a significant increase, averaging roughly 20%, in mean speed in the GS is apparent when comparing standard processed data and QCD data, concentrated at large range and at the azimuthal extremes of radial site coverage. Shifts in the distributions of the standard processed and QCD velocity estimates are consistent with the removal of zero-mean noise from the observations, which has minimal impact where the radial site range is <70 km and a large impact at greater range in the GS where mean currents exceed 1 m s−1. [ABSTRACT FROM AUTHOR]

In this paper, a detailed description of the roadmap to transform four individual CODAR SeaSonde high-frequency (HF) radars into an integrated HF radar network operated by Puertos del Estado (Madrid, Spain) is provided. To assess the maturation process into a fully operational status, the system must evolve via an implementation of phased approaches, including: homogenization of maintenance practices, quality control checks in real time, standardization of data access, and development of customized visualization tools. Additionally, continuous validation works with independent in situ sensors are required to provide upper bounds on both radial and total radar current measurement accuracies. Consistent correlation coefficients and uncertainty values emerge in the range of 0.31–0.81 and 8–22 cm/s, respectively, for the remotely estimated velocities. Complementarily, a dedicated online website has been developed to operationally monitor radar system health in real time. This automated quality control application analyzes a number of diagnose parameters to obtain estimates of their standard ranges and evaluate radar site performance according to them. Abrupt changes, gradual degradation, and/or failure problems can be easily detected, triggering alerts for troubleshooting. Once a good performance of HF radar systems is ensured, 2-D surface current maps are used for a broad range of practical applications, i.e., search-and-rescue operations, oil spill response, or the rigorous skill assessment of an operational ocean forecasting system such as Iberia–Biscay–Ireland (IBI), implemented within the frame of the Copernicus Marine Environment Monitoring Service. [ABSTRACT FROM AUTHOR]

Images of microwave sensors such as radar help to have well-timed data of land areas, which allow to produce basic mapping or thematic information. The radar sensors are more complex in their acquisition geometry. Therefore, this research defines criteria and procedures to obtain more precise estimates in the process of ortho-rectification of Synthetic Aperture Radar images. The methodology involved several processes that emphasized the ortho-rectification of SAR images, applying ephemeris adjustment with DGNSS ground control points. The distortions by relief effect were corrected by using a DEM suitable to the working scale in an area of North Central Venezuela. Subsequently, the cartographic product validation was performed in order to verify that it is within the mapping accuracy standards under different conditions of relief. As a result, a product at 1:50,000 scale which may be used in engineering applications and in any other application in Geomatics was obtained. The final result of the research is reflected in a synthesis of criteria and procedures for the planimetric control in the generation of orthoimages. [ABSTRACT FROM AUTHOR]

Non-precipitation echoes due to ground and sea clutter, chaff, anomalous propagation, biological targets, and interference in weather radar observations are major issues causing a decline in the accuracy of meteorological and hydrological applications based on radar data. Statistically based quality control techniques using polarimetric variables have improved the accuracy of radar echo classification, however their performance is affected by attenuation, nonuniform beam filling, and hydrometeor diversity as well as terrain blockage, beam broadening, and noise correction issues due to the quality degradation of polarimetric measurements. To address this, a new quality control algorithm, named clutter elimination algorithm for non-precipitation echo of radar data (CLEANER), was designed by employing independent feature parameters and variable classification conditions with spatial and temporal observation environments to adapt to these meteorological artifacts and observational limitations. CLEANER was applied to several precipitation cases with various non-precipitation echoes, showing improved performance compared with results from the fuzzy logic-based quality control algorithm in terms of non-precipitation echo removal as well as in precipitation echo conservation. In addition, CLEANER shows better computational efficiency and robustness, as well as an excellent expandability for different radar networks. [ABSTRACT FROM AUTHOR]

The cloud measurements for two years from the vertical pointing Ka-band cloud radar at Boseong in Korea are used to analyze detailed cloud properties. The reflectivity of the cloud radar is calibrated with other vertical pointing radars compared with the two disdrometers. A simple threshold-based quality control method is applied to eliminate non-meteorological echoes (insects and noise) in conjunction with despeckling along the radial direction. Clouds are classified into five types: high (HC), middle (MC), low (LC) for non-precipitating clouds, and deep (RainDP) and shallow (RainSH) for precipitating clouds. The average cloud frequency was about 35.9% with the maximum frequency of 50% in June for the total two-year sampling period. The RainDP occurred most frequently (11.8%), followed by HC (9.3%), MC (7.4%), RainSH (4.4%), and LC (2.9%) out of the average occurrence of the total 35.9%. HC and RainDP were frequently observed in summer and autumn, while RainSH, LC, and MC were dominant in the winter due to the dominant cloud development by the air-sea interaction during the cold air outbreak. The HC showed a significant seasonal variation of the maximum height and the rapid growth in the layer above 7 km (about −15 °C) in summer and autumn. This rapid growth appears in HC, MC, LC, and RainDP and is linked with rapid increases in Doppler velocity and mass flux. Thus, this growth is originated from the dominant riming processes in addition to depositional growth and is supported by an updraft in the layer between 6 and 8 km. MC showed a single frequency peak around 6 km with rapid growth above and strong evaporation below. The Doppler velocity of MC rapidly increases above 8 km and is nearly constant below this height due to strong evaporation except in the summer. LC had a similar trend of reflectivity (rapid growth in the HC region and strong evaporation in the lower region) lacking high frequency in the MC region. Unlike LC, the RainDP had continuous growth toward the ground in the entire layer with rapid growth in the HC and MC regions. In addition, two modes (cloud and precipitation) appear on the ground in spring and fall with the vertical continuity of the high frequency in the precipitation mode. The precipitation growth was most efficient in RainSH in summer with a reflectivity gradient of about 20 dBZ km−1 and frequent updrafts larger than 1 m s−1 and was smaller in the MC and HC regions. [ABSTRACT FROM AUTHOR]

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90°), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature. [ABSTRACT FROM AUTHOR]

In this paper, the authors made an attempt to detect the fibre content and fibre spacing in a steel fibre reinforced concrete (SFRC) industrial floor. Two non-destructive testing (NDT) methods, an electromagnetic induction technique and a radar-based technique, were applied. The first method allowed us to detect the spacing in subsequent layers located in the thickness of the slab. The result of the second method was a 3D visualization of the detected fibre in the volume of concrete slab. The conducted tests showed aptitude and limitations of the applied methods in estimating fibre volume and spacing. The two techniques also allowed us to locate the areas with relatively low fibre concentration, which are very likely to be characterized by low mechanical properties. [ABSTRACT FROM AUTHOR]

The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing. [ABSTRACT FROM AUTHOR]

This paper describes a method to provide quality control for radial velocity maps derived from radar echo voltage cross spectra measured by broad-beam high frequency radars. The method involves the comparison of voltage cross spectra measured at Doppler frequencies in the Bragg region with values predicted from basic equations defining the complex voltage cross spectra in terms of the measured antenna patterns and the radar cross section. Poor agreement at a given Doppler frequency indicates contamination of the spectra, usually due to interference; velocity results from that Doppler frequency are then eliminated. Examples are given of its application to broad-beam radars operating at four sites. [ABSTRACT FROM AUTHOR]