Generation Weather Radar Research Articles

The Operational Weather Radar Network in Europe

The operational weather radar network in Europe covers more than 30 countries and contains more than 200 weather radars. The radar network is heterogeneous in hardware, signal processing, transmit/receive frequency, and scanning strategy, thus making it fundamentally different than the Next Generation Weather Radar (NEXRAD) network. Another difference is that the density of the European weather radar network is roughly twice that of the NEXRAD network. Within the European National Meteorological Services (EUMETNET), a grouping of services, the Operational Program for Exchange of Weather Radar Information (OPERA) has been working since 1999 on improving the harmonization of radars and their measurements. In addition, OPERA has facilitated and stimulated the exchange of radar data between its members, among others, by the development of a radar data information model and jointly agreed data formats. Since 2011, a radar data center (“Odyssey”) has been in operation, producing network-wide radar mosaics from ...

An Empirical Model for Assessing the Severe Weather Potential of Developing Convection

Abstract The formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to observe. Given inherent uncertainties in storm development, it is intuitive to predict severe storms in a probabilistic manner. This paper presents such an approach to forecasting severe thunderstorms and their associated hazards, fusing together data from several sources as input into a statistical model. Mesoscale numerical weather prediction (NWP) models have been developed in part to forecast environments favorable to severe storm development. Geostationary satellites, such as the Geostationary Operational Environmental Satellite (GOES) series, maintain a frequently updating view of growing cumulus clouds over the contiguous United States to provide temporal trends in developing convection to forecasters. The Next Generation Weather Radar (NEXRAD) network delivers repeated scans of hydrometeors inside storms, monitoring the intensification of hydrometeor size and extent, as well as hydrometeor motion. Forecasters utilize NWP models, and GOES and NEXRAD data, at different stages of the forecast of severe storms, and the model described in this paper exploits data from each in an attempt to predict severe hazards in a more accurate and timely manner while providing uncertainty information to the forecaster. A preliminary evaluation of the model demonstrates good skill in the forecast of storms, and also displays the potential to increase lead time on severe hazards, as measured relative to the issuance times of National Weather Service (NWS) severe thunderstorm and tornado warnings and occurrence times of severe events in local storm reports.

Investigating the Effect of the “Land between the Lakes” on Storm Patterns

AbstractThe artificially created region around the “Land between the Lakes” (LBL) in Kentucky represents unique land use and land cover (LULC) heterogeneities. Over a distance of 100 km, the LULC comprises artificially created open water bodies (i.e., two parallel large run-on-river dams separated by the LBL), mountainous terrain, forest cover, and extensive agricultural land. Such heterogeneities increase (decrease) moisture supply and sensible heat, resulting in a differential air mass boundary that helps to initiate (inhibit) convection. Hence, the LBL can potentially modify precipitation formation. Historical anecdotes reveal a tendency for storms to dissipate or reintensify near the LBL. The specific scientific question pursued in this study is therefore the following: Has the unique development of two parallel run-on-river reservoirs and the surrounding LULC heterogeneity modified storm patterns in the region? Ten storm events during the growing season were selected. Two additional events, observed by the newly established high-resolution Kentucky Mesonet network, were also considered. Radar reflectivity images were visually inspected to understand the evolution of convective cells that originated or were modified near the LBL. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was used to determine near-surface trajectories that led to the selected events. The spatial synoptic classification and merged Geostationary Operational Environmental Satellite (GOES) IR images were analyzed to determine the prevailing synoptic conditions on the event dates. Six storm events showed a pattern wherein the convective cells lost strength as it passed over the LBL in a northeasterly direction. In two events, Next Generation Weather Radar (NEXRAD) reflectivity imagery revealed enhancement of convection as the storm passed over the LBL toward the Mississippi valley. Further dissection of the storm morphology suggested that the thermodynamic environment may have played an important role for the eight events where modification of precipitation near LBL has been clearly observed.

Tuning Extreme NEXRAD and CMORPH Precipitation Estimates

AbstractHigh-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.

Airborne Rain-Rate Measurement with a Wide-Swath Radar Altimeter

Abstract The NOAA Wide-Swath Radar Altimeter (WSRA) uses 80 narrow beams spread over ±30° in the cross-track direction to generate raster lines of sea surface topography at a 10-Hz rate from which sea surface directional wave spectra are produced. A ±14° subset of the backscattered power data associated with the topography measurements is used to produce independent measurements of rain rate and sea surface mean square slope at 10-s intervals. Theoretical calculations of rain attenuation at the WSRA 16.15-GHz operating frequency using measured drop size distributions for both mostly convective and mostly stratiform rainfall demonstrate that the WSRA absorption technique for rain determination is relatively insensitive to both ambient temperature and the characteristics of the drop size distribution, in contrast to reflectivity techniques. The variation of the sea surface radar reflectivity in the vicinity of a hurricane is reviewed. Fluctuations in the sea surface scattering characteristics caused by changes in wind speed or the rain impinging on the surface cannot contaminate the rain measurement because they are calibrated out using the WSRA measurement of mean square slope. WSRA rain measurements from a NOAA WP-3D hurricane research aircraft off the North Carolina coast in Hurricane Irene on 26 August 2011 are compared with those from the stepped frequency microwave radiometer (SFMR) on the aircraft and the Next Generation Weather Radar (NEXRAD) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE) system.

A test pattern identification algorithm and its application to CINRAD/SA(B) data

A variety of faulty radar echoes may cause serious problems with radar data applications, especially radar data assimilation and quantitative precipitation estimates. In this study, “test pattern” caused by test signal or radar hardware failures in CINRAD (China New Generation Weather Radar) SA and SB radar operational observations are investigated. In order to distinguish the test pattern from other types of radar echoes, such as precipitation, clear air and other non-meteorological echoes, five feature parameters including the effective reflectivity data percentage (R Z), velocity RF (range folding) data percentage (R RF), missing velocity data percentage (R M), averaged along-azimuth reflectivity fluctuation $$\left( {R_{N_{r,Z} } } \right)$$ and averaged along-beam reflectivity fluctuation $$\left( {R_{N_{a,Z} } } \right)$$ are proposed. Based on the fuzzy logic method, a test pattern identification algorithm is developed, and the statistical results from all the different kinds of radar echoes indicate the performance of the algorithm. Analysis of two typical cases with heavy precipitation echoes located inside the test pattern are performed. The statistical results show that the test pattern identification algorithm performs well, since the test pattern is recognized in most cases. Besides, the algorithm can effectively remove the test pattern signal and retain strong precipitation echoes in heavy rainfall events.

Improving Satellite-Based Convective Cloud Growth Monitoring with Visible Optical Depth Retrievals

AbstractThe use of geostationary satellites for monitoring the development of deep convective clouds has been recently well documented. One such approach, the University of Wisconsin Cloud-Top Cooling Rate (CTC) algorithm, utilizes frequent Geostationary Operational Environmental Satellite (GOES) observations to diagnose the vigor of developing convective clouds through monitoring cooling rates of infrared window brightness temperature imagery. The CTC algorithm was modified to include GOES visible optical depth retrievals for the purpose of identifying growing convective clouds in regions of thin cirrus clouds. An automated objective skill analysis of the two CTC versions (with and without the GOES visible optical depth) versus a variety of Next Generation Weather Radar (NEXRAD) fields was performed using a cloud-object tracking system developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies. The skill analysis was performed in a manner consistent with a recent study employing the same cloud-object tracking system. The analysis indicates that the inclusion of GOES visible optical depth retrievals in the CTC algorithm increases probability of detection and critical success index scores for all NEXRAD fields studied and slightly decreases false alarm ratios for most NEXRAD thresholds. In addition to better identifying vertically growing storms in regions of thin cirrus clouds, the analysis further demonstrates that the strongest cooling rates associated with developing convection are more reliably detected with the inclusion of visible optical depth and that storms that achieve intense reflectivity and large radar-estimated hail exhibit strong cloud-top cooling rates in much higher proportions than they do without the inclusion of visible optical depth.

Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

Comparison of Gauge and MPE Precipitation Data for the Chesapeake Bay Watershed Model

AbstractIn the interest of assessing an alternative source of precipitation input for the Chesapeake Bay Watershed model, the goal of this study is to compare the next generation weather radar (NEXRAD)-based multisensor precipitation estimator (MPE) with currently available gauge-based precipitation inputs for the model at both point-pixel and spatially aggregated scales. The comparison of gauge-based data with MPE at a point-pixel scale offers insight into how commonly known characteristics of gauge-point data are reflected in the mean areal gauge-based precipitation product. The time and effort for recalibration using MPE can be significantly reduced by strategizing the procedure according to the results of this study. Techniques and findings described here should be of general value for model users to anticipate changes in model parameters and output when moving from gauge-based to MPE precipitation in continuous simulation models.

Quantifying Errors due to Frequency Changes and Target Location Uncertainty for Radar Refractivity Retrievals

Abstract Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (ΔfTx) or the refractivity (ΔN). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a ΔfTx of 190 kHz (34 ppm); this allows the effect due to ΔN to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large ΔN develops relative to the reference period [e.g., ~55° when ΔN = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dual–pulse repetition frequency (PRF) operation of a magnetron transmitter.

Evolution of Severe and Nonsevere Convection Inferred from GOES-Derived Cloud Properties

AbstractGeostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, including thunderstorms. Initial research on thunderstorm growth with geostationary imagery focused on the mature stages of storm evolution, whereas more recent research on satellite-observed storm growth has concentrated on convective initiation, often defined arbitrarily as the presence of a given radar echo threshold. This paper seeks to link the temporal trends in robust GOES-derived cloud properties with the future occurrence of severe-weather radar signatures during the development phase of thunderstorm evolution, which includes convective initiation. Two classes of storms (severe and nonsevere) are identified and tracked over time in satellite imagery, providing distributions of satellite growth rates for each class. The relationship between the temporal trends in satellite-derived cloud properties and Next Generation Weather Radar (NEXRAD)-derived storm attributes is used to show that this satellite-based approach can potentially be used to extend severe-weather-warning lead times (with respect to radar-derived signatures), without a substantial increase in false alarms. In addition, the effect of varying temporal sampling is investigated on several storms during a period of GOES super-rapid-scan operations (SRSOR). It is found that, from a satellite perspective, storms evolve significantly on time scales shorter than the current GOES operational scan strategies.

Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method

AbstractThis study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

An Improved Method for Estimating Radar Echo-Top Height

Abstract It is demonstrated that the traditional method, in widespread use on Next Generation Weather Radar (NEXRAD) and other radar systems, to compute echo-top heights results in both under- and overestimates. It is proposed that echo tops be computed by interpolating between elevation scans that bracket the echo-top threshold. The traditional and proposed techniques are evaluated using simulated radar samples of a modeled thunderstorm and by sampling a high-resolution range–height indicator (RHI) of a real thunderstorm. It is shown that the proposed method results in smaller errors when higher-elevation scans are available.

Investigating rainfall estimation from radar measurements using neural networks

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

Diurnal variations in convective storm activity over contiguous North China during the warm season based on radar mosaic climatology

Diurnal variations in the formation and development of convective storms over contiguous North China during the warm season were studied using reflectivity from six China Next Generation Weather Radars between 2008 and 2011. Our results, including temporal and spatial analysis of hourly storm frequency through the warm season, and inter‐month comparisons during June, July and August, indicate that most storms initiate over the northwestern mountains in the afternoon as a result of solar heating, with the highest frequency in June, and the lowest in August. Storms propagate from the mountains to the southeastern foothills and plains, with the highest rates occurring in June, and the lowest in August. In the late afternoon, there is a remarkably high storm frequency over the foothills and plains, which indicates a significant topographic control on the southeastward propagation and intensification of storms during the warm season. Storm activity occurs mainly on the plains through the night, with the highest frequency in July and the lowest in June, as a result of a favorable nocturnal convection mechanism. The region‐averaged hourly storm frequencies for the warm season, and also for each month in JJA, are all bimodally distributed, with peak frequencies occurring in late afternoon and during the night, with the highest frequency recorded in the late afternoon during June and July, but at night in August. In general, the mean storm frequency is highest during the day and night in July, and lowest from afternoon to evening in August, but from nighttime to the next morning in June.

Evaluation of a near-real time NEXRAD DSP product in evolution of heavy rain events on the Upper Guadalupe River Basin, Texas

This study evaluates the Next Generation Weather Radar (NEXRAD) Digital Storm-Total Precipitation product (DSP) by analyzing 30 rain events on the Upper Guadalupe River Basin, Texas, from September 2006 to May 2007. The DSP product provides relatively accurate information on the evolution of rain events at high spatial and temporal resolutions in near-real time. This is particularly important for rainfall estimation of heavy rain events and flash flood forecasting. The DSP's accuracy is comparable to the other NEXRAD product MPE (multisensor precipitation estimator, at hourly resolution and 4 km grid spacing) at both hourly and event total scales for some heavy rain events, although the DSP is inferior to the MPE product for total rainfall of all 30 rain events analyzed, especially for light rain events. The DSP product shows the best agreement with gauges at ranges of 50–150 km from the radar (with mean absolute estimation bias (MAEB) of +15–22% for total rainfall of 30 rain events), while underestimating precipitation at both close ranges (<30 km) and far ranges (>180 km). The DSP product also tends to underestimate (overestimate) precipitation during event growth (dissipation). However, the total rainfall estimate for all rain events over a long period from DSP shows range dependence and is not recommended for calculation of water resource budget.

A Velocity Dealiasing Scheme for Synthetic C-Band Data from China’s New Generation Weather Radar System (CINRAD)

Abstract A velocity dealiasing algorithm is developed for C-band (5-cm wavelength) Doppler radars. With the shorter wavelength, C-band radars operating in a single PRF mode have a Nyquist interval about one-half that of S-band radars. As a proxy for C-band radar data, S-band Doppler data were sampled to produce radial velocity patterns as they would have been observed by a C-band Doppler radar. This algorithm includes six modules, some of which are modifications to previously existing methods. The modules (i) remove weak signals and determine the reference radials and dealias the radial wind field with a continuity constraint, (ii) perform a bulk continuity check, (iii) perform a “box” check to provide local consistency, (iv) perform a linear consistency fit in the radial direction, (v) provide quadratic least squares fit in the azimuthal direction, and (vi) perform quadratic least squares fit for elevation angle above 6.0° only for data from typhoons. The proposed dealiasing algorithm was tested on data from four typhoons and one squall line observed in China. When compared to the dealiasing results of the synthetic C-band data by the existing Next Generation Weather Radar (NEXRAD) algorithm, it was consistently better, particularly with smaller Nyquist cointervals. The algorithm correctly dealiased 91.56% of the aliased radial velocity data in all test cases.

Assessing NEXRAD P3 Data Effects on Stream-Flow Simulation Using SWAT Model in an Agricultural Watershed

AbstractRadar-derived next generation weather radar (NEXRAD) process 3 (P3) data offer high spatial resolution precipitation data that might improve stream-flow simulation accuracy using watershed models. The objective of this study was to assess the performance of spatially averaged subwatershed-specific P3 data on stream-flow simulations by using the soil and water assessment tool (SWAT) hydrologic model. This study was the first to evaluate the P3 product in watershed modeling. The SWAT model was chosen to simulate hydrological processes in North Fork Ninnescah Watershed (2,400 km2) located in south central Kansas. A precipitation gauge (PG) station was created for each subwatershed by using an area-weighted average of P3 precipitation estimates for all 16-km2 grid cells intersecting the subwatershed. Average subwatershed area (19 km2) was selected to correspond roughly with NEXRAD cell area to minimize spatial aggregation of radar precipitation data. The SWAT model was calibrated with both P3 data a...

Applications of a Velocity Dealiasing Scheme to Data from the China New Generation Weather Radar System (CINRAD)

Abstract An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon and heavy rain cases are slightly better than for squall-line cases.

Research on X-Band Weather Radar Network Based on Sensor Grid

Monitoring of precipitation using X-band weather radar systems is becoming popular. X-band weather radar network, as an additional equipment of China new generation weather radar, primarily used to measure weather echo within 3km above the ground and has a high prospect. The network, based on sensor grid, is greater information advantage and network advantage. This paper describes the design, the key technology and implementation of an architectural framework of the weather radar network based on sensor grid. The results show that the network works robustly in real time.