Generation Weather Radar Research Articles

Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones

The early tornadic phase of the Greensburg, Kansas, supercell on the evening of 4 May 2007 is simulated using a set of storm-scale (1-km horizontal grid spacing) 30-member ensemble Kalman filter (EnKF) data assimilation and forecast experiments. The Next Generation Weather Radar (NEXRAD) level-II radar data from the Dodge City, Kansas (KDDC), Weather Surveillance Radar-1988 Doppler (WSR-88D) are assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (COMMAS). The initially horizontally homogeneous environments are initialized from one of three reconstructed soundings representative of the early tornadic phase of the storm, when a low-level jet (LLJ) was intensifying. To isolate the impact of the low-level wind profile, 0–3.5-km AGL wind profiles from Vance Air Force Base, Oklahoma (KVNX), WSR-88D velocity-azimuth display (VAD) analyses at 0130, 0200, and 0230 UTC are used. A sophisticated, double-moment bulk ice microphysics scheme is employed. For each of the three soundings, ensemble forecast experiments are initiated from EnKF analyses at various times prior to and shortly after the genesis of the Greensburg tornado (0200 UTC). Probabilistic forecasts of the mesocyclone-scale circulation(s) are generated and compared to the observed Greensburg tornado track. Probabilistic measures of significant rotation and observation-space diagnostic statistics are also calculated. It is shown that, in general, the track of the Greensburg tornado is well predicted, and forecasts improve as forecast lead time decreases. Significant variability is also seen across the experiments using different VAD wind profiles. Implications of these results regarding the choice of initial mesoscale environment, as well as for the “Warn-on-Forecast” paradigm for probabilistic numerical prediction of severe thunderstorms and tornadoes, are discussed.

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

AbstractThis study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

Performance evaluation of interpolation methods for incorporating rain gauge measurements into NEXRAD precipitation data: a case study in the Upper Guadalupe River Basin

AbstractHigh spatial and temporal resolution of precipitation data is critical input for hydrological budget estimation and flash flood modelling. This study evaluated four methods [Bias Adjustment (BA), Simple Kriging with varying Local Means (SKlm), Kriging with External Drift (KED), and Regression Kriging (RK)] for their performances in incorporating gauge rainfall measurements into Next Generation Weather Radar (NEXRAD) multi‐sensor precipitation estimator (MPE; hourly and 4 × 4 km2). Measurements from a network of 50 gauges at the Upper Guadalupe River Basin, central Texas and MPE data for the year 2004 were used in the study. We used three evaluation coefficients percentage bias (PB), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) to examine the performance of the four methods for preserving regional‐ and local‐scale characteristics of observed precipitation data. The results show that the two Kriging‐based methods (SKlm and RK) are in general better than BA and KED and that the PB and NSE criteria are better than theR2criterion in assessing the performance of the four methods. It is also worth noting that the performance of one method at regional scale may be different from its performance at local scale. Critical evaluation of the performance of different methods at local or regional scale should be conducted according to the different purposes. The results obtained in this study are expected to contribute to the development of more accurate spatial rainfall products for hydrologic budget and flash flood modelling. Copyright © 2011 John Wiley & Sons, Ltd.

Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne synthetic aperture radar over land

Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rain rate local average value.

Radar-rainfall estimation algorithms of Hydro-NEXRAD

Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple (‘Quick Look’) to complex and computing-intensive (‘Hi-Fi’). The ‘Pseudo NWS PPS’ option allows close comparison with the algorithm used operationally by the US National Weather Service. The ‘Custom’ algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications

Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

A coupled stochastic space‐time intermittent random cascade model for rainfall downscaling

Analysis of Next Generation Weather Radar rainfall data indicates that for the central United States, rainfall exhibits a composite behavior with respect to its spatial and temporal scaling characteristics. Our data analysis shows that rainfall fluctuations at spatial scales smaller than a reference scale exhibit self‐similarity and that at scales larger than the reference scale, rainfall fluctuations are scale dependent. Accordingly, we present a new methodology for downscaling large‐scale rainfall consistent with this composite character of rainfall variability. The new downscaling model is a composite of a stochastic space‐time submodel that preserves the spatial and temporal dependency characteristics at scales larger than the reference scale and an intermittent random cascade submodel that preserves the statistical self‐similarity and spatial intermittency at scales smaller than the reference scale. The new model is applied to downscale summer daily rainfall for the central United States from a scale of 256 km to a scale of 2 km. We show that the new model reproduces quite well the intermittency and self‐similarity features and the interscale and across‐scale correlation structures of observed rainfall with a relatively low computational burden.

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

Three‐dimensional charge structure of a mountain thunderstorm

Lightning charge transport is analyzed for a thunderstorm which occurred on 18 August 2004 near Langmuir Laboratory in New Mexico. The analysis employs wide band measurements of the electric field by a balloon‐borne electric field sonde or Esonde, simultaneous Lightning Mapping Array measurements of VHF pulses emitted during lightning breakdown, and Next Generation Weather Radar data. The thunderstorm was composed of two principal updrafts. In the stronger updraft the positively charged particles reached altitudes up to 14 km, and in the weaker updraft the positive particles reached 11 km altitude. The negatively charged particles generated in the updraft appeared to reach altitudes up to 10 km in the strong updraft and 8 km in the weaker updraft. Just outside the updrafts the positive and negative particles drop sharply; thereafter, they drop down at a nearly linear rate, between 1 and 2 km in altitude per 10 km in horizontal distance. Initially, as the updraft developed, most charge was transported by updraft flashes; later, after about 15 to 20 min, extensive flashes were predominant. Most cloud‐to‐ground (CG) flashes transported negative charge from outside the updraft at 6 km altitude down to ground; however, some strokes of a CG reached into a higher negative charge region closer to the updraft. Nearly 6 times as much charge was transported by intracloud (IC) flashes when compared to CG flashes. The ratio of the average charge transport for an IC flash to the average charge transport for a CG flash was 1.6, while the average generator current associated with the combined updrafts was 2.3 amperes for 40 min.

Using CASA IP1 to Diagnose Kinematic and Microphysical Interactions in a Convective Storm

Abstract Data from the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project I (IP1) network of polarimetric X-band radars are used to observe a convective storm. A fuzzy logic hydrometeor identification algorithm is employed to study microphysical processes. Dual-Doppler techniques are used to analyze the 3D wind field. The scanning strategy, sensitivity, and low-level scanning focus of the radars are investigated for influencing bulk hydrometeor identification and dual-Doppler wind retrievals. Comparisons are made with the nearby S-band polarimetric Next Generation Weather Radar (NEXRAD) prototype radar (KOUN), for consistency. Lightning data are used as an independent indicator of storm evolution for comparison with radar observations. A new methodology for retrieving the vertical wind utilizing upward and variational integration techniques is employed and shown to illustrate trends in mean wind, with particularly good results at low levels. IP1 observations of a case on 10 June 2007 show the development of the updraft, subsequent graupel echo volume evolution, and a descending downdraft preceded by significant graupel in the midlevels, with updraft and graupel volumes leading the onset of lightning. Many of these trends are corroborated by KOUN. The high temporal resolution of three minutes and near-ground sampling provided by IP1 is integral to resolving up- and downdrafts, as well as hydrometeor evolution. IP1 coverage of the upper levels is diminished compared to KOUN, impacting the quality of the dual-Doppler derived vertical winds and ice echo volumes, although the low-level coverage helps to mitigate some errors. However, IP1 coverage of the low- to midlevels is demonstrated to be comparable or better than coverage by KOUN for this storm location.

Evidence of Rainfall Signatures on X-Band Synthetic Aperture Radar Imagery Over Land

Five spaceborne X-band synthetic aperture radars (X-SARs) are nowadays operating, and several more will be launched in the coming years. These X-SAR sensors, able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about a few hundred meters due to the moving-target degradation effects. This work is devoted to experimentally demonstrate this X-SAR capability, which can also be exploited to correct synthetic aperture radar (SAR) imagery for rainfall attenuation effects. Several case studies, selected from TerraSAR-X (TSX) overpasses over Europe and the southern U.S. in 2008, are qualitatively analyzed in terms of rainfall signatures. Visual validation of these rainfall SAR signatures is carried out by using available data from ground-based weather radars. A detailed data analysis for the case study of Hurricane ¿Gustav¿ on September 2, 2008, is carried out to assess a quantitative correlation among X-SAR response and near-surface precipitation rain rate. Two simplified empirical inversion algorithms, based on statistical regression and probability matching, are developed to retrieve rain rate from TSX cross-track ground-range measurements. The TSX-retrieved rain fields are compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (Alabama, U.S.), showing a root-mean-square error less than 15 mm/h and a correlation of about 0.7.

Sensitivity Studies of the Models of Radar-Rainfall Uncertainties

Abstract It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result from the measurement principles, parameter estimation, and the not fully understood physical processes. Even though comprehensive quantitative evaluation of the total radar-rainfall uncertainties has been the object of earlier studies, an open question remains concerning how the error model results are affected by parameter values and correction setups in the radar-rainfall algorithms. This study focuses on the effects of different exponents in the reflectivity–rainfall (Z–R) relation [Marshall–Palmer, default Next Generation Weather Radar (NEXRAD), and tropical] and the impact of an anomalous propagation removal algorithm. To address this issue, the authors apply an empirically based model in which the relation between true rainfall and radar rainfall could be described as the product of a systematic distortion function and a random component. Additionally, they extend the error model to describe the radar-rainfall uncertainties in an additive form. This approach is fully empirically based, and rain gauge measurements are considered as an approximation of the true rainfall. The proposed results are based on a large sample (6 yr) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet.

Algorithm for Estimation of the Specific Differential Phase

Abstract The specific differential phase Kdp is one of the important parameters measured by dual-polarization radar that is being considered for the upgrade of the current Next Generation Weather Radar (NEXRAD) system. Estimation of the specific differential phase requires computing the derivative of range profiles of the differential propagation phase. The existence of possible phase wrapping, noise, and associated fluctuation in the differential propagation phase makes the evaluation of derivatives an unstable numerical process. In this paper, a robust algorithm is presented to estimate the specific differential phase, which is able to work on wrapped phases and keep up with the spatial gradients of rainfall, to provide a high-resolution specific differential phase.

Regional CO2 and latent heat surface fluxes in the Southern Great Plains: Measurements, modeling, and scaling

Characterizing net ecosystem exchanges (NEE) of CO2 and sensible and latent heat fluxes in heterogeneous landscapes is difficult, yet critical given expected changes in climate and land use. We report here a measurement and modeling study designed to improve our understanding of surface to atmosphere gas exchanges under very heterogeneous land cover in the mostly agricultural U.S. Southern Great Plains (SGP). We combined three years of site‐level, eddy covariance measurements in several of the dominant land cover types with regional‐scale climate data from the distributed Mesonet stations and Next Generation Weather Radar precipitation measurements to calibrate a land surface model of trace gas and energy exchanges (isotope‐enabled land surface model (ISOLSM)). Yearly variations in vegetation cover distributions were estimated from Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index and compared to regional and subregional vegetation cover type estimates from the U.S. Department of Agriculture census. We first applied ISOLSM at a 250 m spatial scale to account for vegetation cover type and leaf area variations that occur on hundred meter scales. Because of computational constraints, we developed a subsampling scheme within 10 km “macrocells” to perform these high‐resolution simulations. We estimate that the Atmospheric Radiation Measurement Climate Research Facility SGP region net CO2 exchange with the local atmosphere was −240, −340, and −270 gC m−2 yr−1 (positive toward the atmosphere) in 2003, 2004, and 2005, respectively, with large seasonal variations. We also performed simulations using two scaling approaches at resolutions of 10, 30, 60, and 90 km. The scaling approach applied in current land surface models led to regional NEE biases of up to 50 and 20% in weekly and annual estimates, respectively. An important factor in causing these biases was the complex leaf area index (LAI) distribution within cover types. Biases in predicted weekly average regional latent heat fluxes were smaller than for NEE, but larger than for either ecosystem respiration or assimilation alone. However, spatial and diurnal variations of hundreds of W m−2 in latent heat fluxes were common. We conclude that, in this heterogeneous system, characterizing vegetation cover type and LAI at the scale of spatial variation are necessary for accurate estimates of bottom‐up, regional NEE and surface energy fluxes.

A Theory-Based Hydrometeor Identification Algorithm for X-Band Polarimetric Radars

Abstract Although much work has been done at S band to automatically identify hydrometeors by using polarimetric radar, several challenges are presented when adapting such algorithms to X band. At X band, attenuation and non-Rayleigh scattering can pose significant problems. This study seeks to develop a hydrometeor identification (HID) algorithm for X band based on theoretical simulations using the T-matrix scattering model of seven different hydrometeor types: rain, drizzle, aggregates, pristine ice crystals, low-density graupel, high-density graupel, and vertical ice. Hail and mixed-phase hydrometeors are excluded for the purposes of this study. Non-Rayleigh scattering effects are explored by comparison with S-band simulations. Variable ranges based on the theoretical simulations are used to create one-dimensional fuzzy-logic membership beta functions that form the basis of the new X-band HID. The theory-based X-band HID is applied to a case from the Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) network of X-band radars, and comparisons are made with similar S-band hydrometeor identification algorithms applied to data from the S-band polarimetric Next Generation Weather Radar (NEXRAD) prototype radar, KOUN. The X-band HID shows promise for illustrating bulk hydrometeor types and qualitatively agrees with analysis from KOUN. A simple reflectivity- and temperature-only HID is also applied to both KOUN and CASA IP1 data to reveal benefits of the polarimetric-based HID algorithms, especially in the classification of ice hydrometeors and oriented ice crystals.

Structure and Evolution of Precipitation along a Cold Front in the Northeastern United States

Abstract Heavy precipitation in the northeastern United States is examined through observational and numerical modeling analyses for a weather system that produced extreme rainfall rates and urban flash flooding over the New York–New Jersey region on 4–5 October 2006. Hydrometeorological analyses combine observations from Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars, the National Lightning Detection Network, surface observing stations in the northeastern United States, a vertically pointing lidar system, and a Joss–Waldvogel disdrometer with simulations from the Weather Research and Forecasting Model (WRF). Rainfall analyses from the Hydro-Next Generation Weather Radar (NEXRAD) system, based on observations from WSR-88D radars in State College, Pennsylvania, and Fort Dix, New Jersey, and WRF model simulations show that heavy rainfall was organized into long-lived lines of convective precipitation, with associated regions of stratiform precipitation, that develop along a frontal zone. Structure and evolution of convective storm elements that produced extreme rainfall rates over the New York–New Jersey urban corridor were influenced by the complex terrain of the central Appalachians, the diurnal cycle of convection, and the history of convective evolution in the frontal zone. Extreme rainfall rates and flash flooding were produced by a “leading line–trailing stratiform” system that was rapidly dissipating as it passed over the New York–New Jersey region. Radar, disdrometer, and lidar observations are used in combination with model analyses to examine the dynamical and cloud microphysical processes that control the spatial and temporal structure of heavy rainfall. The study illustrates key elements of the spatial and temporal distribution of rainfall that can be used to characterize flash flood hazards in the urban corridor of the northeastern United States.

Detection of flooding responses at the river basin scale enhanced by land use change

The Georges Creek watershed (area 187.5 km2) in western Maryland (United States) has experienced land use changes (>17% of area) associated with surface mining of coal. The adjacent Savage River watershed (area 127.2 km2) is unmined. Moments of flood frequency distributions indicated that climatic variability affected both watersheds similarly. Normalizing annual maximum flows by antecedent streamflow and causative precipitation helped identify trends in flooding response. Analysis of contemporary storm events using Next Generation Weather Radar (NEXRAD) stage III precipitation data showed that Georges Creek floods are characterized by higher peak runoff and a shorter centroid lag than Savage River floods, likely attributable to differences in current land use. Interestingly, Georges Creek produces only two thirds of the stormflow volume as Savage River, apparently because of infiltration into abandoned deep mine workings and an associated transbasin diversion constructed circa 1900. Empirical trend analysis is thus complicated by both hydroclimatic variability and the legacy of deep mining in the basin.

Investigating Spatial Downscaling of Satellite Rainfall Data for Streamflow Simulation in a Medium-Sized Basin

Abstract The objective of this study was to investigate spatial downscaling of satellite rainfall data for streamflow prediction in a medium-sized (970 km2) river basin prone to flooding. The spatial downscaling scheme used in the study was based on the principle of scale invariance. It reproduced the rainfall variability at finer scales while being conditioned on the large-scale rainfall. Two Tropical Rainfall Measuring Mission (TRMM)-based real-time global satellite rainfall products were analyzed: 1) the infrared (IR)-based 3B41RT product available at 1 hourly and 0.25° scales and 2) the combined passive microwave (PMW) and IR-based 3B42RT product available at 3 hourly and 0.25° scales. The conceptual Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used for the simulation of streamflow. It was found that propagation of spatially downscaled satellite rainfall in the hydrologic model increased simulation uncertainty in streamflow as rainfall grid scales became smaller than 0.25°. The streamflow simulation uncertainty for satellite downscaling was found to be very similar to that for ground validation Next Generation Weather Radar (NEXRAD) downscaling at any given scale, indicating that the effectiveness of the spatial downscaling scheme is not influenced by rainfall data type. Closer inspection at the subbasin level revealed that the limitation of the selected spatial downscaling scheme to preserve the mean rainfall intensity for irregularly sized drainage units was responsible for the increase in simulation uncertainty as scales became smaller. Although the findings should not be construed as a generalization for spatial downscaling schemes, there is a need for more rigorous hydrometeorological assessment of downscaled satellite rainfall data prior to institutionalizing its use for real-time streamflow simulation over ungauged basins.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

Abstract The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

Weather Radar Ground Clutter. Part I: Identification, Modeling, and Simulation

Abstract Real-time ground-clutter identification and subsequent filtering of clutter-contaminated data is addressed in this two-part paper. Part I focuses on the identification, modeling, and simulation of S-band ground-clutter echo. A new clutter identification parameter, clutter phase alignment (CPA), is presented. CPA is a measure primarily of the phase variability of the in-phase and quadrature-phase time series samples for a given radar resolution volume. CPA is also a function of amplitude variability of the time series. It is shown that CPA is an excellent discriminator of ground clutter versus precipitation echoes. A typically used weather model, time series simulator is shown to inadequately describe experimentally observed CPA. Thus, a new technique for the simulation of ground-clutter echo is developed that better predicts the experimentally observed CPA. Experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG), and NCAR’s S-band dual-polarization Doppler radar (S-Pol) are used to illustrate CPA. In Part II, CPA is used in a fuzzy logic algorithm for improved clutter identification.