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There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes. [ABSTRACT FROM AUTHOR]

A National Science Foundation sponsored educational deployment of a Doppler on Wheels radar called the Hawaiian Educational Radar Opportunity (HERO) was conducted on Oahu from 21 October to 13 November 2013. This was the first-ever deployment of a polarimetric X-band (3 cm) research radar in Hawaii. A unique fine-resolution radar and radiosonde dataset was collected during 16 intensive observing periods through a collaborative effort between University of Hawai'i at Manoa undergraduate and graduate students and the National Weather Service's Weather Forecast Office in Honolulu. HERO was the field component of MET 628 "Radar Meteorology," with 12 enrolled graduate students who collected and analyzed the data as part of the course. Extensive community outreach was conducted, including participation in a School of Ocean and Earth Science and Technology open house event with over 7,500 visitors from local K-12 schools and the public. An overview of the HERO project and highlights of some interesting tropical rain and cloud observations are described. Phenomena observed by the radar include cumulus clouds, trade wind showers, deep convective thunderstorms, and a widespread heavy rain event associated with a cold frontal passage. Detailed cloud and precipitation structures and their interactions with O'ahu terrain, unique dual-polarization signatures, and the implications for the dynamics and microphys-ics of tropical convection are presented. [ABSTRACT FROM AUTHOR]

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

This study investigates the influence of microscale turbulent clustering of cloud droplets on the radar reflectivity factor and proposes a new parameterization to account for it. A three-dimensional direct numerical simulation of particle-laden isotropic turbulence is performed to obtain turbulent clustering data. The clustering data are then used to calculate the power spectra of droplet number density fluctuations, which show a dependence on the Taylor microscale-based Reynolds number (Re λ) and the Stokes number (St). First, the Reynolds number dependency of the turbulent clustering influence is investigated for 127 < Re λ < 531. The spectra for this wide range of Re λ values reveal that Re λ = 204 is sufficiently large to be representative of the whole wavenumber range relevant for radar observations of atmospheric clouds. The authors then investigate the Stokes number dependency for Re λ = 204 and propose an empirical model for the turbulent clustering influence assuming power laws for the number density spectrum. For Stokes numbers less than 2, the proposed model can estimate the influence of turbulence on the spectrum with an RMS error less than 1 dB when calculated over the wavenumber range relevant for radar observations. For larger Stokes number droplets, the model estimate has larger errors, but the influence of turbulence is likely negligible in typical clouds. Applications of the proposed model to two idealized cloud observing scenarios reveal that microscale turbulent clustering can cause a significant error in estimating cloud droplet amounts from radar observations with microwave frequencies less than 13.8 GHz. [ABSTRACT FROM AUTHOR]

An operational method is presented that corrects the bias of radar-based quantitative precipitation estimations (QPE) in radar networks that is due to the vertical profile of reflectivity (VPR) factor. It is used in both rain and snowfall. Measured average VPRs are obtained from the volume scans of each radar at ranges of 2-40 km. At each radar, two time ensembles of the bias estimates are made use of: the first ensemble contains 0-24 members at each range gate, calculated by beam convolution from the measured VPRs at 15-min intervals during the most recent 6 h. The second ensemble similarly contains 24 members calculated from parameterized climatological VPRs. In each scan the precipitation type classification and the climatological VPR are matched with the freezing level obtained from a numerical weather prediction model. The members of the two ensembles are weighted for both time lapse and quality and are then combined. At each composite grid point, the value of the networked VPR correction is then determined as a distance-weighted mean of the time ensembles of biases from all radars located closer than 300 km. In the absence of calibration errors, the resulting estimate of the reflectivity factor at ground level Z e is a seamless continuous field. As verified by radar-radar and radar-gauge comparisons in the Finnish network of eight C-band Doppler radars, the method efficiently reduces the range-dependent bias in QPE. For example, at radar ranges of 141-219 km, the average bias in the ground level Z e was −8.7 and 1.2 dB before and after the VPR correction, respectively. [ABSTRACT FROM AUTHOR]

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 volumetric data. An essential part of the OPERA work is the documentation of the members' best practices in radar operation and data production and the making of joint recommendations: for example, on the interferences caused by other microwave sources and the disturbances caused by wind turbines. Hence, the expertise of the most experienced members is made available to all members supporting the development of the network as a whole. Recent work has produced reports on best practices for production of radar data, on quality indicators, and on experiences with the use of polarimetric radars. All of these reports and recommendations are publicly available on the OPERA website, for use by the wider meteorological community. [ABSTRACT FROM AUTHOR]

An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system's computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications. [ABSTRACT FROM AUTHOR]

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 (Δ fTx) or the refractivity (Δ N). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a Δ fTx of 190 kHz (34 ppm); this allows the effect due to Δ N to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large Δ N develops relative to the reference period [e.g., ~55° when Δ N = 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. [ABSTRACT FROM AUTHOR]

This study presents a two-dimensional variational approach to retrieving raindrop size distributions (DSDs) from polarimetric radar data in the presence of attenuation. A two-parameter DSD model, the constrained-gamma model, is used to represent rain DSDs. Three polarimetric radar measurements-reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP-are optimally used to correct for the attenuation and retrieve DSDs by taking into account measurement error effects. Retrieval results with simulated data demonstrate that the proposed algorithm performs well. Applications to real data collected by the X-band Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radars and the C-band University of Oklahoma-Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) also demonstrate the efficacy of this approach. [ABSTRACT FROM AUTHOR]

This study presents the synthesis of dual-Doppler and cloud photography data of the 5 June 2009 Goshen County, Wyoming, tornado observed during the Second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). Analyses focused on the hook region of the parent supercell. It will be shown that radar-determined tornadogenesis and initial surface wind damage occurred 14 min before the funnel cloud was observed continuously on the ground. In addition to the cyclonic wall cloud, an anticyclonic lowering was also observed on the southern flank of the hook echo near the time of tornadogenesis. The relationship between the intensities of the tornado and its parent circulation, the low-level mesocyclone, will also be discussed. Funnel diameter was not well correlated with the maximum vertical vorticity or circulation associated with the mesocyclone. Furthermore, changes in the minimum reflectivity observed in the tornado-scale weak echo hole (WEH) were weakly correlated with the maximum vertical vorticity of the mesocyclone. The tornado funnel was observed within and was relatively small compared to the WEH diameter. The distribution and evolution of angular momentum were also examined. The radial increase of angular momentum terminated at or beyond the wall cloud edge. Prior to the time that the funnel made continuous contact with the ground, low-level angular momentum increased despite the fact that azimuthally averaged low-level flow within the mesocyclone was divergent, advecting low angular momentum air away from the circulation center. Both the tornado and mesocyclone generally intensified during this time. Thereafter, while the tornado continued to intensify, angular momentum within the low-level mesocyclone weakened. [ABSTRACT FROM AUTHOR]

The range-weighting function (RWF) determines how individual scatterer contributions are weighted as a function of range to produce the meteorological data associated with a single resolution volume. The RWF is commonly defined in terms of the transmitter pulse envelope and the receiver filter impulse response, and it determines the radar range resolution. However, the effective RWF also depends on the range-time processing involved in producing estimates of meteorological variables. This is a third contributor to the RWF that has become more significant in recent years as advanced range-time processing techniques have become feasible for real-time implementation on modern radar systems. In this work, a new formulation of the RWF for weather radars that incorporates the impact of signal processing is proposed. Following the derivation based on a general signal processing model, typical scenarios are used to illustrate the variety of RWFs that can result from different range-time signal processing techniques. Finally, the RWF is used to measure range resolution and the range correlation of meteorological data. [ABSTRACT FROM AUTHOR]

A network of three boundary layer radar wind profilers is used to study characteristics of mountain waves and rotors and to explore the utility of such a network. The data employed were collected as part of the Terrain-Induced Rotor Experiment (T-REX), which took place in Owens Valley, California, in early 2006. The wind profilers provide a continuous time--height representation of wave and rotor structure. During intensive observing period 3 (IOP 3), the profiler network was positioned in an L-shaped configuration, capturing key features of the mountain waves and rotor, including the boundary layer vortex sheet (or shear layer), turbulence within this shear layer, the classical lower turbulence zone (LTZ), and wave motion above the LTZ. Observed features were found to be in good agreement with recent high-resolution numerical simulations. Using the wind profiler with superior time resolution (Multiple Antenna Profiler Radar), a series of updraft--downdraft couplets were observed beneath the first downwind wave crest. These are interpreted as signatures of subrotors. Such detailed observations of subrotors are rare, even though subrotors are believed to be a common feature of rotor circulations in Owens Valley. During IOP 6, the network was repositioned to form a line across the valley. A simple algorithm was used to determine the amplitude, wavelength, and phase of the primary wave over the valley and to observe their changes over time and height. In the IOP-6 case, the wavelength increased over time, the phase indicated an eastward-shifting wave crest, and the amplitude increased with height and also varied over time. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at a symposium on the development of radar technology for weather surveillance is presented. Topics include the efforts key to a successful multifunction phased array radar (MPAR) risk-reduction plan, the future Federal Aviation Administration challenges, and the mission requirements for aircraft detection. The symposium featured several leaders from different agencies including Mary Glackin, Victoria Cox and Randel Zeller.

A large collection of radar reflectivity maps gathered from 1988 to 1992 at the Spino d’Adda experimental station, located in the Padana Valley, has been exploited to investigate the statistical properties of rain structures and their descriptors. The results of this analysis can be of interest for meteorological, hydrological, and telecommunication applications. The authors found that the isosuperficial diameter follows an exponential distribution; when the threshold of rain intensity is increased, disappearance is dominant over fragmentation; moreover, the number of “mother cells” that generate N “daughter cells” decreases exponentially with N. To give a complete but concise characterization of the geometrical, physical, and morphological properties of rain cells, a set of analytical descriptors has been introduced and statistically defined through their probability density functions and the centrality, dispersion, and excursion parameters. As a final point, comparative statistical analyses have been performed at different thresholds for every couple of descriptors introduced, which allowed the authors to highlight correlations between them. [ABSTRACT FROM AUTHOR]

The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar. [ABSTRACT FROM AUTHOR]

To obtain statistically stable reflectivity measurements by meteorological radars, it is common practice to average over several consecutive pulses during which the antenna rotates at a certain angular velocity. Taking into account the antenna’s continuous motion, the measured reflectivity is determined by an effective beam weighting function, which is different from a single-pulse weighting function—a fact that is widely ignored in applications involving beam weighting. In this paper, the effective beam weighting function is investigated in detail. The theoretical derivation shows that the effective weighting function is essentially a simple moving sum of single-beam weighting functions. Assuming a Gaussian shape of a single pulse, a simple and easy-to-use parameterization of the effective beam weighting function is arrived at, which depends only on the single beamwidth and the ratio of the single beamwidth to the rotational angular averaging interval. The derived relation is formulated in the “radar system” (i.e., the spherical coordinate system consisting of azimuth and elevation angles) that is often applied in practice. Formulas for the “beam system” (two orthogonal angles relative to the beam axis) are also presented. The final parameterization should be applicable to almost all meteorological radars and might be used (i) in specialized radar data analyses (with ground-based or satellite radars) and (ii) for radar forward operators to calculate simulated radar parameters from the results of NWP models. [ABSTRACT FROM AUTHOR]

High-resolution Doppler lidar observations obtained during the Mesoscale Alpine Program (MAP) 1999 field campaign are used to investigate the along-valley structure of daytime valley flows in the Wipp Valley, Austria. The observations show that under varying ambient conditions the valley flow increases in speed through a narrow section of the valley. Furthermore, the along-valley volume flux diverges along the valley segment under investigation, which suggests that the observed along-valley acceleration of the valley flow cannot be explained by the horizontal constriction of the valley sidewalls. It is hypothesized that the along-valley acceleration of the flow is caused by an intravalley change in the horizontal pressure gradient induced by differential heating rates of the valley atmosphere. [ABSTRACT FROM AUTHOR]

The spatial variability and temporal variability of precipitation are widely recognized. In particular, rainfall rates can fluctuate widely in regions where the raindrops are clustered and where mean conditions are changing (statistical heterogeneity). Indeed, at times, the ambiguity associated with an estimated average rainfall rate may become very large. Therefore, in quantitative measurements of precipitation, it would be useful to identify where this occurs. In this work a technique is proposed and applied to quantify the variability in rainfall rates introduced by statistical heterogeneity and raindrop clustering using deviations from Rayleigh statistics of intensity fluctuations. This technique separates the Rayleigh contributions to the observed relative dispersion from those arising from clustering and statistical heterogeneities. Applications to conventional meteorological radar measurements are illustrated using two scans. Often, but not always, the greatest ambiguities in estimates of the average rainfall rate occur just where the rainfall rates are the largest and presumably where accurate estimates are most important. This ambiguity is not statistical; rather, it indicates the presence of important sub-beam-scale fluctuations. As a consequence, no single average value can be applied uniformly to the entire domain. The examples provided here also demonstrate that the appropriate observations are feasible using current conventional meteorological radars with adequate processing capabilities. However, changes in radar technology that improve and increase pulse-to-pulse statistical independence will permit such observations to be gathered more routinely at finer spatial resolution and with enhanced precision. [ABSTRACT FROM AUTHOR]

An airborne microwave temperature profiler (MTP) was deployed during the Texas 2000 Air Quality Study (TexAQS-2000) to make measurements of boundary layer thermal structure. An objective technique was developed and tested for estimating the mixed layer (ML) height from the MTP vertical temperature profiles. The technique identifies the ML height as a threshold increase of potential temperature from its minimum value within the boundary layer. To calibrate the technique and evaluate the usefulness of this approach, coincident estimates from radiosondes, radar wind profilers, an aerosol backscatter lidar, and in situ aircraft measurements were compared with each other and with the MTP. Relative biases among all instruments were generally less than 50 m, and the agreement between MTP ML height estimates and other estimates was at least as good as the agreement among the other estimates. The ML height estimates from the MTP and other instruments are utilized to determine the spatial and temporal evolution of ML height in the Houston, Texas, area on 1 September 2000. An elevated temperature inversion was present, so ML growth was inhibited until early afternoon. In the afternoon, large spatial variations in ML height developed across the Houston area. The highest ML heights, well over 2 km, were observed to the north of Houston, while downwind of Galveston Bay and within the late afternoon sea breeze ML heights were much lower. The spatial variations that were found away from the immediate influence of coastal circulations were unexpected, and multiple independent ML height estimates were essential for documenting this feature. [ABSTRACT FROM AUTHOR]

Vertical profiles of ice water content (IWC) can now be derived globally from spaceborne cloud satellite radar (CloudSat) data. Integrating these data with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data may further increase accuracy. Evaluations of the accuracy of IWC retrieved from radar alone and together with other measurements are now essential. A forward model employing aircraft Lagrangian spiral descents through mid- and low-latitude ice clouds is used to estimate profiles of what a lidar and conventional and Doppler radar would sense. Radar reflectivity Ze and Doppler fall speed at multiple wavelengths and extinction in visible wavelengths were derived from particle size distributions and shape data, constrained by IWC that were measured directly in most instances. These data were provided to eight teams that together cover 10 retrieval methods. Almost 3400 vertically distributed points from 19 clouds were used. Approximate cloud optical depths ranged from below 1 to more than 50. The teams returned retrieval IWC profiles that were evaluated in seven different ways to identify the amount and sources of errors. The mean (median) ratio of the retrieved-to-measured IWC was 1.15 (1.03) ± 0.66 for all teams, 1.08 (1.00) ± 0.60 for those employing a lidar–radar approach, and 1.27 (1.12) ± 0.78 for the standard CloudSat radar–visible optical depth algorithm for Ze > -28 dBZe. The ratios for the groups employing the lidar–radar approach and the radar–visible optical depth algorithm may be lower by as much as 25% because of uncertainties in the extinction in small ice particles provided to the groups. Retrievals from future spaceborne radar using reflectivity–Doppler fall speeds show considerable promise. A lidar–radar approach, as applied to measurements from CALIPSO and CloudSat, is useful only in a narrow range of ice water paths (IWP) (40 < IWP < 100 g m-2). Because of the use of the Rayleigh approximation at high reflectivities in some of the algorithms and differences in the way nonspherical particles and Mie effects are considered, IWC retrievals in regions of radar reflectivity at 94 GHz exceeding about 5 dBZe are subject to uncertainties of ±50%. [ABSTRACT FROM AUTHOR]

Several radar fine lines, all with a humidity contrast, were sampled in the central Great Plains during the 2002 International H2O Project (IHOP). This study primarily uses aircraft and airborne millimeter-wave radar observations to dynamically interpret the presence and vertical structure of these fine lines as they formed within the well-developed convective boundary layer. In all cases the fine line represents a boundary layer convergence zone. This convergence sustains a sharp contrast in humidity, and usually in potential temperature, across the fine line. The key question addressed herein is whether, at the scale examined here (∼10 km), the airmass contrast itself, in particular the horizontal density (virtual potential temperature) difference and resulting solenoidal circulation, is responsible for the sustained convergence and the radar fine line. For the 10 cases examined herein, the answer is affirmative. [ABSTRACT FROM AUTHOR]

A method of analyzing radar data is developed and applied to determine whether the X-band radar reflectivity evolution of clouds observed during summertime on the northeast Florida coast during the Small Cumulus Microphysics Study (SCMS) shows distinct differences in precipitation development that can be associated with the clouds’ maritime or continental characteristics. For this study, the entire National Center for Atmospheric Research CP2 radar dataset from SCMS was examined, and 38 clouds were used. For these clouds the evolution in X-band radar reflectivity, from the clouds’ earliest detection through precipitation, was clearly documented and met specific requirements concerning the clouds’ location relative to the coastline and direction of movement. Since cloud condensation nuclei (CCN) and giant and ultragiant nuclei (GN) measurements were not available for the specific clouds used in this study, proxies were used to partition the clouds into four groups based on the cloud location and direction of movement. Specifically, it was assumed that clouds forming over the ocean during onshore flow had maritime characteristics (group 1: low CCN, high GN), clouds forming over land during onshore flow would have modified maritime characteristics (group 2: high CCN, high GN), clouds forming over land during offshore flow would have continental characteristics (group 3: high CCN, low GN), and clouds forming over the ocean during offshore flow would have modified continental characteristics (group 4: high CCN, high GN). These assumptions are based on past measurements presented by Sax and Hudson. Then, these populations were statistically compared using the nonparametric multiresponse permutation procedure developed by Mielke et al. A comparison of groups 1 and 2 provided a test of the role of CCN concentrations in precipitation development in these cloud populations. A comparison of groups 3 and 4 provided a test of the role of GN concentrations in precipitation development in these cloud populations. The two cloud populations that were disjoint at a statistically significant level were groups 1 and 2. For these groups, the analysis showed that the median characteristic total water content of the truly maritime clouds (group 1) was about half that of the modified maritime clouds (group 2) at the time of precipitation formation. The characteristic time to precipitation formation was about 60% smaller for the truly maritime clouds. Thus, the characteristic reflectivity threshold for precipitation development was reached at a much lower altitude above cloud base in a much faster time in the truly maritime clouds. This result supports the conclusions of Hudson and Yum that precipitation development in the SCMS clouds was primarily controlled by CCN concentrations rather than GN concentrations. [ABSTRACT FROM AUTHOR]

Polarimetric Doppler radars provide valuable information about the kinematic and microphysical structure of storms. However, in-depth analysis using radar products, such as Doppler-derived wind vectors and hydrometeor identification, has been difficult to achieve in (near) real time, mainly because of the large volumes of data generated by these radars, lack of quick access to these data, and the challenge of applying quality-control measures in real time. This study focuses on modifying and automating several radar-analysis and quality-control algorithms currently used in postprocessing and merging the resulting data from several radars into an integrated analysis and display in (near) real time. Although the method was developed for a specific network of four Doppler radars: two Weather Surveillance Radar-1988 Doppler (WSR-88D) radars (KFTG and KCYS) and two Colorado State University (CSU) research radars [Pawnee and CSU–University of Chicago–Illinois State Water Survey (CSU–CHILL)], the software is easily adaptable to any radar platform or network of radars. The software includes code to synthesize radial velocities to obtain three-dimensional wind vectors and includes algorithms for automatic quality control of the raw polarimetric data, hydrometeor identification, and rainfall rate. The software was successfully tested during the summers of 2004 and 2005 at the CSU–CHILL radar facility, ingesting data from the four-radar network. The display software allows users the ability to view mosaics of reflectivity, wind vectors, and rain rates, to zoom in and out of radar features easily, to create vertical cross sections, to contour data, and to archive data in real time. Despite the lag time of approximately 10 min, the software proved invaluable for diagnosing areas of intense rainfall, hail, strong updrafts, and other features such as mesocyclones and convergence lines. A case study is presented to demonstrate the utility of the software. [ABSTRACT FROM AUTHOR]

Lidar remote sensing measurements of low-level water clouds in the form of vertical soundings and instantaneous (∼1 min) azimuth-over-elevation scans are reported. Retrievals are made of the liquid water content and effective droplet diameter at the same range, time, and angular resolutions as those of the measurements. The results are presented as time–height plots and two-dimensional horizontal maps of the retrieved parameters. The cloud structure is resolved by calculating histograms, spatial autocorrelation functions, and power spectra. The distribution of the horizontal inhomogeneities is characterized over the size range from 10 to ∼1000 m. The Kolmogorov -5/3 power-law dependence is verified in all cases, but the -5/3 regime is broken into two subregimes that are separated by a sudden increase in the energy density level. The results illustrate how lidars can contribute meaningful information on cloud structure at high spatial and temporal resolutions, in near–real time, and over extended periods of time. [ABSTRACT FROM AUTHOR]

Three-dimensional cloud-resolving model simulations of a mesoscale region around Kwajalein Island during the Kwajalein Experiment (KWAJEX) are performed. Using observed winds along with surface and large-scale thermodynamic forcings, the model tracks the observed mean thermodynamic soundings without thermodynamic nudging during 52-day simulations spanning the whole experiment time period, 24 July–14 September 1999. Detailed comparisons of the results with cloud and precipitation observations, including radar reflectivities from the Kwajalein ground validation radar and International Satellite Cloud Climatology Project (ISCCP) cloud amounts and radiative fluxes, reveal the biases and sensitivities of the model’s simulated clouds. The amount and optical depth of high cloud are underpredicted by the model during less rainy periods, leading to excessive outgoing longwave radiation (OLR) and insufficient albedo. The simulated radar reflectivities tend to be excessive, especially in the upper troposphere, suggesting that simulated high clouds are precipitating large hydrometeors too efficiently. Occasionally, large-scale advective forcing errors also seem to contribute to upper-level cloud and relative humidity biases. An extensive suite of sensitivity studies to different microphysical and radiative parameterizations is performed, with surprisingly little impact on the results in most cases. [ABSTRACT FROM AUTHOR]

The main object of this paper is to emphasize that clouds—the nonprecipitating component of condensed atmospheric water—can produce a strong attenuation at operational microwave frequencies, although they present a low reflectivity preventing their radar detection. By way of a simple and realistic model, simulations of radar observations through warm precipitating targets are thus presented in order to quantify cloud attenuation. Simulations concern an airborne radar oriented downward and observing precipitation at four frequencies: 3, 10, 35, and 94 GHz. Two cases are first considered: a convective cell (vigorous cumulus congestus plus rain) and a stratiform one (nimbostratus plus drizzle) superimposed on the previous one. Other simulations are then performed on different types of cumulus (congestus, mediocris, and humilis) with various thicknesses characterized, in a microphysical sense, by their maximum liquid water content. Simulations confirm the low cumulus reflectivity ranging from -45 dBZ for the weakest cumulus (i.e., the humilis one) to -5 dBZ for the strongest one (i.e., the vigorous cumulus congestus). It reaches -35 dBZ for the nimbostratus cloud. On the other hand, cumulus attenuation [precisely path-integrated cloud attenuation (PICA)] is not negligible and, depending on the frequency, can be very strong: the higher the frequency, the stronger the PICA. At 3 GHz, the far less attenuated frequency, PICA for the vigorous cumulus congestus alone in the convective cell (embedded into the stratiform background) is on the order of 1.2 dB (1.5 dB) at 10 GHz, 16 dB (20 dB) at 35 GHz, and 80 dB (100 dB) at 94 GHz. For weaker cumulus, PICA is lower but, in certain cases, significant. All these results mean that it is necessary to be very careful about radar measurements if reliable information on precipitation—for example, the precipitation rate R—has to be deduced, particularly at high operational frequencies. [ABSTRACT FROM AUTHOR]

Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements. Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology). In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold). The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role. [ABSTRACT FROM AUTHOR]

During the Joint Urban 2003 (JU2003) atmospheric field experiment in Oklahoma City, Oklahoma, of July 2003, lidar teams from Arizona State University and the Army Research Laboratory collaborated to perform intersecting range–height indicator scans. Because a single lidar measures radial winds, that is, the dot product of the wind vector with a unit vector pointing along the lidar beam, the data from two lidars viewing from different directions can be combined to produce horizontal velocity vectors. Analysis programs were written to retrieve horizontal velocity vectors for a series of eight vertical profiles to the southwest (approximately upwind) of the downtown urban core. This technique has the following unique characteristics that make it well suited for urban meteorology studies: 1) continuous vertical profiles from far above the building heights to down into the street canyons can be measured and 2) the profiles can extend to very near the ground without a loss of accuracy (assuming clear lines of site). The period of time analyzed spans from 1400 to 1730 UTC (0900–1230 local time) on 9 July 2003. Both shear and convective heating are important during the development of the boundary layer over this period of time. Differences in 10- and 20-min mean profiles show the effect of the variation of position approaching the urban core; for example, several hundred meters above the ground, velocity magnitudes for profiles separated by less than a kilometer may differ by over 1 m s-1. The effect of the increased roughness associated with the central business district can be seen as a deceleration of the velocity and a turning of the wind direction as the flow approaches the core, up to approximately 10° for some profiles. This effect is evident below 400–500 m both in the wind directions and magnitudes. Recommendations are given for how this type of data can be used in a comparison with model data. [ABSTRACT FROM AUTHOR]

The steeply rising coastal terrain of southeast Alaska can produce a wide variety of terrain-induced flows such as barrier jets, gap flows, and downslope wind storms. This study uses a combination of satellite remote sensing, field observations, and modeling to improve our understanding of the dynamics of these flows. After examining several thousand synthetic aperture radar (SAR) high-resolution wind speed images over the Gulf of Alaska, several subclasses of barrier jets were identified that do not fit the current conceptual model of barrier jet development. This conceptual model consists of an acceleration and turning of the ambient cross-barrier flow into the along-barrier direction when the ambient low-level flow is blocked by terrain; however, the SAR imagery showed many barrier jet cases with significant flow variability in the along-coast direction as well as evidence for the influence of cold, dry continental air exiting the gaps in coastal terrain. A subclass of jets has been observed where the transition from the coastal to the offshore flow is abrupt. The results from these climatological studies have motivated modeling studies of selected events as well as field observations from the Southeast Alaska Regional Jets (SARJET) experiment field campaign in the Gulf of Alaska during fall of 2004. This paper will highlight preliminary results obtained during SARJET, which collected in situ measurements of barrier jets and gap flows using the University of Wyoming's King Air research aircraft. [ABSTRACT FROM AUTHOR]

Features of the melting layer of precipitation such as height and thickness are important in many meteorological applications. Doppler radar observations with a vertically pointing antenna have often been used in order to derive these features and to investigate the physical processes governing formation of the precipitation. This paper presents a technique to detect the characteristics of the melting layer based on the standard deviation of polarimetric radar measurements taken at vertical incidence. Using two case studies with stratiform and convective precipitation, the proposed technique is compared with conventional techniques based on reflectivity and mean Doppler velocity profiles. Data presented in this paper were obtained by the coherent dual-polarization C-band radar, Polar 55C, in Rome, Italy, during the summer and the fall of 2004. [ABSTRACT FROM AUTHOR]

The ability of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to simulate midlatitude ice clouds is evaluated. Model outputs are compared to long-term meteorological measurements by active (radar and lidar) and passive (infrared and visible fluxes) remote sensing collected at an atmospheric observatory near Paris, France. The goal is to understand which of four microphysical schemes is best suited to simulate midlatitude ice clouds. The methodology consists of simulating instrument observables from the model outputs without any profile inversion, which allows the authors to use fewer assumptions on microphysical and optical properties of ice particles. Among the four schemes compared in the current study, the best observation-to-simulations scores are obtained with Reisner et al. provided that the particles’ sedimentation velocity from Heymsfield and Donner is used instead of that originally proposed. For this last scheme, the model gives results close to the measurements for clouds with medium optical depth of typically 1 to 3, whatever the season. In this configuration, MM5 simulates the presence of midlatitude ice clouds in more than 65% of the authors’ selection of observed cloud cases. In 35% of the cases, the simulated clouds are too persistent whatever the microphysical scheme and tend to produce too much solid water (ice and snow) and not enough liquid water. [ABSTRACT FROM AUTHOR]

Polarimetric radar measurements are used to retrieve drop size distributions (DSD) in subtropical thunderstorms. Retrievals are made with the single-moment exponential drop size model of Marshall and Palmer driven by radar reflectivity measurements and with a two-parameter constrained-gamma drop size model that utilizes reflectivity and differential reflectivity. Results are compared with disdrometer observations. Retrievals with the constrained-gamma DSD model gave better representation of total drop concentration, liquid water content, and drop median volume diameter and better described their natural variability. The Marshall–Palmer DSD model, with a fixed intercept parameter, tended to underestimate the total drop concentration in storm cores and to overestimate significantly the concentration in stratiform regions. Rainwater contents in strong convection were underestimated by a factor of 2–3, and drop median volume diameters in stratiform rain were underestimated by 0.5 mm. To determine possible DSD model impacts on numerical forecasts, evaporation and accretion rates were computed using Kessler-type parameterizations. Rates based on the Marshall–Palmer DSD model were lower by a factor of 2–3 in strong convection and were higher by about a factor of 2 in stratiform rain than those based on the constrained-gamma model. The study demonstrates the potential of polarimetric radar measurements for improving the understanding of precipitation processes and microphysics parameterization in numerical forecast models. [ABSTRACT FROM AUTHOR]

Ice clouds are an important yet largely unvalidated component of weather forecasting and climate models, but radar offers the potential to provide the necessary data to evaluate them. First in this paper, coordinated aircraft in situ measurements and scans by a 3-GHz radar are presented, demonstrating that, for stratiform midlatitude ice clouds, radar reflectivity in the Rayleigh-scattering regime may be reliably calculated from aircraft size spectra if the “Brown and Francis” mass–size relationship is used. The comparisons spanned radar reflectivity values from -15 to +20 dBZ, ice water contents (IWCs) from 0.01 to 0.4 g m-3, and median volumetric diameters between 0.2 and 3 mm. In mixed-phase conditions the agreement is much poorer because of the higher-density ice particles present. A large midlatitude aircraft dataset is then used to derive expressions that relate radar reflectivity and temperature to ice water content and visible extinction coefficient. The analysis is an advance over previous work in several ways: the retrievals vary smoothly with both input parameters, different relationships are derived for the common radar frequencies of 3, 35, and 94 GHz, and the problem of retrieving the long-term mean and the horizontal variance of ice cloud parameters is considered separately. It is shown that the dependence on temperature arises because of the temperature dependence of the number concentration “intercept parameter” rather than mean particle size. A comparison is presented of ice water content derived from scanning 3-GHz radar with the values held in the Met Office mesoscale forecast model, for eight precipitating cases spanning 39 h over southern England. It is found that the model predicted mean IWC to within 10% of the observations at temperatures between -30° and -10°C but tended to underestimate it by around a factor of 2 at colder temperatures. [ABSTRACT FROM AUTHOR]

A method to estimate ocean wave directional spectra using a high-frequency (HF) ocean radar was developed. The governing equations of wave spectra are integral equations of first- and second-order radar cross sections, the wave energy balance equation, and the continuity equation of surface winds. The parameterization of the source function is the same as that in WAM. Furthermore, the method uses the constraints that wave spectral values are smooth in both wave frequency and direction and that the propagation terms are small. The unknowns to be estimated are surface wind vectors at radial grids whose centers are the radar position, and wave spectral values at radial and wave frequency–direction grids. The governing equations are discretized in the radial and wave frequency–direction grids and are converted into a nonlinear minimization problem. Identical twin experiments showed that the present method can estimate wave spectra and dynamically extrapolate wave spectra, even in an inhomogeneous wave field. [ABSTRACT FROM AUTHOR]

A novel resolution enhancement technique using range oversampling (RETRO) is presented. Oversampled signals are radar returns from shifted and overlapped resolution volumes in range. It has been recently shown that these signals can be whitened and averaged to optimally reduce the statistical error of weather spectral moment estimations for the case of uniform reflectivity and velocity. Using the same oversampled data, when the resolution is of interest, RETRO can reveal the variation of reflectivity and velocity in range at finescale. The idea is to utilize the redundant information contained in oversampled signals, which come from common regions, to improve the resolution defined by the range weighting function. As a result, oversampled data are optimally combined to produce high-resolution signals for spectral moment estimations. RETRO is demonstrated and verified using numerical simulations for two cases. In the first case, range variation of a tornadic vortex with a diameter of 120 m can be reconstructed by RETRO at a scale of 25 m when a 250-m pulse and an oversampling factor of 10 are used. Application of RETRO to mitigate ground clutter contamination is demonstrated in the second case. [ABSTRACT FROM AUTHOR]

The combination of radar and lidar in space offers the unique potential to retrieve vertical profiles of ice water content and particle size globally, and two algorithms developed recently claim to have overcome the principal difficulty with this approach—that of correcting the lidar signal for extinction. In this paper “blind tests” of these algorithms are carried out, using realistic 94-GHz radar and 355-nm lidar backscatter profiles simulated from aircraft-measured size spectra, and including the effects of molecular scattering, multiple scattering, and instrument noise. Radiation calculations are performed on the true and retrieved microphysical profiles to estimate the accuracy with which radiative flux profiles could be inferred remotely. It is found that the visible extinction profile can be retrieved independent of assumptions on the nature of the size distribution, the habit of the particles, the mean extinction-to-backscatter ratio, or errors in instrument calibration. Local errors in retrieved extinction can occur in proportion to local fluctuations in the extinction-to-backscatter ratio, but down to 400 m above the height of the lowest lidar return, optical depth is typically retrieved to better than 0.2. Retrieval uncertainties are greater at the far end of the profile, and errors in total optical depth can exceed 1, which changes the shortwave radiative effect of the cloud by around 20%. Longwave fluxes are much less sensitive to errors in total optical depth, and may generally be calculated to better than 2 W m-2 throughout the profile. It is important for retrieval algorithms to account for the effects of lidar multiple scattering, because if this is neglected, then optical depth is underestimated by approximately 35%, resulting in cloud radiative effects being underestimated by around 30% in the shortwave and 15% in the longwave. Unlike the extinction coefficient, the inferred ice water content and particle size can vary by 30%, depending on the assumed mass–size relationship (a problem common to all remote retrieval algorithms). However, radiative fluxes are almost completely determined by the extinction profile, and if this is correct, then errors in these other parameters have only a small effect in the shortwave (around 6%, compared to that of clear sky) and a negligible effect in the longwave. [ABSTRACT FROM AUTHOR]

The Keys Area Microphysics Project (KAMP), conducted as part of NASA’s Fourth Convective and Moisture Experiment (CAMEX-4) in the lower Keys area, deployed a number of ground radars and four arrays of rain gauge and disdrometer clusters. Among the various instruments is an X-band dual-polarization Doppler radar on wheels (XPOL), contributed by the University of Connecticut. XPOL was used to retrieve rainfall rate and raindrop size distribution (DSD) parameters to be used in support of KAMP science objectives. This paper presents the XPOL measurements in KAMP and the algorithm developed for attenuation correction and estimation of DSD model parameters. XPOL observations include the horizontal polarization reflectivity ZH, differential reflectivity ZDR, and differential phase shift ΦDP. Here, ZH and ZDR were determined to be positively biased by 3 and 0.3 dB, respectively. A technique was also applied to filter noise and correct for potential phase folding in ΦDP profiles. The XPOL attenuation correction uses parameterizations that relate the path-integrated specific (differential) attenuation along a radar ray to the filtered-ΦDP (specific attenuation) profile. Attenuation-corrected ZH and specific differential phase shift (derived from filtered ΦDP profiles) data are then used to derive two parameters of the normalized gamma DSD model, that is, intercept (Nw) and mean drop diameter (D0). The third parameter (shape parameter μ) is calculated using a constrained μ–Λ relationship derived from the measured raindrop spectra. The XPOL attenuation correction is evaluated using coincidental nonattenuated reflectivity fields from the Key West Weather Surveillance Radar-1988 Doppler (WSR-88D), while the DSD parameter retrievals are statistically assessed using DSD parameters calculated from the measured raindrop spectra. Statistics show that XPOL DSD parameter estimation is consistent with independent observations. XPOL estimates of water content and Nw are also shown to be consistent with corresponding retrievals from matched ER-2 Doppler radar (EDOP) profiling observations from the 19 September airborne campaign. Results shown in this paper strengthen the applicability of X-band dual-polarization high resolution observations in cloud modeling and precipitation remote sensing studies. [ABSTRACT FROM AUTHOR]

The threat of flooding from landfalling tropical cyclones is a function of the local variation in rain rate and rain accumulation. To date, these have been inferred from single-frequency radar reflectivity measurements. However, the Tropical Rainfall Measuring Mission experience has confirmed that one of the main difficulties in retrieving rain profiles using a single-frequency radar is the unknown raindrop size distribution (DSD). A dual-frequency radar such as the one planned for the upcoming Global Precipitation Measurement (GPM) core satellite is expected to help sort out at least part of this DSD-induced ambiguity. However, the signature of precipitation at 14 GHz does not differ greatly from its signature at 35 GHz (the GPM radar frequencies). To determine the extent of the vertical variability of the DSD in tropical systems and to quantify the effectiveness of a dual-frequency radar in resolving this ambiguity, several different models of DSD shape are considered and used to estimate the rain-rate and mean-diameter profiles from the measurements made by Jet Propulsion Laboratory’s (JPL’s) airborne second generation precipitation radar (PR-2) over Hurricanes Gabrielle and Humberto during the Fourth Convection and Moisture Experiment (CAMEX-4) in September 2001. It turns out that the vertical structures of the rain profiles retrieved from the same measurements under different DSD assumptions are similar, but the profiles themselves are quantitatively significantly different. [ABSTRACT FROM AUTHOR]

Intense tropical cyclones often possess relatively little convection around their cores. In radar composites, this surrounding region is usually echo-free or contains light stratiform precipitation. While subsidence is typically quite pronounced in this region, it is not the only mechanism suppressing convection. Another possible mechanism leading to weak-echo moats is presented in this paper. The basic idea is that the strain-dominated flow surrounding an intense vortex core creates an unfavorable environment for sustained deep, moist convection. Strain-dominated regions of a tropical cyclone can be distinguished from rotation-dominated regions by the sign of S21 + S22 - ζ2, where S1 = ux - υy and S2 = υx + uy are the rates of strain and ζ = υx - uy is the relative vorticity. Within the radius of maximum tangential wind, the flow tends to be rotation-dominated (ζ2 > S21 + S22), so that coherent structures, such as mesovortices, can survive for long periods of time. Outside the radius of maximum tangential wind, the flow tends to be strain-dominated (S21 + S22 > ζ2), resulting in filaments of anomalous vorticity. In the regions of strain-dominated flow the filamentation time is defined as τfil = 2(S21 + S22 - ζ2)-1/2. In a tropical cyclone, an approximately 30-km-wide annular region can exist just outside the radius of maximum tangential wind, where τfil is less than 30 min and even as small as 5 min. This region is defined as the rapid filamentation zone. Since the time scale for deep moist convective overturning is approximately 30 min, deep convection can be significantly distorted and even suppressed in the rapid filamentation zone. A nondivergent barotropic model illustrates the effects of rapid filamentation zones in category 1–5 hurricanes and demonstrates the evolution of such zones during binary vortex interaction and mesovortex formation from a thin annular ring of enhanced vorticity. [ABSTRACT FROM AUTHOR]

Fine-resolution simulations of Hurricane Erin are conducted using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to investigate roles of thermodynamic, boundary layer, and microphysical processes on Erin’s structure and evolution. Choice of boundary layer scheme has the biggest impact on simulations, with the minimum surface pressure (Pmin) averaged over the last 18 h (when Erin is relatively mature) varying by over 20 hPa. Over the same period, coefficients used to describe graupel fall speeds (Vg) affect Pmin by up to 7 hPa, almost equivalent to the maximum 9-hPa difference between microphysical parameterization schemes; faster Vg and schemes with more hydrometeor categories generally give lower Pmin. Compared to radar reflectivity factor (Z) observed by the NOAA P-3 lower fuselage radar and the NASA ER-2 Doppler radar (EDOP) in Erin, all simulations overpredict the normalized frequency of occurrence of Z larger than 40 dBZ and underpredict that between 20 and 40 dBZ near the surface; simulations overpredict Z larger than 25 to 30 dBZ and underpredict that between 15 and 25 or 30 dBZ near the melting layer, the upper limit depending on altitude. Brightness temperatures (Tb) computed from modeled fields at 37.1- and 85.5-GHz channels that respond to scattering by graupel-size ice show enhanced scattering, mainly due to graupel, compared to observations. Simulated graupel mixing ratios are about 10 times larger than values observed in other hurricanes. For the control run at 6.5 km averaged over the last 18 simulated hours, Doppler velocities computed from modeled fields (Vdop) greater than 5 m s-1 make up 12% of Erin’s simulated area for the base simulation but less than 2% of the observed area. In the eyewall, 5% of model updrafts above 9 km are stronger than 10 m s-1, whereas statistics from other hurricanes show that 5% of updrafts are stronger than only 5 m s-1. Variations in distributions of Z, vertical motion, and graupel mixing ratios between schemes are not sufficient to explain systematic offsets between observations and models. A new iterative condensation scheme, used with the Reisner mixed-phase microphysics scheme, limits unphysical increases of equivalent potential temperature associated with many condensation schemes and reduces the frequency of Z larger than 50 dBZ, but has minimal effect on Z below 50 dBZ, which represent 95% of the modeled hurricane rain area. However, the new scheme changes the Erin simulations in that 95% of the updrafts are weaker than 5 m s-1 and Pmin is up to 12 hPa higher over the last 18 simulated hours. [ABSTRACT FROM AUTHOR]

A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the sample of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation. [ABSTRACT FROM AUTHOR]

A modified dual-Doppler analysis technique for use with airborne Doppler radars utilizing two fixed beams is presented. Although the data collected by such a system would ideally lie in the plane defined by the radar beam orientations and the aircraft velocity vector, variations in the aircraft attitude and drift angles lead to displacements between the radar observations and the idealized observation plane. These variations motivated the development of a formal framework in which an a priori velocity estimate is used in conjunction with the two Doppler velocity measurements to form a three-dimensional velocity estimate. Two velocity components, lying within or close to the observation plane, and therefore containing only a small contribution from the a priori velocity estimate, are then extracted from the three-dimensional velocity estimate. Advantages of using the three-dimensional framework include improved accuracy (when an a priori velocity estimate is available) and a framework for assessing the effects of cross-plane contamination on the retrieved velocity components. The velocity fields retrieved using the modified dual-Doppler analysis are affected by errors in the platform motion correction to the Doppler velocities, random noise in the mean Doppler velocity estimates, displacements between the radar beams (and between the radar beams and the idealized observation plane), and meteorological velocity variations about the a priori velocity estimate. Errors in the platform motion correction remain poorly characterized but are believed to be the largest source of error in many cases. However, these errors result primarily in biases (or low-frequency errors) in the retrieved velocity fields and therefore do not interfere with the ability to resolve actual velocity variations. Random noise in the mean Doppler velocity estimates increases dramatically with decreasing signal-to-noise ratio (SNR) (for SNR < 5 dB) and effectively limits the use of the single-plane dual-Doppler (SPDD) analysis to SNR > 0 dB. Displacements between the volumes sampled by the nadir and slanted beams can also be a significant source of error, especially at larger displacements from the aircraft. Errors resulting from meteorological velocity variations about the a priori velocity estimate tend to be small compared to the velocity variations of interest. The dual-Doppler analysis presented in this paper has been applied to retrieve two-dimensional velocity fields with a resolution of ∼50 m using Doppler velocities collected using dual-beam configurations of the Wyoming Cloud Radar. Results are in horizontal and vertical planes for marine stratocumulus, cumulus congestus, and for the clear-air boundary layer. [ABSTRACT FROM AUTHOR]

The dryline has long been associated with the development of severe thunderstorms in the southern plains during the spring and early summer months. The propagation and structure of the dryline are closely tied to surface processes that are neither well understood nor well resolved with current observational capabilities. As a result, there are often large errors in forecasts of dryline position and structure. Improvements in radar technology have allowed for better observations of the dryline in recent years. Here, very finescale radar observations taken with the University of Massachusetts—Amherst (UMass) mobile W-band radar during an International H2O Project (IHOP) double-dryline event on 22 May 2002 in the Oklahoma panhandle are presented. The observations are placed in the context of the dryline secondary circulation, which describes flow in a plane normal to the dryline. The narrow, half-power beamwidth of the antenna on the W band (0.18°) permitted the measurements of channels of upward (8–9 m s-1 over a horizontal distance of 50–100 m) and downward vertical velocity, greater in absolute magnitude than that previously reported in dryline field studies. A ground-based variational pseudo-multiple-Doppler processing technique is introduced, which is used to decompose time series of RHI velocity data into horizontal and vertical wind components. The technique is applied to a retrograding dryline from 22 May 2002. Finescale structure of the retreating dryline interface is presented. [ABSTRACT FROM AUTHOR]

In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km-1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars. [ABSTRACT FROM AUTHOR]

The vertical distribution of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR)-observed precipitation reflectivity and their mirror image (MI) reflectivity are outlined in this paper. The purpose of this study is to investigate the possibility and the limitation of the MI method, which can be used to estimate rain attenuation. Because the MI returns are attenuated much more greatly than the direct image returns, and also because the MI return is affected by the surface reflection and surface scattering, the MI returns are much smaller and more complex than the direct image (DI) returns. However, because the MI returns might be contaminated by the surface or contributed by bistatic scattering near the surface, there are many strong mirror returns between the surface and below surface at 1 km. The ratio of detectable MI return pixels to detected DI return pixels depends on rain rate, target height, and storm height. In addition, differences also exist between the convective and stratiform rain. The reason for this might be because the difference of the surface cross section and the difference of the storm height between the two types of rainfall. Furthermore, the direct and the mirror returns for a 35-GHz radar are also estimated. The virtue of the MI method of the Ka-band radar may reside in expanding the dynamic range of the MI method from 4–30 to 0.6–30 mm h-1. [ABSTRACT FROM AUTHOR]

The features of near-nadir probing of the ocean surface from a satellite are investigated, and a new configuration of a microwave radar for the surface slopes measurement is proposed. Numerical modeling is used to examine the feasibility of a spaceborne implementation, to devise a measuring technique, and to develop a data processing algorithm. It is shown that the radar with a knifelike beam (1° × 25°–30°) can be used to measure the variance of surface wave slopes. Using range selection and a new processing procedure to synthesize data, it is possible to achieve the spatial resolution required to study wave processes on the ocean surface from satellites. A new approach to wind speed retrieval at and near nadir is also discussed. [ABSTRACT FROM AUTHOR]

In this two-part paper, a correction for rain attenuation of radar reflectivity (ZH) and differential reflectivity (ZDR) at the X-band wavelength is presented. The correction algorithm that is used is based on the self-consistent method with constraints proposed by Bringi et al., which was originally developed and evaluated for C-band polarimetric radar data. The self-consistent method is modified for the X-band frequency and is applied to radar measurements made with the multiparameter radar at the X-band wavelength (MP-X) operated by the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan. In this paper, characteristic properties of relations among polarimetric variables, such as AH–KDP, ADP–AH, AH–ZH, and ZDR–ZH, that are required in the correction methodology are presented for the frequency of the MP-X radar (9.375 GHz), based on scattering simulations using drop spectra measured by disdrometers at the surface. The scattering simulations were performed under conditions of three different temperatures and three different relations for drop shapes, in order to consider variability of polarimetric variables for these conditions. For the X-band wavelength, the AH–KDP and ADP–AH relations can be assumed to be nearly linear. The coefficient α of the AH–KDP relation varies over a wide range from 0.139 to 0.335 dB (°)-1 with a mean value of 0.254 dB (°)-1. The coefficient γ of the ADP–AH relation varies from 0.114 to 0.174, with a mean value of 0.139. The exponent b of the AH–ZH relation does not depend on drop shapes and is almost constant for a given temperature (about 0.78 at the temperature of 15°C). The ZDR–ZH relation depends primarily on drop shape, and does not vary with temperature. [ABSTRACT FROM AUTHOR]

Observations from the East Pacific Investigation of Climate (EPIC) 2001 field campaign are well suited for exploring the relationships among the diurnal cycle, mesoscale (10–100 km) structure, and precipitation in the stratocumulus region in the southeast Pacific. Meteorological time series and observations from a scanning C-band radar, vertically pointing cloud radar, and ceilometer, as well as satellite data, are used to show that drizzle is associated with increased variability in cloud and boundary layer properties compared to nondrizzling periods. The stratocumulus-topped boundary layer is typically well mixed at night, transitioning to less well mixed in the afternoon, with drizzle most frequently occurring in the early morning. Coherent patches of drizzle, or “cells,” can have large areas with radar reflectivities of greater than 5 dBZ of up to about 100 km2. Individual cells have long lifetimes, up to 2 h, and appear to be replenished by moisture in the boundary layer. [ABSTRACT FROM AUTHOR]

A model of the melting snow and its radar reflectivity is presented here. The main addition to previous description of the melting layer is the explicit introduction of snow density as a variable. The model is validated with radar observations. Differences in brightband intensity for comparable precipitation rates are related here to the coexistence of supercooled cloud water (SCW) with snow above the melting level leading to riming and change in snow density. Cases where riming was suspected were selected according to the characteristics of the vertical profile of reflectivity flux above the melting layer and vertical Doppler velocities faster than expected from low-density snow. For stratiform precipitation with a melting layer, high snow-to-rain velocity ratio indicates high-density snow and consequently a small peak-to-rain reflectivity difference is expected. This relationship was computed from the model and confirmed with vertically pointing radar observations. In spite of the complexity of the physical processes present in the melting layer the model appears to capture the essential elements. [ABSTRACT FROM AUTHOR]

Based on the measurement principles used on incoherent scatter radars, the authors have developed the Simultaneous Multiple Pulse Repetition Frequency (SMPRF) code that is intended to solve the range–Doppler dilemma and that can be used with modern magnetron radars. The working principle of the code is explained in mathematical terms and with the help of a simplified model. Results from the SMPRF and traditional fixed PRF weather radar measurements are compared, and the reasons for the differences are explained. The practical results show that the SMPRF code seems to work in the manner that is predicted by the theoretical and model calculations. The SMPRF code provides enough information to produce a high-resolution measured spectrum for each range gate. The shape of these measured spectra are seldom purely Gaussian. It is possible that more advanced raw products, other than just reflectivity, velocity, and width, can be produced with the help of these high-resolution spectra. [ABSTRACT FROM AUTHOR]