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A validation of passive microwave–adjusted rainfall analyses of tropical cyclones using spaceborne radar data is presented. This effort is part of the one-dimensional plus four-dimensional variational (1D+4D-Var) rain assimilation project that is being carried out at the European Centre for Medium-Range Weather Forecasts (ECMWF). Brightness temperatures or surface rain rates from the Tropical Rainfall Measuring Mission (TRMM) satellite are processed through a 1D-Var retrieval to derive values of total column water vapor that can be ingested into the operational ECMWF 4D-Var. As an indirect validation, the precipitation fields produced at the end of the 1D-Var minimization process are converted into equivalent radar reflectivity at the frequency of the TRMM precipitation radar (13.8 GHz) and are compared with the observations averaged at model resolution. The averaging process is validated using a sophisticated downscaling/upscaling approach that is based on wavelet decomposition. The precipitation radar measurements are ideal for this validation exercise, being approximately collocated with but completely independent of the TRMM Microwave Imager (TMI) radiometer measurements. Qualitative and statistical comparisons between radar observations and retrievals from the TMI-derived surface rain rates and from TMI radiances are made using 17 well-documented tropical cyclone occurrences between January and April of 2003. Several statistical measures, such as bias, root-mean-square error, and Heidke skill score, are introduced to assess the 1D-Var skill as well as the model background skill in producing a realistic rain distribution. Results show a good degree of skill in the retrievals, especially near the surface and for medium–heavy rain. The model background produces precipitation in the domain that is sometimes in excess with respect to the observations, and it often shows an error in the location of precipitation maxima. Differences between the two 1D-Var approaches are not large enough to make final conclusions regarding the advantages of one method over the other. Both methods are capable of redistributing the rain patterns according to the observations. It appears, however, that the brightness temperature approach is in general more effective in increasing precipitation amounts at moderate-to-high rainfall rates. [ABSTRACT FROM AUTHOR]

Airborne radar reflectivity measurements at frequencies of 9.6 and 94 GHz, with collocated, in situ particle size distribution and ice water content measurements from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) in Florida in July 2002, offer one of the first opportunities to evaluate and improve algorithms for retrieving ice water content from single-wavelength spaceborne radar measurements. Both ice water content and radar reflectivity depend on the distribution of particle mass with size. It is demonstrated that single, power-law, mass dimensional relationships are unable to adequately account for the dominating contribution of small particles at lower reflectivities and large particles at higher reflectivities. To circumvent the need for multiple, or complex, mass dimensional relationships, analytic expressions that use particle ensemble mean ice particle densities that are derived from the coincident microphysical and radar observations are developed. These expressions, together with more than 5000 CRYSTAL FACE size distributions, are used to develop radar reflectivity–ice water content relationships for the two radar wavelengths that appear to provide improvements over earlier relationships, at least for convectively generated stratiform ice clouds. [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]

The retrieval errors of cloud and precipitation hydrometeor contents from spaceborne observations are estimated at microwave frequencies in atmospheric windows between 18 and 150 GHz and in oxygen absorption complexes near 50–60 and 118 GHz. The method is based on a variational retrieval framework using a priori information on the cloud, atmosphere, and surface states from ECMWF short-range forecasts under different weather regimes. This approach was chosen because a consistent description of the model state and its uncertainties is provided, which is unavailable for other methods. The results show that the sounding channels provide more stable, more accurate, and less biased retrievals than window channels—in particular, over land surfaces and with regard to snowfall. Average performance estimates showed that if sounding channels are used, 80% of all retrievals are within 100% error limits and 60% of them are within 50% error limits with regard to rainfall. For snowfall, the sounding channels produce 60% of all retrievals with errors below 100% for rates smaller than 1 mm h-1, and 50%–80% of the cases have errors below 50% for more intense snowfall. [ABSTRACT FROM AUTHOR]

Errors in surface rainfall estimates that are caused by ignoring the vertical profile of reflectivity (VPR) and range effects have been assessed by simulating how fine-resolution 3D reflectivity measurements at close ranges are sampled by the radar at various ranges and heights. Uncorrected and corrected accumulations from 33 events of mainly stratiform precipitation, with a recognizable melting layer for over 250 h, have been generated using two basic procedures: (a) the “near range” or “inner” VPR and (b) the intensity-dependent “climatological” VPR. The root-mean-square (rms) error structure has been derived as a function of height and range, for accumulations ranging from 5 min to 2 h, for various brightband heights and verification areas. However, it is the errors along the lowest default height that are most relevant. The stratification of the results by the height of the bright band is essential to understand the influence of the bright band with range. The largest errors (>100% at near ranges without correction) are encountered with lower and stronger bright bands. After correction, errors of less than 20% can be achieved with method “a” but only over large verification areas (>100 km2), with long accumulation intervals (>45 min), with bright bands that are relatively high (>2.5 km), and for ranges within ∼130 km. The climatological correction yields errors that are roughly 2 times as large. The results with the inner VPR method can only be obtained by assuming conditions of spatial homogeneity in the VPR structure of the rainfall fields. Simulations of the VPR variability have indicated that larger errors are to be expected in real-time operations, particularly when measurements are made inside the bright band. The magnitude of these errors may approach those of a “realistic climatological” correction that incorporates some uncertainty in the brightband height. [ABSTRACT FROM AUTHOR]

Polarimetric radar data have been used to produce microphysical classifications. This kind of analysis is run in a real-time mode from several research radars, including the C-band polarimetric (C-Pol) radar in Darwin, Australia. However, these classifications have had very little systematic evaluation with independent data. Using surface data is often difficult because of sampling issues, particularly for hail. The approach taken here is to use a combination of 50- and 920-MHz wind profiler estimates of rain and hail to provide validation data for the radar pixels over the profiler. The profilers also observe signals associated with lightning, and some comparisons are made between lightning occurrence and the radar measurements of graupel. The retrievals of hail–rain mixtures are remarkably robust; there are some issues regarding other microphysical classes, however, including difficulties in detecting melting snow layers in stratiform rain. These difficulties are largely due to the resampling of the radar volume data onto a grid and to poor separation of the snow classes. [ABSTRACT FROM AUTHOR]

A systematic and intensive analysis is performed on 5 yr of reliable disdrometric data (over 20 000 one-minute drop size distributions, DSDs) to investigate the variability of DSDs in the Montreal, Quebec, Canada, area. The scale dependence (climatological scale, day to day, within a day, between physical processes, and within a physical process) of the DSD variability and its effect on rainfall intensity R estimation from radar reflectivity Z are explored in terms of bias and random errors. Detail error distributions are also provided. The use of a climatological R–Z relationship for rainfall—affected by all of the DSDs’ variability—leads on average to a random error of 41% in instantaneous rain-rate estimation. This error decreases with integration time, but the decrease becomes less pronounced for integration times longer than 2 h. Daily accumulations computed with the climatological R–Z relationship have a bias of 28% because of the day-to-day DSD variability. However, when daily R–Z relationships are used, a random error of 32% in instantaneous rain rate is still present because of the DSD variability within a day. This illustrates that most of the variability of DSDs has its origin within a storm or between storms within a day. Physical processes leading to the formation of DSDs are then classified according to the vertical structure of radar data as measured by a UHF profiler collocated with the disdrometer. The DSD variability among different physical processes is larger than the day-to-day variability. A bias of 41% in rain accumulations is due to the DSD variability between physical processes. Accurate rain-rate estimation (∼7%) can be achieved only after the proper underlying physical process is identified and the associated R–Z relationship is used. [ABSTRACT FROM AUTHOR]

A simple empirical procedure for determining freezing levels with polarimetric radar measurements is described. The algorithm takes advantage of the strong melting-layer signatures and the redundancy provided by the suite of polarimetric radar measurements—in particular, radar reflectivity, linear depolarization ratio, and cross-correlation coefficient. Freezing-level designations can be made with all volumetric scanning strategies. Application to uniform (stratiform) precipitation within 60 km of the radar and with brightband reflectivity maxima of greater than 25 dBZ suggests an accuracy of 100–200 m. [ABSTRACT FROM AUTHOR]

High-resolution airborne measurements of vertical incidence radar reflectivity and Doppler velocity, as well as coincident upwelling 85-GHz radiances, are analyzed for several Atlantic Ocean hurricanes and for numerous convection-generated systems in Florida and Amazonia. Characteristic reflectivity, hydrometeor motion, and vertical air motion profiles of convective and stratiform precipitation are compared and related to their ice-scattering signature, with an emphasis on the difference between hurricanes and convection-generated storms. Hurricanes are found to be largely and clearly stratiform, displaying a remarkably narrow echo and vertical velocity spectrum. Air currents are inferred to be rising steadily at all levels, even in stratiform regions. Land-based, convection-generated stratiform regions tend to experience low-level descent and mid- to upper-level ascent, although the vertical velocity variability is large. Florida storms produce little stratiform precipitation. Their spectrum of echo and updraft strengths is broad, including some of the highest reflectivities aloft, resulting in very low 85-GHz radiances. Amazonian storms are relatively weak and are more “maritime” in echo, vertical velocity, and ice-scattering characteristics, when compared with those in Florida, especially during a westerly low-level wind regime. [ABSTRACT FROM AUTHOR]

Airborne measurements of vertical incidence radar reflectivity and radial velocity are analyzed for some 21 231 km of high-altitude flight tracks over tropical precipitation systems, in order to describe their characteristic vertical structure. The strength of the radar dataset lies in its superb vertical resolution, sufficient to detect unambiguously a bright band and the coincident Doppler velocity change, which identify the melting layer in stratiform precipitation. In this first of a two-part study, a technique based on the detection of this stratiform precipitation signature is developed to classify hydrometer profiles as convective, stratiform, or shallow. Even though the profiles are classified individually, stratiform and convective regions emerge, whose characteristics are described. The hydrometeor vertical velocity variability is smaller in stratiform profiles, which is consistent with the physical concept of a stratiform region. The purpose of the classification is to describe, in, the composite vertical structure of the various rain types in hurricanes, as well as in isolated to organized precipitating convection sampled in Florida and Brazil. [ABSTRACT FROM AUTHOR]

A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm. [ABSTRACT FROM AUTHOR]

An algorithm, the spectral latent heating (SLH) algorithm, has been developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission precipitation radar with a cloud-resolving model (CRM). Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were produced with numerical simulations of tropical cloud systems in the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. For convective and shallow stratiform regions, the lookup table refers to the precipitation-top height (PTH). For the anvil region, on the other hand, the lookup table refers to the precipitation rate at the melting level instead of PTH. A consistency check of the SLH algorithm was also done with the CRM-simulated outputs. The first advantage of this algorithm is that differences of heating profiles between the shallow convective stage and the deep convective stage can be retrieved. This is a result of the utilization of observed information, not only on precipitation type and intensity, but also on the precipitation depth. The second advantage is that heating profiles in the decaying stage with no surface rain can also be retrieved. This comes from utilization of the precipitation rate at the melting level for anvil regions. [ABSTRACT FROM AUTHOR]

The Kwajalein, Marshall Islands, Tropical Rainfall Measuring Mission (TRMM) ground validation radar has provided a multiyear three-dimensional radar dataset at an oceanic site. Extensive rain gauge networks are not feasible over the ocean and, hence, are not available to aid in calibrating the radar or determining a conversion from reflectivity to rain rate. This paper describes methods used to ensure the calibration and allow the computation of quantitative rain maps from the radar data without the aid of rain gauges. Calibration adjustments are made by comparison with the TRMM satelliteborne precipitation radar. The additional steps required to convert the calibrated reflectivity to rain maps are the following: correction for the vertical profile of reflectivity below the lowest elevation angle using climatological convective and stratiform reflectivity profiles; conversion of reflectivity (Z) to rain rate (R) with a relationship based on disdrometer data collected at Kwajalein, and a gap-filling estimate. The time series of rain maps computed by these procedures include low, best, and high estimates to frame the estimated overall uncertainty in the radar rain estimation. The greatest uncertainty of the rain maps lies in the calibration of the radar (±30%). The estimation of the low-altitude vertical profile of reflectivity is also a major uncertainty (±15%). The Z–R and data-gap uncertainties are relatively minor (±5% or less). These uncertainties help to prioritize the issues that need to be addressed to improve quantitative rainfall mapping over the ocean and provide useful bounds when comparing radar-derived rain estimates with other remotely sensed measures of oceanic rain (such as from satellite passive microwave sensors). [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the ability of radar-derived rainfall products to characterize the small-scale spatial variability of rainfall. The authors use independent datasets from high-quality dense rain gauge networks employed during the Texas and Florida Underflights (TEFLUN-B) and Tropical Rainfall Measuring Mission component of the Large-Scale Biosphere–Atmosphere (TRMM-LBA) field experiments conducted by NASA in 1998 and 1999. A detailed comparison between gauge- and radar-derived spatial variability estimates is carried out by means of a correlation function, covariance, variogram, scaling characteristics, and variance reduction due to spatial averaging. Emphasis is given to the correlation function because it is involved in most of these statistics. The approach followed in the analysis addresses the problems associated with the traditional estimation methods and the recognized differences in the scales of observation. The performance of the radar-derived correlation function is evaluated in two ways: by direct comparison with gauge-derived correlation function and by quantifying its effect on one of the applications, that is, gauge sampling uncertainty estimation. Results show that, at separation distances shorter than about 5 km, radar-derived correlations are lower than those obtained from gauges. Three sources of uncertainty that may have caused the discrepancy between gauge- and radar-derived correlations are identified, and their effects are quantified to the extent possible. The error introduced in gauge sampling uncertainty estimates due to the use of radar-derived correlation function is within 30%. Discrepancies between gauge- and radar-rainfall fields are also observed in terms of the other spatial statistics. [ABSTRACT FROM AUTHOR]

An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Climatology Centre (GPCC) global rain gauge analyses and 2) by comparing the rainfall estimates from the PR and TMI on a rainfall feature-by-feature basis within the narrow swath of the PR using a 1-yr database of classified precipitation features (PFs). The former is done to evaluate the overall biases of the TMI and PR relative to “ground truth” to examine regional differences in the estimates; the latter allows a direct comparison of the estimates with the same sampling area, also identifying relative biases as a function of storm type. This study finds that the TMI overestimates rainfall in most of the deep Tropics and midlatitude warm seasons over land with respect to both the GPCC gauge analysis and the PR (which agrees well with the GPCC gauges in the deep Tropics globally), in agreement with past results. The PR is generally higher than the TMI in midlatitude cold seasons over land areas with gauges. The analysis by feature type reveals that the TMI overestimates relative to the PR are due to overestimates in mesoscale convective systems and in most features with 85-GHz polarization-corrected temperature of less than 250 K (i.e., with a significant optical depth of precipitation ice). The PR tended to be higher in PFs without an ice-scattering signature of less than 250 K. Normalized for a subset of features with a large rain volume (exceeding 104 mm h-1 km2) independent of the PF classification, features with TMI > PR in the Tropics tended to have a higher fraction of stratiform rainfall, higher IR cloud tops, more intense radar profiles and 85-GHz ice-scattering signatures, and larger rain areas, whereas the converse is generally true for features with PR > TMI. Subtropical-area PF bias characteristics tended not to have such a clear relationship (especially over the ocean), a result that is hypothesized to be due to the influence of more variable storm environments and the presence of frontal rain. Melting-layer effects in stratiform rain and a bias in the ice-scattering–rain relationship were linked to the TMI producing more rainfall than the PR. However, noting the distinct characteristic biases Tropics-wide by feature type, this study reveals that accounting for regime-dependent biases caused by the differing horizontal and vertical morphologies of precipitating systems may lead to a reduction in systematic relative biases in a microwave precipitation algorithm. [ABSTRACT FROM AUTHOR]

The performance of radar reflectivity (Z[sub e] )–based relations for retrievals of marine stratiform cloud liquid water content (LWC) is evaluated by comparing liquid water path (LWP) estimates from microwave radiometers with vertically integrated LWC values retrieved from radar measurements. Based on a measurement dataset from a research vessel in the tropical eastern Pacific Ocean, it is shown that reflectivity thresholding allows minimizing of the influence of drizzle drops present in marine stratiform clouds to the extent that LWP estimates from a ground-/shipborne radar can have uncertainties that might be acceptable for different applications. The accuracies of Z[sub e] -based retrievals depend on the thresholding level Z[sub et] , and they are generally better than a factor of 2 for Z[sub et] ≲ -15 dBZ. These accuracies typically improve when Z[sub et] is lowered; however, the amount of cloud profiles that pass thresholding diminishes as Z[sub et] is decreased from about 50% for a -15-dbZ threshold to only about 10% for a -25-dBZ threshold. Different thresholding strategies are considered. Ancillary information on cloud-base heights can improve LWP estimates from reflectivities. The ship-based dataset was used to simulate measurements from prospective 94-GHz spaceborne cloud radar (CloudSat). CloudSat measurements would, on average, detect about 75% of warm marine stratiform clouds, though many clouds with negligible presence of drizzle will be missed. Because of sensitivity and resolution issues for the spaceborne radar, reflectivity-based estimates of LWP are generally biased toward high values and have higher uncertainties when compared with the ground-based radar, for the same Z[sub et] . [ABSTRACT FROM AUTHOR]

Filtering of nonpredictable scales of precipitation can be used to improve forecast precision (rms). Previous papers have studied the scale dependence of predictability of patterns of instantaneous rainfall rate and of probabilistic forecasts. In this paper, motivated by the often localized, intermittent nature of rainfall, the wavelet transform is used to develop measures of predictability at each scale. These measures are then used to design optimal forecast filters. This method is applied to radar composites of rainfall reflectivity over much of the continental United States and is developed to be appropriate for operational forecasts of rainfall rates and raining areas. For the four precipitation events studied, the average correlation at 4-h lead time was increased from 0.50 for the original nowcasts to 0.62 with forecast filtering. This forecast filtering is incorporated into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), which now includes variational echo tracking, a semi-Lagrangian advection scheme, scale-based filtering, and appropriate rescaling of the filtered nowcast fields. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) retrieval from remote radar measurements or from in situ disdrometer measurements is an important area of research. If the shape (μ) and slope (Λ) of a three-parameter gamma distribution n(D) = N[sub 0] D[sup μ] exp(-ΛD) are related to one another, as recent disdrometer measurements suggest, the gamma DSD model is simplified to a two-parameter DSD, that is, a constrained gamma DSD. An empirical relation between the μ and Λ was derived using moments estimated from video-disdrometer measurements. Here, the effects of DSD truncation on a μ and Λ relation were analyzed. It was shown that characteristic size and variance of size of a constrained gamma DSD depend only on the shape parameter μ. Assuming that a constrained gamma DSD is valid, S-band polarimetric radar–based estimators for rain rate, median volume diameter, specific propagation phase, attenuation, and differential attenuation were derived. The radar-based estimators were used to obtain the spatial distribution of DSD parameters corresponding to a range–height indicator of radar measurements. Self-consistency among polarization radar measurements is used to indirectly verify constrained gamma DSD-based polarization radar estimators. [ABSTRACT FROM AUTHOR]

A method for the objective evaluation of short-term, nonrandomized operational convective cloud-seeding projects on a floating-target-area basis has been developed and tested in the context of the operational cloud-seeding projects of Texas. The computer-based method makes use of the Next-Generation Radar (NEXRAD) mosaic radar data to define fields of circular (25-km radius) floating-target analysis units with lifetimes from the first echo to the disappearance of all echoes and then superimposes the track and seeding actions of the project seeder aircraft onto the unit fields to define seeded (S) and nonseeded (NS) analysis units. Objective criteria (quantified herein) are used to identify “control” (C) matches for each of the seed units from the archive of NS units. To minimize potential contamination by seeding, no matching is allowed for any control unit if its perimeter came within 25 km of the perimeter of a seed unit during its lifetime. The methodology was used to evaluate seeding effects in the High Plains Underground Water Conservation District (HP) and Edwards Aquifer Authority (EA) programs during the 1999, 2000, and 2001 (EA only) seasons. Objective unit matches were selected from within and outside each operational target within 12, 6, 3, and 2 h of the time on a given day that seeding of a particular unit took place. These were done to determine whether selection biases and the diurnal convective cycle confounded the results. Matches were also drawn from within and outside each target using the entire archive of days on which seeding was done. Although the results of all analyses are subjected to statistical testing, the resulting probability (P) values were used solely to determine the relative strength of the various findings. In the absence of treatment, randomization P values cannot be used as proof of seeding efficacy. The apparent effect of seeding in both programs was large—even after determining the effect of selection biases and the diurnal convective cycle. The most conservative and credible estimates of seeding effects were obtained from control matches drawn from outside the operational target within 2 h of the time that each unit was seeded initially. Under these circumstances, the percentage increase exceeds 50% and the volumetric increment was greater than 3000 acre-feet (3700 kt) per unit with strong P-value support (i.e., <0.0001) in both the HP and EA programs. This is in good agreement with the apparent percentage effects of seeding for the randomized Texas and Thailand cloud-seeding programs, which were 43% and 48%–92%, respectively. The results and their P-value support after partitioning gave even stronger indications of positive seeding effects. Although the results of these and other analyses described herein make a strong case for enhanced rainfall by the operational seeding programs, such programs must not be viewed as substitutes for randomized seeding efforts that are conducted in conjunction with realistic cloud modeling and are followed by replication, preferably by independent groups for maximum credibility. [ABSTRACT FROM AUTHOR]

Eulerian and Lagrangian persistence of precipitation patterns derived from continental-scale radar composite images are used as a measure of predictability and for nowcasting [the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE)]. A previous paper introduced the method and focused on the lifetime of patterns of rainfall rates and the scale dependence of predictability. This paper shows how the method of persistence of radar precipitation patterns can be extended to produce probabilistic forecasts. For many applications, probabilistic information is at least as important as the expected point value. Four techniques are presented and compared. One is entirely new and makes use of the intrinsic relationship between scale and predictability. The results with this technique suggest potential use for downscaling of numerical model output. For the 143 h of precipitation analyzed so far, roughly a factor of 2 was obtained between lead times of Eulerian and Lagrangian techniques. Three of the four techniques involve a scale parameter. The slope of the relationship between optimum scale and lead time is about 1 and 2 km min[sup -1] for Lagrangian and Eulerian techniques, respectively. The skill scores obtained for the four techniques can be used as a measure of predictability in terms of probabilistic rainfall rates. The progress of other probabilistic forecasting methods, such as expert systems or numerical models, can be evaluated against the standard set by simple persistence. [ABSTRACT FROM AUTHOR]

Precipitation measurement using passive or active microwaves from space- or ground-based radar involves hypotheses about the raindrop size distribution (DSD). A universal knowledge of DSD characteristics is needed. A 4-yr dataset collected with a disdrometer at Dakar, Senegal, on the Atlantic coast of West Africa is used to analyze the DSD at the end of the continental trajectory of Sahelian squall lines. The DSDs are stratified in eight rain-rate classes and are fitted to analytical distributions. The shape of the averaged DSDs is found to be very similar from one year to the next. From rain rates R higher than about 20 mm h[sup -1] , the slope of the DSDs tends toward a constant value. The coefficients of the Z–R relation, between the radar reflectivity factor Z and R, are different for convective and stratiform parts of the squall lines. However, because the Z–R relations for convective rain intersect the relation for stratiform rain for high rates, it is suggested that using a single Z–R relation enables correct representation of the whole Z and R range of variation in West Africa. The coefficients of this single Z–R are close to that of the stratiform relation and to that observed in other West African areas. The conditional probability density function of rain rate, P(R), is of lognormal shape and also is very stable year after year. The coefficient of variation of R is found to be constant and close to 2.24, the value observed at many other sites. From P(R), the linear coefficient S(τ) of the relation that links the area-averaged rain rate to the fractional area where the rain rate is higher than the threshold τ is computed and is found to be very stable for the values of τ close to m[sub R] , the mean climatic value of R (around 5–6 mm h[sup -1] ). Because most of the sub-Saharan West African sites have a similar m[sub R] , comparison shows that S(τ) is homogeneous over this area. This result suggests that S(τ) can be used with confidence for average rainfall estimation over a climatically homogeneous region. [ABSTRACT FROM AUTHOR]

A remote sensing method is proposed to derive vertical profiles of the visible extinction coefficients in ice clouds from measurements of the radar reflectivity and Doppler velocity taken by a vertically pointing 35-GHz cloud radar. The extinction coefficient and its vertical integral, optical thickness τ, are among the fundamental cloud optical parameters that, to a large extent, determine the radiative impact of clouds. The results obtained with this method could be used as input for different climate and radiation models and for comparisons with parameterizations that relate cloud microphysical parameters and optical properties. An important advantage of the proposed method is its potential applicability to multicloud situations and mixed-phase conditions. In the latter case, it might be able to provide the information on the ice component of mixed-phase clouds if the radar moments are dominated by this component. The uncertainties of radar-based retrievals of cloud visible optical thickness are estimated by comparing retrieval results with optical thicknesses obtained independently from radiometric measurements during the yearlong Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment. The radiometric measurements provide a robust way to estimate τ but are applicable only to optically thin ice clouds without intervening liquid layers. The comparisons of cloud optical thicknesses retrieved from radar and from radiometer measurements indicate an uncertainty of about 77% and a bias of about -14% in the radar estimates of τ relative to radiometric retrievals. One possible explanation of the negative bias is an inherently low sensitivity of radar measurements to smaller cloud particles that still contribute noticeably to the cloud extinction. This estimate of the uncertainty is in line with simple theoretical considerations, and the associated retrieval accuracy should be considered good for a nonoptical instrument, such as radar. This paper also presents relations between radar-derived characteristic cloud particle sizes and effective sizes used in models. An average relation among τ, cloud ice water path, and the layer mean value of cloud particle characteristic size is also given. This relation is found to be in good agreement with in situ measurements. Despite a high uncertainty of radar estimates of extinction, this method is useful for many clouds where optical measurements are not available because of cloud multilayering or opaqueness. [ABSTRACT FROM AUTHOR]

A method for estimating the governing parameters of gamma drop size distributions (DSDs) and associated rainfall rates from polarimetric radar measurements at the S band is examined. The technique uses radar reflectivity at horizontal polarization, differential reflectivity, and an empirical constraining relationship between the DSD shape factor and slope parameter. Retrieved DSD parameters show good agreement with disdrometer observations. Retrieved rainfall estimates are insensitive to drop climatological regime. Comparison with fixed-form power-law estimators reveals that the constrained-gamma method outperforms reflectivity estimators and is roughly equivalent to radar reflectivity–differential reflectivity estimators optimized for local DSDs. [ABSTRACT FROM AUTHOR]

The spatial variability of surface rainfall over 5- and 30-day time periods is observed, and it is found that the spatial decorrelation length of precipitation is comparable to the size of a single surface gauge network. The observed variability is found to affect radar-derived precipitation estimation, particularly if it is based on calibration using rain gauges. The radar subgrid-scale variability is also observed using some redundant and finer-scale gauge networks deployed during the Tropical Rainfall Measuring Mission (TRMM) ground-validation field campaigns. Based upon statistical analysis and a point-based decision-making system, a best-suited spatial–temporal filtering technique is suggested and, when applied to match radar data with any other surface observation, is found to reduce bias. [ABSTRACT FROM AUTHOR]

Information about the vertical microphysical cloud structure is useful in many modeling and predictive practices. Radiometers and radars are used to observe hydrometeor properties. This paper describes an iterative retrieval algorithm that combines the use of airborne active and wideband (10-340 GHz) passive observations to estimate the vertical content and particle size distributions of liquid and frozen hydrometeors. Airborne radar and radiometer observations from the third Convection and Moisture Experiment (CAMEX-3) were used in the retrieval algorithm as constraints. Nadir profiles were estimated for 1 min each of flight time (approximately 12.5 km along track) for anvil, convective, and quasi-stratiform clouds associated with Hurricane Bonnie (August 1998). The physically based retrieval algorithm relies on high frequencies (≥150 GHz) to provide details on the frozen hydrometeors. Neglecting the high frequencies yielded acceptable estimates of the liquid profiles, but the ice profiles were poorly retrieved. The wideband observations were found to more than double the estimated frozen hydrometeor content as compared with retrievals using only 90 GHz and below. The convective and quasi-stratiform iterative retrievals quickly reached convergence. The complex structure of the frozen hydrometeors required the most iterations for convergence for the anvil cloud type. Nonunique profiles, within physical and theoretical bounds, were retrieved for thin anvil ice clouds. A qualitative validation using coincident in situ CAMEX-3 observations shows that the retrieved particle size distributions are well corroborated with independent measurements. [ABSTRACT FROM AUTHOR]

Continuous observations from a 50-MHz radar of the vertical profiles of winds, refractivity turbulence structure constant ([SUBn,SUP2]), turbulent kinetic energy density (σ[SUBt, SUP2]), and hourly standard deviation of the vertical velocity (σ[SUBw] are combined with routine rawinsonde ascents and weather observations to study changes in turbulence intensity over Vandenberg Air Force Base, California, during 21-27 July 2001. Early and late in this period the flow aloft was relatively steady and turbulence intensities in the upper troposphere were very low. For about 36 h around 25 July, the flow at midlevels (about 600-150 hPa) was disturbed by the passage of a cutoff cyclonic circulation center. During the low's passage, C[SUBn, SUP2] and σ[SUBt, SUP2] intensities were enhanced by up to about 15 dB. The enhancements of C[SUBn, SUP2] and σ[SUBt, SUP2] are highly correlated with each other, as well as with the vertical shear of the horizontal wind and synoptic-scale relative vorticity. The increased turbulence intensities apparently are due to in situ processes, such as shear instabilities associated with the low. Brief episodes of enhanced σ[SUBw], taken as an indicator of enhanced gravity wave activity, are also associated with the low's passage, and they are also apparently due to an in situ process, such as shear instability or geostrophic adjustment. In the lower stratosphere, above 16 km, winds show a periodic oscillation, with a period near the local inertial period and with downward phase progression. There are bursts of enhanced C[SUBn, SUP2] near the lower edge of this oscillation. The oscillation does not appear to be affected by the low's passage. [ABSTRACT FROM AUTHOR]

Mean vertical velocities and their variations observed with Indian mesosphere-stratosphere-troposphere (MST) radar located at Gadanki (13.5°N, 79.2°E), a tropical station in India, are presented. In this study, a comparison has been made between Indian MST radar-measured vertical velocities and those computed by radiosonde data using kinematic and adiabatic methods. From this study, it is observed that the signs of vertical motion estimated by the kinematic method agree well with MST-radar values, although the magnitudes differ, except in a small region where radar vertical velocity changes in sign from negative to positive in the lower troposphere during monsoon months. This upward motion in this season is attributed to horizontal convergence due to change in wind direction that is not observed in radiosonde data when averaged, because of poor height resolution of the radiosonde (500 m or more varying with height) as compared with the radar range resolution (150 m). Profiles of vertical velocities computed using the kinematic method tend to approach the shape of radar vertical velocity profiles as the separation of the radiosonde network decreases. The vertical velocities computed using the adiabatic method are found to be small and are attributed to a small tilt in isentropic surfaces caused by small latitudinal temperature gradients commonly observed in the Tropics; they also may be partly due to neglect of diabatic heating. The bias between radar and radiosonde vertical velocities tends to decrease when the time used for averaging of MST radar data is approximately 6 h (before and after 3-h average). [ABSTRACT FROM AUTHOR]

The accuracy of the radial wind velocity measured with a radar wind profiler will depend on turbulent variability and instrumental noise. Radial velocity estimates of a boundary layer wind profiler are compared with those estimated by a Doppler lidar over 2.3 h. The lidar resolution volume was much narrower than the profiler volume, but the samples were well matched in range and time. The wind profiler radial velocity was computed using two common algorithms [profiler online program (POP) and National Center for Atmospheric Research improved moments algorithm (NIMA)]. The squared correlation between radial velocities measured with the two instruments was R² = 0.99, and the standard deviation of the difference was about σ[sub r] = 0.20-0.23 m s[sup -1] for radial velocities of greater than 1 m s[sup -1] and σ[sub r] = 0.16-0.35 m s[sup -1] for radial velocities of less than 1 m s[sup -1]. Small radial velocities may be treated differently in radar wind profiler processing because of ground-clutter mitigation strategies. A standard deviation of σ[sub r] = 0.23 m s[sup -1] implies an error in horizontal winds from turbulence and noise of less than 1 m s[sup -1] for a single cycle through the profiler beam directions and of less than 0.11-0.27 m s[sup -1] for a 30-min average measurement, depending on the beam pointing sequence. The accuracy of a wind profiler horizontal wind measurement will also depend on assumptions of spatial and temporal inhomogeneity of the atmosphere, which are not considered in this comparison. The wind profiler radial velocities from the POP and NIMA are in good agreement. However, the analysis does show the need for improvements in wind profiler processing when radial velocity is close to zero. [ABSTRACT FROM AUTHOR]

In the Alps, the volume visible by a radar is reduced because of ground clutter, elevated horizon, and earth curvature. This often inhibits a direct view on precipitation close to the ground. When using radar measurements from aloft to estimate precipitation rates at ground level, the measurements must be corrected for the vertical change of the radar echo (the profile) caused by the growth and transformation of precipitation. In this paper a robust profile-correction scheme for operational use in complex orography is presented. The aim is to correct for the large errors related to the profile in an Alpine environment: frequent underestimation caused by the vertical decrease of the radar echo, and occasional overestimation in the bright band. The profile is determined from volumetric radar data integrated over a few hours within a 70-km range of the radar (mesobeta scale). The correction scheme is verified by comparing radar estimates to gauge measurements of 247 h of summer and winter precipitation in a highly mountainous area. During the selected period, 10 gauges collected a total of 3966 mm of water. Four concepts to estimate ground-level precipitation are compared: the vertical maximum echo, the lowest visible echo, estimates corrected with the average-event profile, and estimates corrected using the mesobeta profile. Comparisons with the ground truth show that in summer profile correction considerably reduces the bias and scatter. The root-mean-square error diminishes by a factor of 2. Thus, corrected radar images give a much better overall view of the precipitation field than uncorrected ones. In winter, the improvement is found in a very significant reduction of the bias. The algorithm is currently being implemented in the operational radar network of MeteoSwiss. Long-term verification is needed after a few years of operation. [ABSTRACT FROM AUTHOR]

This work resolves an apparent conflict between the results of Yuter and Houze and Atlas et al. concerning relations between radar reflectivity factor Z and rainfall rate R derived from essentially the same drop size data observed aloft during the Tropical Ocean and Global Atmosphere program's (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE). It is shown that the single relation found by Yuter and Houze is founded upon distinct underlying Z–R relations for stratiform and convective rains. This result occurs despite the fact that errors in rain types classified by different methods and the variability of the effective drop sizes within each type tend to obscure the distinction. This newly found agreement between the two distinctly different methods of analysis also suggests the essential equivalence of the classification methods of Steiner et al. (based upon the existence of a maximum in the observed reflectivity field) and of Atlas et al. (based upon the magnitude of the drafts). However, further trials are necessary to demonstrate the generality of this equivalence. [ABSTRACT FROM AUTHOR]

Focuses on the combined use of the radar and radiometer of the Tropical Rainfall Measuring Mission satellite to estimate the influence of drop size distribution on rain retrievals. Crude consistency of the radar and radiometer; Retrieval of the three-dimensional precipitation domain; Brightness temperature simulations without antenna pattern effects.

Assesses the effectiveness of the Tropical Rainfall Measuring Mission (TRMM) satellite precipitation radar (PR) in measuring the vertical reflectivity structure of a variety of precipitating systems. Comparison of PR and ground-radar geometries; Vertical reflectivity structure and relation to other TRMM measurements; Rainfall classification of convective cells.

Focuses on the use of height-time analysis of VHS stratospheric-trospospheric (ST) radar vertical velocity data to monitor lee wave structure. Reference to the study of mountain/lee waves; Information on the two dimensional linear theory; Characteristics of VHS ST radar; What findings of experiments conducted indicate.

Presents a multicomponent estimation algorithm, based on weather radar and rain gauge network, that can be used as a ground-based reference in the satellite Tropical Rainfall Measuring Mission (TRMM). Study methodology and observations; Findings that the algorithm performance is fairly sensitive to the parameter variations around the optimum.

Opinion. Analyzes some of the properties that are common to both the window probability matching method and probability matching method for rainfall measurements with radar. Introduction of a mathematical definition of the methods; Ability of the methods to build a relation between any two random variables; Validity of the methods.

Analyzes the polarimetric radar observations of hail formation in Colorado. Availability of the dual-Doppler wind field syntheses; Appearance of linear depolarization ratio (LDR) before the onset of the hail; Indication of the scattering calculations of the LDR enhancements.