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A dataset of convection initiation (CI) is of great value in studying the triggering mechanisms of deep moist convection and evaluating the performances of numerical models. In recent years, the data quality of the operationally generated radar mosaics over China has been greatly improved, which provides an opportunity to retrieve a CI dataset from that region. In this work, an attempt is made to reveal the potential of applying a simple framework of objective CI detection for the study of CI climatology in China. The framework was tested using radar mosaic maps in South China that were accessible online. The identified CI events were validated in both direct and indirect ways. On the basis of a direct manual check, nearly all of the identified CI cells had an organized motion. The precipitation echoes of the cells had a median duration of approximately 2.5 h. The CI occurrences were further compared with rainfall estimates to ensure physical consistency. The diurnal cycle of CI occurrence exhibits three major modes: a late-night-to-morning peak at the windward coasts and offshore, a noon-to-late-afternoon peak on the coastal land, and an evening-to-early-morning peak over the northwestern highland. These spatial modes agree well with those of rainfall, indirectly suggesting the reliability of the CI statistics. By processing radar mosaic maps, such a framework could be applied for studying CI climatology over China and other regions.

Time series of quasi–vertical profiles (QVPs) from 52 stratiform precipitation events observed with the polarimetric X-band radar in Bonn, Germany (BoXPol), between 2013 and 2016 have been statistically analyzed to infer microphysical processes shaping the dendritic-growth-layer (DGL) and melting-layer (ML) signatures including surface rainfall. Specific differential phase KDP in the ML shows an average correlation of 0.65 with surface rainfall for these cases. Radar reflectivity decreases below the ML by about 2 dB on average while differential reflectivity ZDR is hardly affected, which suggests rain evaporation as the dominating effect. Estimated ice water content or snow water equivalent precipitation rate S in the DGL is correlated with surface rain rates with lead times of 30 min and longer, which opens a pathway for radar-based nowcasting of stratiform precipitation tendencies. Trajectories of snow generated aloft down to the surface are constructed from wind profiles derived both from the nearest radiosounding and radar-based velocity azimuth displays (VAD) to narrow down the location at which the DGL-generated snow reaches the surface as rain. The lagged correlation analysis between KDP in the DGL and reflectivity ZH at that location demonstrates the superiority of the VAD information. Correlation coefficients up to 0.80 with lead times up to 120 min provide a proof of concept for future nowcasting applications that are based on DGL monitoring. Statistical relations found in our QVP dataset provide a database for estimating intrinsic polarimetric variables from the usual azimuth and elevation scans within and in the vicinity of the ML.

During the Observations of Aerosols above Clouds and Their Interactions (ORACLES) 2016 and 2017 field experiments, the Third Generation Advanced Precipitation and Cloud Radar (APR-3) flew aboard the NASA P-3 aircraft taking over 10 million profiles of stratocumulus clouds in the southeast Atlantic Ocean. This study documents cloud structure, precipitation frequency and intensity, and atmospheric stability for each flight during both field experiments. A larger cloud fraction was estimated for 2016, likely due to a larger estimated inversion strength (EIS) in the experiment area (between 6 and 10 K) compared to 2017 where EIS was on average 4–6 K lower. We used an optimal estimation retrieval to derive precipitation rates for all measurable clouds during both experiments. Over 30% of clouds observed during the 2016 experiment exhibited precipitation reaching the surface, but retrieved drizzle rates were below 0.01 mm h−1 in all but 40% of these profiles. This is in sharp contrast to the 2017 campaign where over 53% of precipitating profiles had rainfall rates larger than 0.01 mm h−1. The differences in cloud and rain fractions between the two years are most likely due to differences in the sampling environments; however, enough variations in cloud, virga, and rain fraction exist for similar environmental conditions such that additional analysis of cloud and aerosol interactions—specifically their effect on precipitation processes—needs further exploration. The extensive APR-3 sampling of drizzling stratocumulus under similar thermodynamic conditions provides a rich dataset for examining the influence of biomass burning aerosols on cloud fraction, morphology, and precipitation characteristics in this climatically important region.

The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.

Global Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter Do of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing Do for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing Do of cloud ice, channels at ≥89.0 GHz are most sensitive to changing Do of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail Do. Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

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.

This study used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350 m MSL, August 2014–February 2015), and La Paz, Bolivia (3440 m MSL, October 2015–February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time–height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17% of the time in Cusco and 30% of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015–16 El Niño, almost half of storms in La Paz had melting layers above 5000 m MSL.

Utilizing the cloud parameters derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner and the near-surface rainfall detected by the TRMM Precipitation Radar, the differences of cloud parameters for precipitating clouds (PCs) and nonprecipitating clouds (NPCs) are examined in tropical cyclones (TCs) during daytime from June to September 1998–2010. A precipitation delineation scheme that is based on cloud parameter thresholds is proposed and validated using the independent TC datasets in 2011 and observational datasets from Terra/MODIS. Statistical analysis of these results shows that the differences in the effective radius of cloud particles Re are small for PCs and NPCs, while thick clouds with large cloud optical thickness (COT) and liquid water path (LWP) can be considered as candidates for PCs. The probability of precipitation increases rapidly as the LWP and COT increase, reaching ~90%, whereas the probability of precipitation reaches a peak value of only 30% as Re increases. The combined threshold of a brightness temperature at 10.8 μm (BT4) of 270 K and an LWP of 750 g m−2 shows the best performance for precipitation discrimination at the pixel levels, with the probability of detection (POD) reaching 68.2% and false-alarm ratio (FAR) reaching 31.54%. From MODIS observations, the composite scheme utilizing BT4 and LWP also proves to be a good index, with POD reaching 77.39% and FAR reaching 24.2%. The results from this study demonstrate a potential application of real-time precipitation monitoring in TCs utilizing cloud parameters from visible and infrared measurements on board geostationary weather satellites.

This study investigates the how riming in stratiform precipitation impacts polarimetric signatures. Using a vertically pointing Doppler X-band radar, cases can be separated into one of three groups: unrimed to lightly rimed, riming with no bimodal spectra and fall speeds greater than 2.0 m s−1, and riming with bimodal velocity spectra. By averaging polarimetric variables over a 20° by 10-km box near the X-band radar, different signatures were documented for each of the three groups. These polarimetric signatures were then compared with a simplified T-matrix scattering model. Differential reflectivity ZDR was the one polarimetric variable to consistently vary across all three groups. Unrimed to lightly rimed cases had profiles of polarimetric signatures similar to numerous previous studies. Riming cases without detectable bimodal spectra had ZDR values on the order of 0.2 dB lower than unrimed to lightly rimed cases, while cases with bimodal spectra had ZDR values about 0.2–0.4 dB higher than unrimed to lightly rimed cases. Both signatures were reproduced using populations of aggregates, dendrites, and needles in the T-matrix scattering model. While these signatures show the potential to identify riming, they are not enough larger than measurement biases and case-to-case variability to be confidently used without confirmation from other data sources, such as a vertically pointing radar.

Accurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as the large diversity among snow growth habits. In this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations S = and along with similar relations for the intercept N0s and slope Λs of the exponential snow size distribution. The physical basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle distribution and to a lesser extent the particles’ aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three geographical regions (Oklahoma, Colorado, and Canada). Significant improvement in snow estimates relative to the traditional Z-based methods is demonstrated.

In previous studies, remote sensing properties of hailstorms have been discussed using various spaceborne sensors. Relationships between hail occurrence and strong passive microwave brightness temperature depressions have been established. Using a 16-yr precipitation-feature database derived from the Tropical Rainfall Measuring Mission (TRMM) satellite, the performance of the TRMM Precipitation Radar and TRMM Microwave Imager is further investigated for hail detection. Detection criteria for hail larger than 19 mm are separately developed from Ku-band radar reflectivity and microwave brightness temperature properties of precipitation features that are collocated with surface hail reports over the southeastern and south-central United States. A threshold of 44 dBZ at −22°C is found to have the highest critical success index and Heidke skill score. The threshold of 230 K at 37 GHz yields the best scores among passive microwave properties. Using these two thresholds, global distributions of possible hail events are generated over 65°S–65°N using two years of observations from the Global Precipitation Measurement Core Observatory satellite. Differences in the derived hail geographical distributions are found between radar and passive microwave methods over tropical South America, the “Maritime Continent,” west-central Africa, Argentina, and South Africa. These discrepancies result from different vertical structures of the maximum radar reflectivity profiles over these regions relative to the southeastern and south-central United States, where the thresholds were established. Those differences generally led to overestimates in the tropics from the passive microwave methods, relative to the radar-based methods.

Empirical power-law relations between the equivalent radar reflectivity factor Ze and the slope parameter of the gamma function Λ (i.e., Λ = c; used to describe ice hydrometeor size distributions) are derived. The Λ parameter can also be considered as a size parameter since it is proportional to the inverse of the hydrometeor characteristic size, which is an important geophysical parameter describing the entire distribution. Two datasets from two-dimensional microphysical probes, collected during aircraft flights in subtropical and midlatitude regions, were used to obtain Λ by fitting measured size distributions. Reflectivity for different radar frequencies was calculated from microphysical probe data by using nonspherical-particle models. The derived relations have exponent d values that are approximately from −0.35 to −0.40, and the prefactors c are approximately between 30 and 55 (Λ: cm−1; Ze: mm6 m−3). There is a tendency for d and c to decrease when radar frequency increases from Ku band (~14 GHz) to W band (~94 GHz). Correlation coefficients between Ze and Λ can be very high (~0.8), especially for lower frequencies. Such correlations are similar to those for empirical relations between reflectivity and ice water content (IWC), which are used in many modeling and remote sensing applications. Close correspondences of reflectivity to both Λ and IWC are due to a relatively high correlation between these two microphysical parameters. Expected uncertainties in estimating Λ from reflectivity could be as high as a factor of 2, although estimates at lower radar frequencies are more robust. Stratifying retrievals by temperature could result in relatively modest improvement of Λ estimates.

Observations and analysis of an ice–liquid phase precipitation event, collected with an S-band polarimetric KOUN radar and a two-dimensional video disdrometer (2DVD) in central Oklahoma on 20 January 2007, are presented. Using the disdrometer measurements, precipitation is classified either as ice pellets or rain/freezing rain. The disdrometer observations showed fast-falling and slow-falling particles of similar size. The vast majority (>99%) were fast falling with observed velocities close to those of raindrops with similar sizes. In contrast to the smaller particles (1.5 mm) were relatively easy to distinguish because their shapes differ from the raindrops. The ice pellets were challenging to detect by looking at conventional polarimetric radar data because of the localized and patchy nature of the ice phase and their occurrence close to the ground. Previously published findings referred to cases in which ice pellet areas were centered on the radar location and showed a ringlike structure of enhanced differential reflectivity ZDR and reduced copolar correlation coefficient ρhv and horizontal reflectivity ZH in PPI images. In this study, a new, unconventional way of looking at polarimetric radar data is introduced: slanted vertical profiles (SVPs) at low (0°–1°) radar elevations. From the analysis of the localized and patchy structures using SVPs, the polarimetric refreezing signature, reflected in local enhancement in ZDR and reduction in ZH and ρhv, became much more evident. Model simulations of sequential drop freezing using Marshall–Palmer DSDs along with the observations suggest that preferential freezing of small drops may be responsible for the refreezing polarimetric signature, as suggested in previous studies.

Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.

Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (≥100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day−1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling.

Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.

Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, ZDR, and KDP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level ZDR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm.

S-band profiling (S-PROF) radar measurements from different southeastern U.S. Hydrometeorology Testbed sites indicated a frequent occurrence of rain that did not exhibit radar bright band (BB) and was observed outside the periods of deep-convective precipitation. This common nonbrightband (NBB) rain contributes ~15%–20% of total accumulation and is not considered as a separate rain type by current precipitation-segregation operational radar-based schemes, which separate rain into stratiform, convective, and, sometimes, tropical types. Collocated with S-PROF, disdrometer measurements showed that drop size distributions (DSDs) of NBB rain have much larger relative fractions of smaller drops when compared with those of BB and convective rains. Data from a year of combined DSD and rain-type observations were used to derive S-band-radar estimators of rain rate R, including those based on traditional reflectivity Ze and ones that also use differential reflectivity ZDR and specific differential phase KDP. Differences among same-type estimators for mostly stratiform BB and deep-convective rain were relatively minor, but estimators derived for the common NBB rain type were distinct. Underestimations in NBB rain-rate retrievals derived using other rain-type estimators (e.g., those for BB or convective rain or default operational radar estimators) for the same values of radar variables can be on average about 40%, although the differential phase-based estimators are somewhat less susceptible to DSD details. No significant differences among the estimators for the same rain type derived using DSDs from different observational sites were present despite significant separation and differing terrain. Identifying areas of common NBB rain could be possible from Ze and ZDR measurements.

It is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors’ experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar data are available. As a result, the 3DVAR analysis with the constraint is more accurate when verified against an independent dataset. The performance of this new constraint on improving precipitation forecasts is tested using six convective cases and verified against radar-derived precipitation by employing four skill indices. All of the skill indices show improved forecasts when using the methodology presented in this paper.

Microphysical processes in shallow Arctic precipitation clouds are illustrated using measurements of differential reflectivity ZDR from the U.S. Department of Energy Atmospheric Radiation Measurement Program polarimetric X-band radar deployed in Barrow, Alaska. X-band hemispheric range height indicator scans used in conjunction with Ka-band radar and lidar measurements revealed prolonged periods dominated by vapor depositional, riming, and/or aggregation growth. In each case, ice precipitation fell through at least one liquid-cloud layer in a seeder–feeder situation before reaching the surface. A long period of sustained low radar reflectivity ZH (Z) and high ZDR (6–7.5 dB) throughout the depth of the cloud and subcloud layer, coinciding with observations of large pristine dendrites at the surface, suggests vapor depositional growth of large dendrites at low number concentrations. In contrast, ZDR values decreased to 2–3 dB in the mean profile when surface precipitation was dominated by aggregates or rimed dendrites. Small but consistent differences in zenith Ka-band radar Doppler velocity and lidar depolarization measurements were found between aggregation- and riming-dominated periods. The clean Arctic environment can enhance ZDR signals relative to more complex midlatitude cases, producing higher values.

The spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

An approach is described to retrieve the total amount of ice in a vertical atmospheric column in precipitating clouds observed by the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This amount expressed as ice water path (IWP) is retrieved using measurements obtained during standard WSR-88D scanning procedures performed when observing precipitation. WSR-88D-based IWP estimates are evaluated using dedicated cloud microphysical retrievals available from the CloudSat and auxiliary spaceborne measurements. The evaluation is performed using measurements obtained in extensive predominantly stratiform precipitation systems containing both ice hydrometeors aloft and rain near the ground. The analysis is based on retrievals of IWP from satellite and the ground-based KWGX and KSHV WSR-88D that are closely collocated in time and space. The comparison results indicate a relatively high correlation between satellite and WSR-88D IWP retrievals, with corresponding correlation coefficients of around 0.7. The mean relative differences between spaceborne and ground-based estimates are around 50%–60%, which is on the order of IWP retrieval uncertainties and is comparable to the differences among various operational CloudSat IWP products. The analysis performed in this study suggests that the quantitative information on ice content of precipitation systems can generally be obtained from operational WSR-88D measurements, when they perform routine scans to observe precipitation. The limitations of WSR-88D IWP estimates due to radar beam tilt restrictions and the overshooting effects due to Earth’s sphericity are discussed.

Characteristics of graupel in an Arctic deep mixed-phase cloud on 7 December 2013 were identified with observations from an X-band scanning polarimetric radar and a Ka-band zenith-pointing radar in conjunction with scattering calculations. The cloud system produced generating cells and strongly sheared precipitation fall streaks. The X-band radar hemispheric RHI observables revealed spatial sorting of polarimetric signatures: decreasing (with increasing range) differential propagation phase shift φDP, negative specific differential phase KDP collocated with negative differential reflectivity ZDR in the upper half of the fall streak, and increasing or near-constant φDP with positive ZDR at the bottom edge of the fall streak. The negative KDP and ZDR, indicating prolate particles with vertically oriented maximum dimensions, were consistent with small, slow-falling conical graupel coexisting with low concentrations of more isometric graupel. The observed negative KDP values were best matched by scattering calculations for small, dense conical graupel with 30°–40° cone angles. The positive KDP and ZDR and the Doppler spectra indicate that large isometric graupel coexisted with a second population of slower-falling rimed platelike particles in the lower half of the fall streak. Through the core of the fall streak, φDP decreased in range while ZDR was slightly positive, indicating that the prolate conical graupel dominated φDP while the isometric larger graupel dominated reflectivity (and thus ZDR). These results demonstrate the capability of polarimetric observables and Doppler spectra to distinguish different growth stages of rimed particles, allowing for the improvement of hydrometeor classification methods.

This study demonstrates that linear depolarization ratio (LDR) values obtained from zenith-pointing Ka-band radar Doppler velocity spectra are sufficient for detecting columnar ice crystals. During a deep precipitating system over the Arctic on 7 December 2013, the radar recorded LDR values up to −15 dB at temperatures corresponding to the columnar ice crystal growth regime. These LDR values were also consistent with scattering calculations for columnar ice crystals. Enhancements in LDR were suppressed within precipitation fallstreaks because the enhanced LDR values of columnar ice crystals were masked by the returns from the particles within the fallstreaks. However, Doppler velocity spectra of LDR within the fallstreak distinguished populations of slower-falling particles with high LDR (>−15 dB) and faster-falling particles with much lower LDR, suggesting that columnar ice crystals with high LDR coexisted with larger isometric particles that produced low LDR while dominating the total copolar reflectivity, thereby decreasing LDR. The measurements suggest that the columnar ice crystals originated in liquid-cloud layers through secondary ice production.

Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.

Version-7 (V7) rain rates retrieved by the TRMM Microwave Imager (TMI) and Precipitation Radar (PR) are spatially and temporally collocated over the ocean and compared at TMI footprint scale for the summer months of 16 years, within the TRMM coverage belt from 38°S to 38°N latitude. This study puts special emphasis on examining how the estimates from the two instruments compare with each other for different rain types and for different geographical locations. It is found that, although the two rain-rate estimates agree with each other extremely well (only 2.6% difference) when averaged globally and over all rain types, large discrepancies (~60%) are observed if comparisons are conducted for rain pixels of only convective type or for regions where convective rain types dominate. For the stratiform rain type, the TMI and PR retrievals compare well with a difference of ~13% globally. In particular, the partial beam filling seems to be less important to the underestimation of TMI rain against PR rain than the spatial variability of rain. These findings point to the existing need for better understanding of the remote-sensing physics of convective rain. Such an improved understanding is critically important to decreasing the uncertainty in oceanic rainfall estimation from space in the coming GPM era of global long-term observations that will lead to the creation of a climate record of trends in precipitation.

Small-scale summer rainfall variability in a semiarid zone was studied by deploying five vertically pointing Micro Rain Radars (MRRs) along a nearly straight line and by using 12 rain gauges in the study area of the Xilin River catchment in China. The spatial scales of 4 and 9 km correspond to the resolution of precipitation radar and rainfall products from satellites. The dataset of the MRRs and rain gauges covers two months in the summer of 2009. Three parameters, that is, spatial correlation, intermittency, and the coefficient of variation (CV), were used to describe the rainfall variability as based on the data from the MRRs and rain gauges. The probability of partial beamfilling in a 4-km (9 km) pixel over a 30-min temporal scale was 17%–20% (28%–37%). More accurate equipment can measure lower rainfall intermittency. For scales of 4 and 9 km, the median CV of the accumulation times that were longer than 3 h with rainfall > 1 mm was 0.17–0.42. The accuracy of areal rainfall measured by different quantities of equipment was also evaluated. One MRR was sufficient for measuring the daily areal rainfall at a 4-km scale, with a fraction of prediction within a factor of 2 of observations of 1.0 and a correlation coefficient of ≥0.58 when daily mean rainfall was >1 mm.

Gauge-calibrated radar estimates of daily precipitation are compared with daily observed values of precipitation from National Weather Service (NWS) Cooperative Observer Network (COOP) stations to evaluate the multisensor precipitation estimate (MPE) product that is gridded by the National Centers for Environmental Prediction (NCEP) for the eastern United States (defined as locations east of the Mississippi River). This study focuses on a broad evaluation of MPE across the study domain by season and intensity. In addition, the aspect of precipitation type is considered through case studies of winter and summer precipitation events across the domain. Results of this study indicate a north–south gradient in the error of MPE and a seasonal pattern with the highest error in summer and autumn and the lowest error in winter. Two case studies of precipitation are also considered in this study. These case studies include instances of intense precipitation and frozen precipitation. These results suggest that MPE is less able to estimate convective-scale precipitation as compared with precipitation variations at larger spatial scales. In addition, the results suggest that MPE is subject to errors related both to the measurement gauges and to the radar estimates used. Two case studies are also included to discuss the differences with regard to precipitation type. The results from these case studies suggest that MPE may have higher error associated with estimating the liquid equivalent of frozen precipitation when compared with NWS COOP network data. The results also suggest the need for more analysis of MPE error for frozen precipitation in diverse topographic regimes.

Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

A new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3–8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data.

A framework based on measured raindrop size distribution (DSD) data has been developed to assess uncertainties in DSD models employed in Ku- and Ka-band dual-wavelength radar retrievals. In this study, the rain rates and attenuation coefficients from DSD parameters derived by dual-wavelength algorithms are compared with those directly obtained from measured DSD spectra. The impact of the DSD gamma parameterizations on rain estimation from the Global Precipitation Measurement mission (GPM) Dual-Frequency Precipitation Radar (DPR) is examined for the cases of a fixed shape factor μ as well as for a constrained μ—that is, a μ–Λ relation (a relationship between the shape parameter and slope parameter Λ of the gamma DSD)—by using 11 Particle Size and Velocity (Parsivel) disdrometer measurements with a total number of about 50 000 one-minute spectra that were collected during the Iowa Flood Studies (IFloodS) experiment. It is found that the DPR-like dual-wavelength techniques provide fairly accurate estimates of rain rate and attenuation if a fixed-μ gamma DSD model is used, with the value of μ ranging from 3 to 6. Comparison of the results reveals that the retrieval errors from the μ–Λ relations are generally small, with biases of less than ±10%, and are comparable to the results from a fixed-μ gamma model with μ equal to 3 and 6. The DSD evaluation procedure is also applied to retrievals in which a lognormal DSD model is used.

In this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden–Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Ze (GAZ) below 1 km and near-surface Ze] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z–R (reflectivity–rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z–R relation.

Experimental retrievals of rain rates using the CloudSat spaceborne 94-GHz radar reflectivity gradient method over land were evaluated by comparing them with standard estimates from ground-based operational S-band radar measurements, which are widely used for quantitative precipitation estimations. The comparisons were performed for predominantly stratiform precipitation events that occurred in the vicinity of the Weather Surveillance Radar-1988 Doppler (WSR-88D) KGWX and KSHV radars during the CloudSat overpasses in the vicinity of these ground radar sites. The standard reflectivity-based WSR-88D rain-rate retrievals used in operational practice were utilized as a reference for the CloudSat retrieval evaluation. Spaceborne and ground-based radar rain-rate estimates that were closely collocated in space and time were generally well correlated. The correlation coefficients were approximately 0.65 on average, and the mean relative biases were usually within ±35% for the whole dataset and for individual events with typical rain rates exceeding ~2 mm h−1. For events with lighter rainfall, higher biases and lower correlations were often present. The normalized mean absolute differences between satellite- and ground-based radar retrievals were on average ~60%, with an increasing trend for lighter rainfall. Such mean differences are comparable to combined retrieval errors from both ground-based and satellite radar remote sensing approaches. Evaluation of potential effects of partial beam blockage on the ground-based radar measurements was performed, and the influence of the choice of relation between WSR-88D reflectivity and rain rate that was utilized in the ground-based rain-rate retrievals was assessed.

Profiling airborne radar data and accompanying large-eddy-simulation (LES) modeling are used to examine the impact of ground-based glaciogenic seeding on cloud and precipitation in a shallow stratiform orographic winter storm. This storm occurred on 18 February 2009 over a mountain in Wyoming. The numerical simulations use the Weather Research and Forecasting (WRF) Model in LES mode with horizontal grid spacings of 300 and 100 m in a domain covering the entire mountain range, and a glaciogenic seeding parameterization coupled with the Thompson microphysics scheme. A series of non-LES simulations at 900-m resolution, each with different initial/boundary conditions, is validated against sounding, cloud, and precipitation data. The LES runs then are driven by the most representative 900-m non-LES simulation. The 100-m LES results compare reasonably well to the vertical-plane radar data. The modeled vertical-motion field reveals a turbulent boundary layer and gravity waves above this layer, as observed. The storm structure also validates well, but the model storm thins and weakens more rapidly than is observed. Radar reflectivity frequency-by-altitude diagrams suggest a positive seeding effect, but time- and space-matched model reflectivity diagrams only confirm this in a relative sense, in comparison with the trend in the control region upwind of seeding generators, and not in an absolute sense. A model sensitivity run shows that in this case natural storm weakening dwarfs the seeding effect, which does enhance snow mass and snowfall. Since the kinematic and microphysical structure of the storm is simulated well, future Part II of this study will examine how glaciogenic seeding impacts clouds and precipitation processes within the LES.

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 Ze 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 Ze was −8.7 and 1.2 dB before and after the VPR correction, respectively.

This study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)–NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point– and areal–gauge bias and root-mean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5–2.5 in RMS errors for areal accumulations over a 15-km2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods.

This study reports on the presence of suspicious “extreme rainfall” data in the 2A25 version-7 (V7) product of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset and introduces a simple method for detecting and filtering out the suspicious data. These suspicious data in V7 are found by comparing the extreme rainfall characteristics in the V7 and version-6 products. Most of the suspicious extremes are located over land, especially in mountainous regions. Radar reflectivities in the suspicious extremes show significant monotonic increases toward the echo bottom. These facts indicate that the suspicious extremes are mainly caused by contamination from ground or sea clutter. A simple thresholding filter for eliminating the suspicious extreme data is developed using common characteristics in the horizontal and vertical rainfall structures and reflectivities in the suspicious extremes. The proposed filter mitigates deformations in the frequency distribution of the surface rainfall rate in the 2A25 V7 product.

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

On the basis of simulations and observations made with polarimetric radars operating at X, C, and S bands, the backscatter differential phase δ has been explored; δ has been identified as an important polarimetric variable that should not be ignored in precipitation estimations that are based on specific differential phase KDP, especially at shorter radar wavelengths. Moreover, δ bears important information about the dominant size of raindrops and wet snowflakes in the melting layer. New methods for estimating δ in rain and in the melting layer are suggested. The method for estimating δ in rain is based on a modified version of the “ZPHI” algorithm and provides reasonably robust estimates of δ and KDP in pure rain except in regions where the total measured differential phase ΦDP behaves erratically, such as areas affected by nonuniform beam filling or low signal-to-noise ratio. The method for estimating δ in the melting layer results in reliable estimates of δ in stratiform precipitation and requires azimuthal averaging of radial profiles of ΦDP at high antenna elevations. Comparisons with large disdrometer datasets collected in Oklahoma and Germany confirm a strong interdependence between δ and differential reflectivity ZDR. Because δ is immune to attenuation, partial beam blockage, and radar miscalibration, the strong correlation between ZDR and δ is of interest for quantitative precipitation estimation: δ and ZDR are differently affected by the particle size distribution (PSD) and thus may complement each other for PSD moment estimation. Furthermore, the magnitude of δ can be utilized as an important calibration parameter for improving microphysical models of the melting layer.

Data from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.

This study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the characteristics of VPRs, the shape of VPRs also influences the intensity of surface rainfall rates, the characteristics of VPRs have a seasonal dependence with strong similarities between the spring and autumn months, and the spatial variation of VPR characteristics suggests that the underlying terrain has an impact on the vertical structure. The comprehensive statistical and physical analysis strengthens the understanding of the vertical structure of precipitation and advocates for the approach of VPR correction to improve surface precipitation estimation in complex terrain.

An important application of radar reflectivity measurements is their interpretation as precipitation intensity. Empirical relationships exist for converting microwave backscatter retrieved from precipitation particles (represented by an equivalent reflectivity factor Ze) to precipitation intensity. The reflectivity–snow-rate relationship has the form Ze = αSβ, where S is a liquid-equivalent snow rate and α and β are fitted coefficients. Substantial uncertainty exists in radar-derived values of snow rate because the reflectivity and intensity associated with snow tend to be smaller than those for rain and because of snow-particle drift between radar and surface detection. Uncertainty in radar-derived snow rate is especially evident at the few available high-altitude sites for which a relationship between reflectivity and snow rate has been developed. Using a new type of precipitation sensor and a National Weather Service radar, this work investigates the Ze–S relationship at a high-altitude site (Cheyenne, Wyoming). The S measurements were made 25 km northwest of the radar on the eastern flank of the Rocky Mountains; vertical separation between the radar range gate and the ground was less than 700 m. A meteorological feature of the snowstorms was northeasterly upslope flow of humid air at low levels. The Ze–S data pairs were fitted with β = 2. The finding of this study for Cheyenne, α = 110 mm4 h2 m−3, is bounded by previous determinations made at other high-altitude National Weather Service sites. Also investigated was the temperature dependence of α. A positive α–T relationship is evident and is hypothesized to result from ice crystals produced by heterogeneous ice nucleation, at cloud top, followed by diffusional crystal growth during sedimentation.

This study evaluates space-based rain estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using ground-based measurements from the radar (GR) and tipping-bucket rain gauges (TG) over the TRMM Ground Validation (GV) site at Melbourne, Florida. The satellite rain products are derived from the TRMM Microwave Imager (TMI), precipitation radar (PR), and combined (COM) rain algorithms using observations from both instruments. The TRMM satellite and GV rain products are spatiotemporally matched and are intercompared at multiple time scales over the 12-yr period from 1998 to 2009. On monthly and yearly scales, the TG agree excellently with the GR because the GR rain rates are generated using the TG data as a constraint on a monthly basis. However, large disagreements exist between the GR and TG at shorter time scales because of their significantly different spatial and temporal sampling modes. The yearly biases relative to the GR for the PR and TMI are generally negative, with a few exceptions. The COM bias fluctuates from year to year over the 12-yr period. The PR, TMI, and COM are in good overall agreement with the GR in the lower range of rain rates, but the agreement is notably worse at higher rain rates. The diurnal cycle of rainfall is captured well by all products, but the peak satellite-derived rainfall (PR, TMI, and COM) lags the peak from the ground measurements (GR and TG) by ~1 h.

The short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

A new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

Summer rainfall characteristics over the Korean Peninsula are examined using six years of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements and surface rain measurements from the densely populated rain gauges spread across South Korea. A comparison of the TMI brightness temperature at 85 GHz with the measured surface rain rate reveals that a significant portion of rainfall over the peninsula occurs at warmer brightness temperatures than would be expected from the Goddard profiling (GPROF) database. By incorporating the locally observed rain characteristics into the GPROF algorithm, efforts are made to test whether locally appropriate hydrometeor profiles may be used to improve the retrieved rainfall. Profiles are obtained by simulating rain cases using the cloud-resolving University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) model and matching the calculated radar reflectivities to TRMM precipitation radar (PR) reflectivities. Selected profiles and the corresponding simulated TMI brightness temperatures (limited in this study to values that are larger than 235 K) are added to the GPROF database to form a modified database that is considered to be more suitable for local application over the Korean Peninsula. The rainfall retrieved from the new database demonstrates that heavy-rainfall events—in particular, those associated with warmer clouds—are better captured by the new algorithm as compared with the official TRMM GPROF version-6 retrievals. The results suggest that a more locally suitable rain retrieval algorithm can be developed if locally representative rain characteristics are included in the GPROF algorithm.

Radar-rainfall estimation is a complex process that involves several error sources, some of which are related to the environmental context. The presence of orographic obstacles heavily affects the quality of the retrieved radar products. In relatively flat terrain conditions, dual-polarization capability has been proven either to increase the data quality or to improve the rainfall estimate. The potential benefit of using polarimetric techniques for precipitation retrieval is evaluated here using data coming from two radar systems operating in Italy under complex-orography conditions. The analysis outlines encouraging results that might open new scenarios for operational applications. Indeed, the applied rainfall algorithm employing specific differential phase mostly outperformed the examined reflectivity-based retrieval techniques except for the analyzed winter storm. In the latter case, the likely contamination by frozen or melting snow tended to degrade the performance of the examined Kdp-based rainfall algorithms.

Extended, high-resolution measurements of vertical air motion and median volume drop diameter D0 in widespread precipitation from three diverse Atmospheric Radiation Measurement Program (ARM) locations [Lamont, Oklahoma, Southern Great Plains site (SGP); Niamey, Niger; and Black Forest, Germany] are presented. The analysis indicates a weak (0–10 cm−1) downward air motion beneath the melting layer for all three regions, a magnitude that is to within the typical uncertainty of the retrieval methods. On average, the hourly estimated standard deviation of the vertical air motion is 0.25 m s−1 with no pronounced vertical structure. Profiles of D0 vary according to region and rainfall rate. The standard deviation of 1-min-averaged D0 profiles for isolated rainfall rate intervals is 0.3–0.4 mm. Additional insights into the form of the raindrop size distribution are provided using available dual-frequency Doppler velocity observations at SGP. The analysis suggests that gamma functions better explain paired velocity observations and radar retrievals for the Oklahoma dataset. This study will be useful in assessing uncertainties introduced in the measurement of precipitation parameters from ground-based and spaceborne remote sensors that are due to small-scale variability.

The algorithms used to estimate rainfall from polarimetric radar variables show significant variance in error characteristics over the range of naturally occurring rain rates. As a consequence, to improve rainfall estimation accuracy using polarimetric radar, it is necessary to optimally combine a number of different algorithms. In this study, a new composite method is proposed that weights the algorithms by the inverse of their theoretical error. A number of approaches are discussed and are investigated using simulated radar data calculated from disdrometer measurements. The resultant algorithms show modest improvement over composite methods based on decision-tree logic—in particular, at rain rates above 20 mm h−1.