Microphysics of Convective and Stratiform Precipitation During the summer monsoon season over the Yangtze–Huaihe River Valley, China

Precipitation microphysics are critical for precipitation estimation and forecasting in numerical models. Using six years of observations from the Global Precipitation Measurement satellite, the spatial characteristics of precipitation microphysics are examined during the summer monsoon season over the Yangtze–Huaihe River valley. The results indicate that the heaviest convective rainfall is located mainly between the Huaihe and Yangtze Rivers, associated with a smaller mass-weighted mean diameter (Dm = ∼1.65 mm) and a larger mean generalized intercept parameter (Nw) (∼41 dBNw) at 2 km in altitude than those over the surrounding regions. Further, the convection in this region also has the lowest polarization-corrected temperature at 89 GHz (PCT89 < 254 K), indicating high concentrations of ice-hydrometeors. For a given rainfall intensity, stratiform precipitation is characterized by a smaller mean Dm than convective precipitation. Below 4.5 km in altitude, the vertical slope of medium reflectivity factor varies with the rainfall intensity, which decreases slightly downwards for light rain (< 2.5 mm h−1), increases slightly for moderate rain (2.5–7.9 mm h−1), and increases more sharply for heavy rain (≥8 mm h−1) for both convective and stratiform precipitation. The increase in the amplitude of heavy rain for stratiform precipitation is much higher than that for convective precipitation, probably due to more efficient growth by warm rain processes. The PCT89 values have a greater potential to inform the near-surface microphysical parameters in convective precipitation compared with stratiform precipitation.

Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.

Error Analysis and Modeling of GPM Dual-Frequency Precipitation Radar Near-surface Rainfall Product

The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multi-source precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered at −1−0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1 and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error.

Retrievals of ice microphysics using dual-wavelength polarimetric radar observations during stratiform precipitation events

Ice growth processes within clouds affect the type as well as the amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR), Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-section area between both instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass-size relationship. The combination of dual-wavelength ratio, radar reflectivity and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can obtain ice particle shape, size and mass which are in line with differential radar reflectivity, dual-wavelength ratio and radar reflectivity observations when a suitable mass-size relation is used and when ice hydrometeors are assumed to be represented by oblate ice spheroids. A furthermore finding was the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD could reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

A Comparative Study on the Vertical Structures and Microphysical Properties of Stratiform Precipitation over South China and the Tibetan Plateau

Under different water vapor and dynamic conditions, and the influence of topographies and atmospheric environments, stratiform precipitation over South China and the Tibetan Plateau can produce different features. In this study, stratiform precipitation vertical characteristics, bright-band (BB) microstructures, and the vertical variations of the raindrop size distribution (DSD) over a low-altitude site (Longmen site, 86 m) in South China and a high-altitude site (Nagqu site, 4507 m) on the Tibetan Plateau were comprehensively investigated and compared using measurements from a Ka-band millimeter-wave cloud radar (CR), a K-band microrain radar (MRR), and a Parsivel disdrometer (disdrometer). A reliable BB identification scheme was proposed on the basis of CR variables and used for stratiform precipitation sample selection and further statistics and analysis. Results indicate that melting layers over the Longmen are much higher and slightly thicker than those over the Nagqu due to significant differences in atmospheric conditions. For stratiform precipitation, vertical air motions and radar variables over the two sites show different variation trends from cloud top to the ground. Vertical air motions are very weak in the stratiform precipitation over the Longmen, whereas updrafts are more active over the Nagqu. Above the melting layer, radar equivalent reflectivity factor Ze (mean Doppler velocity VM) gradually increases (decreases) as height decreases over the two sites, but the aggregation rate for ice particles over the Longmen can be faster. In the melting layer, Ze (VM) at the BB bottom/center over the Longmen is larger (smaller) than those over the Nagqu for the reason that melted raindrops in the melting layers over the Longmen are larger than those over the Nagqu. Below the melting layer, profiles of radar variables and DSDs show completely different behaviors over the two sites, which reflects that the collision, coalescence, evaporation, and breakup processes of raindrops are different between the two sites. Over the Longmen, collision and coalescence dominate the precipitation properties; in particular, from 2.0–2.8 km, the breakup process competes with collision–coalescence processes but later is overpowered. In contrast, due to the lower BB heights over the Nagqu, collision and coalescence dominate raindrop properties. Comparisons of raindrop spectra suggest that the concentration of small (medium-to-large) raindrops over the Nagqu is much higher (slightly lower) than that over the Longmen. Therefore, the mass-weighted mean diameter Dm (the generalized intercept parameter Nw) over the Nagqu is smaller (larger) than that over the Longmen.

A Real-Time Algorithm to Identify Convective Precipitation Adjacent to or within the Bright Band in the Radar Scan Domain

Hydrological hazards usually occur after heavy precipitation, especially during strong convection. Therefore, accurately identifying convective precipitation is very helpful for hydrological warning and forecasting. However, separating the convective, bright band (BB), and stratiform precipitation is found to be challenging when the convection is adjacent to or within the BB region. A new convection/BB/stratiform precipitation segregation algorithm is proposed in this study to resolve this challenging issue. This algorithm is applicable for a single radar volume scan data in native (polar) coordinates and consists of four processes: 1) check the freezing (0°C) level to roughly assess whether convection is occurring or not; 2) identify the convective cores through analyzing composite reflectivity (maximum reflectivity for a given range gate among all the sweeps), vertically integrated liquid water (VIL), VIL horizontal gradient, and reflectivity at the levels of 0°, −10°, and above −10°C; 3) delineate the whole convective region through the seeded region growing method by taking account of the microphysical differences between the BB and convective regions; and 4) delineate BB features in the stratiform region. The proposed algorithm utilizes the physical characteristics of different precipitation types for precisely segregating the convective, BB, and stratiform precipitation. This algorithm has been tested with radar data of different precipitation events and evaluated with three months of rain gauge data. The results show that the proposed algorithm performs consistently well for challenging precipitation events with the convection adjacent to or within a strong BB. Furthermore, the proposed algorithm could be used to improve the vertical profile of reflectivity (VPR) correction and reduce the overestimation of rainfall in the BB precipitation region.

Intra-Event Isotopic Changes in Water Vapor and Precipitation in South China

Synchronous observations of the isotopic composition of water vapor and precipitation for 24 rain events were performed. Rain events driven by low-level jets exhibited similar isotopic changes in precipitation and water vapor. The vertical activity of water vapor in convection causes the isotopic variation in precipitation to be opposite to that of water vapor. Isotopic changes of precipitation in low-pressure systems were partially synchronized with that of water vapor at high but not low water vapor concentrations. Changes in microphysical meteorological properties in stratiform precipitation give rise to different patterns of isotopic changes in water. The re-evaporation of raindrops can be determined by the enrichment ratio of heavy isotopes in the water under the cloud base, which is closely related to the raindrop radius. Stratiform precipitation, with small raindrop sizes, was prone to kinetic fractionation under the cloud base. The raindrop radius of low-level jets was small, favoring exchange with surrounding air and re-evaporation. The moist air mass in convection facilitates isotopic exchange of raindrops with surrounding water vapor, leading to low enrichment ratios. The lowest enrichment ratios in low-pressure systems were due to environments characterized by large-scale water vapor convergence.