Explore the vast range of titles available.

In this study, significant rainfall microphysical variability is revealed for the extremely heavy rainfall event over Henan Province in July 2021 (the “21·7” Henan EHR event) using a dense network of disdrometers and two polarimetric radars. The broad distributions of specific drop size distribution (DSD) parameters are identified in heavy rainfall from the disdrometer observations, indicating obvious microphysical variability on the surface. A K-means clustering algorithm is adopted to objectively classify the disdrometer datasets into separate groups, and distinct DSD characteristics are found among these heavy rainfall groups. Combined with the supporting microphysical structures obtained through radar observations, comprehensive microphysical features of the DSD groups are derived. An extreme rainfall group is dominantly formed in the deep convection over the plain regions, where the high number of concentrations and large mean sizes of surface raindrops are underpinned by both active ice-phase processes and efficient warm-rain collision-coalescence processes in the vertical direction. Convection located near orographic regions is characterized by restricted ice-phase processes and high coalescence efficiency of liquid hydrometeors, causing the dominant DSD group to comprise negligible large raindrops. Multiple DSD groups can coexist within certain precipitation episodes at the disdrometer stations, indicating the potential microphysical variability during the passage of convective system on the plain regions.

Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L’Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L’Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles.

Significant variability of raindrop size distributions (DSDs) has been observed in the “21·7” Henan extremely heavy rainfall event (the “21·7” Henan EHR event), while the capability of model to reproduce such complicated heavy rainfall DSDs is yet unclear. This study primarily evaluates the simulated DSDs of multiple microphysics schemes by comparing with the observations from a network of 50 disdrometers. Constrained DSD variability is identified in most schemes that the simulated raindrop mean sizes are gradually restricted around specific values as the growth of heavy rainfall intensity. The schemes are also incapable of reproducing the different raindrop mean sizes from deep convection and shallow convection. Moreover, simulations show unrealistic evolutions of raindrop mean size standard deviations as height declines. By investigating the empirical formula and performing sensitivity experiment, the constrained DSD variability in heavy rainfall is largely blamed on the insufficient parameterizations of the self‐collection (breakup) processes. Plain Language Summary Surface rainfall intensity is determined by raindrop mean size and number concentration. Realistic simulation of raindrop mean size and number concentration in numerical models is one important step to guarantee the accuracy of quantitative precipitation forecast. In this study, we evaluate the value distributions of simulated raindrop mean size and number concentration in an extremely heavy rainfall event. We found that numerical models produce narrower value ranges of raindrop mean size in heavy rainfall compared to observations. Especially as heavy rainfall intensity increases, the simulated raindrop mean size can even be restricted around a specific value. Further investigations show that insufficient modeling of raindrop collide processes in heavy rainfall are likely to be the reason of unrealistic raindrop simulations. Key Points Variability of simulated raindrop size distributions (DSDs) from multiple microphysics schemes are evaluated in the “21·7” Henan EHR event Constrained DSDs are identified in the simulations as the enhancing of rainfall intensity Restricted DSD variability in heavy rainfall is largely attributed to the uncertainties in self‐collection (breakup) processes

The 2-Dimensional Video Disdrometer (2DVD) is a commonly used tool for exploring rain microphysics and for validating remotely sensed rain retrievals. Recent work has revealed a persistent anomaly in 2DVD data. Early investigations of this anomaly concluded that the resulting errors in rain measurement were modest, but the methods used to flag anomalous data were not optimized, and related considerations associated with the sample sensing area were not fully investigated. Here, we (i) refine the anomaly-detecting algorithm for increased sensitivity and reliability and (ii) develop a related algorithm for refining the estimate of sample sensing area for all detected drops, including those not directly impacted by the anomaly. Using these algorithms, we explore the corrected data to measure any resulting changes to estimates of bulk rainfall statistics from two separate 2DVDs deployed in South Carolina combining for approximately 10 total years of instrumental uptime. Analysis of this data set consisting of over 200 million drops shows that the error induced in estimated total rain accumulations using the manufacturer-reported area is larger than the error due to considerations related to the anomaly. The algorithms presented here imply that approximately 4.2% of detected drops are spurious and the mean reported effective sample area for drops believed to be correctly detected is overestimated by ~8.5%. Simultaneously accounting for all of these effects suggests that the total accumulated rainfall in the data record is approximately 1.1% larger than the raw data record suggests.

Quantitative precipitation estimates (QPE) can be further improved using estimation algorithms derived from localized raindrop size distribution (DSD) observations. In this study, DSD measurements from two disdrometer stations within Metro Manila during the Southwest monsoon (SWM) period were used to investigate the microphysical properties of rainfall and develop localized dual-polarimetric relations for different radar bands and rainfall types. Observations show that the DSD in Metro Manila is more distributed to larger diameters compared to Southern Luzon and neighboring countries and regions in the Western Pacific. This is reflected by the relatively higher mass-weighted mean diameter ( D m ) and smaller shape (μ) and slope (Λ) parameters measured in the region. The average values of D m and normalized intercept parameter ( N w ) in convective rain samples also suggest that convective rains in Metro Manila are highly influenced by both continental and oceanic convective processes. Dual-polarimetric variables simulated using the T-matrix scattering method showed good agreement with disdrometer-derived reflectivity ( Z H ) values. The 0.5 dB and 0.3° km −1 thresholds for the differential reflectivity ( Z DR ) and specific differential phase ( K DP ) based on the blended algorithm of Cifelli (J Atmos Ocean Technol 28:352-364, 2011) and Thompson et al. (2017) are proven to be useful since the utility of the dual-polarimetric variables as rainfall estimators are shown to have dependencies on the radar band and rainfall type. Evaluation of the QPE products with respect to the C-band shows that R (K DP , Z DR ) has the best performance among the dual-pol relations and statistically outperformed the conventional Marshall & Palmer relation [ R ( Z MP )]. The results show that dual-polarimetric variables such as Z DR and K DP can better represent the DSD properties compared to one-dimensional Z , hence providing more accurate QPE products than the conventional R ( Z ) relations.

Seasonal variations of rainfall microphysics in East China are investigated using data from the observations of a two-dimensional video disdrometer and a vertically pointing micro rain radar. The precipitation and rain drop size distribution (DSD) characteristics are revealed for different rain types and seasons. Summer rainfall is dominated by convective rain, while during the other seasons the contribution of stratiform rain to rainfall amount is equal to or even larger than that of convective rain. The mean mass-weighted diameter versus the generalized intercept parameter pairs of convective rain are plotted roughly around the “maritime” cluster, indicating a maritime nature of convective precipitation throughout the year in East China. The localized rainfall estimators, i.e., rainfall kinetic energy–rain rate, shape–slope, and radar reflectivity–rain rate relations are further derived. DSD variability is believed to be a major source of diversity of the aforementioned derived estimators. These newly derived relations would certainly improve the accuracy of rainfall kinetic energy estimation, DSD retrieval, and quantitative precipitation estimation in this specific region.

Optical disdrometers are present weather sensors with the ability of providing detailed information on precipitation such as rain intensity, radar reflectivity or kinetic energy, together with discrete information on the particle size and fall velocity distribution (PSVD) of the hydrometeors. Disdrometers constitute a step forward towards a more complete characterization of precipitation, being useful in several research fields and applications. In this article the performance of two extensively used optical disdrometers, the most recent version of OTT Parsivel2 disdrometer and Thies Clima Laser Precipitation Monitor (LPM), is evaluated. During 2 years, four collocated optical disdrometers, two Thies Clima LPM and two OTT Parsivel2, collected up to 100 000 min of data and up to 30 000 min with rain in more than 200 rainfall events, with intensities peaking at 277 mm h-1 in 1 minute. The analysis of these records shows significant differences between both disdrometer types for all integrated precipitation parameters, which can be explained by differences in the raw PSVD estimated by the two sensors. Thies LPM recorded a larger number of particles than Parsivel2 and a higher proportion of small particles than OTT Parsivel2, resulting in higher rain rates and totals and differences in radar reflectivity and kinetic energy. These differences increased greatly with rainfall intensity. Possible causes of these differences, and their practical consequences, are discussed in order to help researchers and users in the choice of sensor, and at the same time pointing out limitations to be addressed in future studies.

The airflow velocity pattern generated by a widely used non-catching precipitation gauge (the Thies laser precipitation monitor or LPM) when immersed in a wind field is investigated using computational fluid dynamics (CFD). The simulation numerically solves the unsteady Reynolds-averaged Navier–Stokes (URANS) equations and the setup is validated against dedicated wind tunnel measurements. The adopted k-ω shear stress transport (SST) turbulence model closely reproduces the flow pattern generated by the complex, non-axisymmetric outer geometry of the instrument. The airflow pattern near the measuring area varies with the wind direction, the most intense recirculating flow and turbulence being observed when the wind blows from the back of the instrument. Quantitative parameters are used to discuss the magnitude of the airflow perturbations with respect to the ideal configuration where the instrument is transparent to the wind. The generated airflow pattern is expected to induce some bias in operational measurements, especially in strong wind conditions. The proposed numerical simulation framework provides a basis to develop correction curves for the wind-induced bias of non-catching gauges, as a function of the undisturbed wind speed and direction.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.