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Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research. [ABSTRACT FROM AUTHOR]

The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious. [ABSTRACT FROM AUTHOR]

In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in the Tianshan Mountains, a typical arid area in China, we investigated the microphysical characteristics of DSD across R classes and dual-polarimetric radar QPE relationships across radar wavelengths and R classes, based on the DSD data from a PARSIVEL2 disdrometer at Zhaosu in the Tianshan Mountains during the summers of 2020 and 2021. As the R class increased, the DSD became wider and flatter. The mean value of the mass-weighted mean diameters (Dm) increased, while the mean value of logarithm normalized intercept parameters (log10 Nw) decreased after increasing from C1 to C3, as the R class increased. The largest contributions to R and the radar reflectivity factor from large raindrops (diameter > 3 mm) accounted for approximately 50% and 97%, respectively, while 84% of the total raindrops were small raindrops (diameter < 1 mm). Dual-polarization radars—horizontal polarization reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp)—were retrieved based on the DSD data using the T-matrix scattering method. The DSD-based polarimetric radar QPE relations of a single-parameter (R(Zh), R(Kdp)), and double-parameters (R(Zh,Zdr), R(Kdp,Zdr)) on the S-, C-, and X-bands were derived and evaluated. Overall, the performance of the R(Kdp) (R(Kdp,Zdr)) scheme was better than that of R(Zh) (R(Zh,Zdr)) for the QPE in the three bands. Furthermore, we have for the first time confirmed and quantified the performance differences in the QPE relationship of dual-polarization radars under different schemes, radar wavelengths, and R classes in typical arid areas of China. Therefore, selecting an appropriate dual-polarization radar band and QPE scheme for different R classes is necessary to improve the QPE ability compared with an independent scheme under all R classes. [ABSTRACT FROM AUTHOR]

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model. [ABSTRACT FROM AUTHOR]

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (KDP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by KDP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(KDP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the ZH-ZDR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall. [ABSTRACT FROM AUTHOR]

Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially during extreme events such as typhoons, since the operational dual-polarization system upgrade only happened recently. This paper extends a polarimetric radar rainfall system for local applications during typhoons in southern China and conducts comprehensive studies about QPE and precipitation microphysics. Observations from S-band dual-polarization radar in Guangdong Province during three typhoon events in 2017 are examined to demonstrate the enhanced radar rainfall performance. The microphysical properties of hydrometeors during typhoon events are analyzed through raindrop size distribution (DSD) data and polarimetric radar measurements. The stratiform precipitation in typhoons presents lower mean raindrop diameter and lower raindrop concentration than that of the convection precipitation. The rainfall estimates from the adapted radar rainfall algorithm agree well with rainfall measurements from rain gauges. Using the rain gauge data as references, the maximum normalized mean bias ( N M B ) of the adapted radar rainfall algorithm is 20.27%; the normalized standard error ( N S E ) is less than 40%; and the Pearson's correlation coefficient ( C C ) is higher than 0.92. For the three typhoon events combined, the N S E and N M B are 36.66% and -15.78%, respectively. Compared with several conventional radar rainfall algorithms, the adapted algorithm based on local rainfall microphysics has the best performance in southern China. [ABSTRACT FROM AUTHOR]

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff. [ABSTRACT FROM AUTHOR]

Raindrop size distributions (DSDs) are the microphysical characteristics of raindrop spectra. Rainfall characterization is important to: (1) provide information on extreme rate, thus, it has an impact on rainfall related hazard; (2) provide data for indirect observation, model and forecast; (3) calibrate and validate the parameters in radar reflectivity-rainfall intensity (Z-R) relationships (quantitative estimate precipitation, QPE) and the mechanism of precipitation erosivity. In this study, the one-year datasets of raindrop spectra were measured by an OTT Parsivel-2 Disdrometer placed in Yulin, Shaanxi Province, China. At the same time, four TE525MM Gauges were also used in the same location to check the disdrometer-measured rainfall data. The theoretical formula of raindrop kinetic energy-rainfall intensity (KE-R) relationships was derived based on the DSDs to characterize the impact of precipitation characteristics and environmental conditions on KE-R relationships in semi-arid areas. In addition, seasonal rainfall intensity curves observed by the disdrometer of the area with application to erosion were characterized and estimated. The results showed that after quality control (QC), the frequencies of raindrop spectra data in different seasons varied, and rainfalls with R within 0.5–5 mm/h accounted for the largest proportion of rainfalls in each season. The parameters in Z-R relationships (Z = aRb) were different for rainfall events of different seasons (a varies from 78.3–119.0, and b from 1.8–2.1), and the calculated KE-R relationships satisfied the form of power function KE = ARm, in which A and m are parameters derived from rainfall shape factor μ. The sensitivity analysis of parameter A with μ demonstrated the applicability of the KE-R formula to different precipitation processes in the Yulin area. [ABSTRACT FROM AUTHOR]

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms. [ABSTRACT FROM AUTHOR]

Polarimetric radar and disdrometer observations obtained during the 2014 Observation, Prediction, and Analysis of Severe Convection of China (OPACC) field campaign are used in this study to investigate the microphysical characteristics of three primary types of organized intense rainfall events (meiyu rainband, typhoon outer rainband, and squall line) in eastern China. Drop size distributions (DSDs) of these three events on the ground are derived from measurements of a surface disdrometer, while the corresponding three-dimensional microphysical structures are obtained from the Nanjing University C-band polarimetric radar (NJU-CPOL). Although the environmental moisture and instability conditions are different, all three events possess relatively high freezing level favorable for warm-rain processes where the high medium to small raindrop concentration at low levels is consistent with the high surface rainfall rates. Convection is tallest in the squall line where abundant ice-phase processes generate large amounts of rimed particles (graupel and hail) above the freezing level and the largest surface raindrops are present among these three events. The storm tops of both the typhoon and meiyu rainbands are lower than that in the squall line, composed of less active ice processes above the freezing level. The typhoon rainrate is more intense than that of meiyu, enhanced by higher coalescence efficiency. A revised generalized intercept parameter versus mass-weighted mean diameter (Nw-Dm) space diagram is constructed to describe the DSD distributions over the three events and illustrate the relative DSD positions for heavy precipitation. DSDs of these intense rainfall convections observed in this midlatitude region of eastern Asia somewhat represent the typical DSD characteristics in low latitudes, suggesting that the parameterization of microphysical characteristics in eastern China in numerical models needs to be further investigated to improve rain fall forecasts in these heavy rainfall events. [ABSTRACT FROM AUTHOR]