scholarly journals Raindrop Size Distributions and Rain Characteristics Observed by a PARSIVEL Disdrometer in Beijing, Northern China

2019 ◽  
Vol 11 (12) ◽  
pp. 1479 ◽  
Author(s):  
Ji ◽  
Chen ◽  
Li ◽  
Chen ◽  
Xiao ◽  
...  
Keyword(s):  
Northern China ◽  
Rain Gauge ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Precipitation Estimation ◽  
Median Volume ◽  
Radar Applications ◽  
Raindrop Size

Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume diameter D0 and the normalized intercept parameter Nw. The separation line between convective and stratiform rain is almost equivalent to rain rate at 8.6 mm h–1 and radar reflectivity at 36.8 dBZ. Convective rain in Beijing shows distinct seasonal variations in log10Nw–D0 domain. X-band dual-polarization variables are simulated using the T-matrix method to derive radar-based quantitative precipitation estimation (QPE) estimators, and rainfall products at hourly scale are evaluated for four radar QPE estimators using collocated but independent rain gauge observations. This study also combines the advantages of individual estimators based on the thresholds on polarimetric variables. Results show that the blended QPE estimator has better performance than others. The rainfall microphysical analysis presented in this study is expected to facilitate the development of a high-resolution X-band radar network for urban QPE applications.

scholarly journals Assessing the benefits of specific attenuation for quantitative precipitation estimation with a C-band radar network

2021 ◽  
Author(s):  
Ju-Yu Chen ◽  
Silke Trömel ◽  
Alexander Ryzhkov ◽  
Clemens Simmer
Keyword(s):  
Rain Gauge ◽  
Horizontal Polarization ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
X Band ◽  
Precipitation Estimation ◽  
Drop Size Distributions ◽  
Differential Reflectivity ◽  
Rainfall Retrieval

AbstractRecent advances demonstrate the benefits of radar-derived specific attenuation at horizontal polarization (AH) for quantitative precipitation estimation (QPE) at S and X band. To date the methodology has, however, not been adapted for the widespread European C-band radars such as installed in the network of the German Meteorological Service (DWD, Deutscher Wetterdienst). Simulations based on a large dataset of drop size distributions (DSDs) measured over Germany are performed to investigate the DSD dependencies of the attenuation parameter αH for the AH estimates. The normalized raindrop concentration (Nw) and the change of differential reflectivity (ZDR) with reflectivity at horizontal polarization (ZH) are used to categorize radar observations into regimes for which scan-wise optimized αH values are derived. For heavier continental rain with ZH > 40 dBZ, the AH-based rainfall retrieval R(AH) is combined with a rainfall estimator using a substitute of specific differential phase (). We also assess the performance of retrievals based on specific attenuation at vertical polarization (AV). Finally, the regime-adapted hybrid QPE algorithms are applied to four convective cases and one stratiform case from 2017 to 2019, and compared to DWD’s operational RAdar-OnLine-ANeichung (RADOLAN) RW rainfall product, which is based on Zh only but adjusted to rain gauge measurements. For the convective cases, our hybrid retrievals outperform the traditional R(Zh) and pure R(AH/V) retrievals with fixed αH/V values when evaluated with gauge measurements and outperform RW when evaluated by disdrometer measurements. Potential improvements using ray-wise αH/V and segment-wise applications of the ZPHI method along the radials are discussed.

scholarly journals Observed Response of the Raindrop Size Distribution to Changes in the Melting Layer

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 319 ◽  
Author(s):  
Patrick Gatlin ◽  
Walter Petersen ◽  
Kevin Knupp ◽  
Lawrence Carey
Keyword(s):  
Size Distribution ◽  
Basic Assumption ◽  
Weighted Mean ◽  
Radar Rainfall ◽  
Melting Layer ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Stratiform Rainfall

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

scholarly journals Quantitative Precipitation Estimation by Combining Rain gauge and Meteorological Radar Network in Viet Nam

2020 ◽  
Vol 5 (5) ◽  
pp. 36-50
Author(s):  
Chiho Kimpara ◽  
Michihiko Tonouchi ◽  
Bui Thi Khanh Hoa ◽  
Nguyen Viet Hung ◽  
Nguyen Minh Cuong ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Viet Nam ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation ◽  
Meteorological Radar

A Bayesian Approach for Integrated Raindrop Size Distribution (DSD) Retrieval on an X-Band Dual-Polarization Radar Network

2016 ◽  
Vol 33 (2) ◽  
pp. 377-389 ◽  
Author(s):  
Eiichi Yoshikawa ◽  
V. Chandrasekar ◽  
Tomoo Ushio ◽  
Takahiro Matsuda
Keyword(s):  
Size Distribution ◽  
Estimation Accuracy ◽  
Dual Polarization ◽  
Estimation Errors ◽  
Axis Ratio ◽  
Radar Network ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Radar Measurements ◽  
Raindrop Size

AbstractA raindrop size distribution (DSD) retrieval method for a weather radar network consisting of several X-band dual-polarization radars is proposed. An iterative maximum likelihood (ML) estimator for DSD retrieval in a single radar was developed in the authors’ previous work, and the proposed algorithm in this paper extends the single-radar retrieval to radar-networked retrieval, where ML solutions in each single-radar node are integrated based on a Bayesian scheme in order to reduce estimation errors and to enhance accuracy. Statistical evaluations of the proposed algorithm were carried out using numerical simulations. The results with eight radar nodes showed that the bias and standard errors are −0.05 and 0.09 in log(Nw); and Nw (mm−1 m−3) and 0.04 and 0.09 in D0 (mm) in an environment with fluctuations in dual-polarization radar measurements (normal distributions with standard deviations of 0.8 dBZ, 0.2 dB, and 1.5° in ZHm, ZDRm, and ΦDPm, respectively). Further error analyses indicated that the estimation accuracy depended on the number of radar nodes, the ranges of varying μ, the raindrop axis ratio model, and the system bias errors in dual-polarization radar measurements.

scholarly journals Toward a Polarimetric Radar Classification Scheme for Coalescence-Dominant Precipitation: Application to Complex Terrain

2017 ◽  
Vol 18 (12) ◽  
pp. 3199-3215 ◽  
Author(s):  
Leonardo Porcacchia ◽  
P. E. Kirstetter ◽  
J. J. Gourley ◽  
V. Maggioni ◽  
B. L. Cheong ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Classification Scheme ◽  
Flash Flood ◽  
Radar Data ◽  
High Elevation ◽  
Rain Gauge ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
X Band ◽  
Precipitation Estimation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.

scholarly journals Exploring the potential of utilizing high resolution X-band radar for urban rainfall estimation

2016 ◽  
Author(s):  
Wen-Yu Yang ◽  
Guang-Heng Ni ◽  
You-Cun Qi ◽  
Yang Hong ◽  
Ting Sun
Keyword(s):  
High Resolution ◽  
Rainfall Estimation ◽  
Wind Drift ◽  
Common Error ◽  
Error Corrections ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Rainfall Estimates

Abstract. X-band-radar-based quantitative precipitation estimation (QPE) system is increasingly gaining interest thanks to its strength in providing high spatial resolution rainfall information for urban hydrological applications. However, prior to such applications, a variety of errors associated with X-band radars are mandatory to be corrected. In general, X-band radar QPE systems are affected by two types of errors: 1) common errors (e.g. mis-calibration, beam blockage, attenuation, non-precipitation clutter, variations in the raindrop size distribution) and 2) “wind drift” errors resulting from non-vertical falling of raindrops. In this study, we first assess the impacts of different corrections of common error using a dataset consisting of one-year reflectivity observations collected at an X-band radar site and a distrometer along with rainfall observations in Beijing urban area. The common error corrections demonstrate promising improvements in the rainfall estimates, even though an underestimate of 24.6% by the radar QPE system in the total accumulated rainfall still exists as compared with gauge observations. The most significant improvement is realized by beam integration correction. The DSD-related corrections (i.e., convective–stratiform classification and local Z-R relationship) also lead to remarkable improvement and highlight the necessity of deriving the localized Z-R relationships for specific rainfall systems. The effectiveness of wind drift correction is then evaluated for a fast-moving case, whose results indicate both the total accumulation and the temporal characteristics of the rainfall estimates can be improved. In conclusion, considerable potential of X-band radar in high-resolution rainfall estimation can be realized by necessary error corrections.

scholarly journals Quantitative Precipitation Estimation in the CASA X-band Dual-Polarization Radar Network

2010 ◽  
Vol 27 (10) ◽  
pp. 1665-1676 ◽  
Author(s):  
Yanting Wang ◽  
V. Chandrasekar
Keyword(s):  
Field Experiments ◽  
Estimation Method ◽  
Dual Polarization ◽  
Spatiotemporal Resolution ◽  
Radar Observations ◽  
Engineering Research ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
X Band ◽  
Precipitation Estimation

Abstract This paper presents the sensing aspects and performance evaluation of the quantitative precipitation estimation (QPE) system in an X-band dual-polarization radar network developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center. CASA’s technology enables precipitation observation close to the ground and QPE is one of the important applications. With expanding urbanization all over the world, vulnerability to floods has increased from intense rainfall such as urban flash floods. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. Derivation of QPE from radar observations is a challenging process, in which the use of dual-polarization radar variables is advantageous. At X band, the specific differential propagation phase (Kdp) between the orthogonal linear polarization states is particularly appealing. The Kdp field is robustly acquired using an adaptive estimation method, and a simple R(Kdp) relation is used to perform precipitation estimation in this X-band radar network. Radar observations and QPE from multiyear field experiments are used to demonstrate the performance of rainfall estimation from the single-parameter Kdp-based rainfall product. The operational feasibility of radar QPE using an X-band radar network is critically assessed.

scholarly journals Uncertainty in Retrieving Raindrop Size Distribution from Polarimetric Radar Measurements

2019 ◽  
Vol 36 (4) ◽  
pp. 585-605 ◽  
Author(s):  
Hao Huang ◽  
Guifu Zhang ◽  
Kun Zhao ◽  
Su Liu ◽  
Long Wen ◽  
...  
Keyword(s):  
Size Distribution ◽  
Measurement Errors ◽  
Distribution Model ◽  
Fundamental Parameter ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
Raindrop Size

AbstractDrop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (KDP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.

scholarly journals A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

2021 ◽  
Vol 13 (1) ◽  
pp. 154
Author(s):  
Ju-Yu Chen ◽  
Wei-Yu Chang ◽  
Pao-Liang Chang
Keyword(s):  
Data Quality ◽  
Radar Data ◽  
Systematic Bias ◽  
Data Quality Control ◽  
Dual Polarization ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Northern Taiwan

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(ZHH) and about 5~15% for R(KDP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5–7.0% and 3.5–8.5% in R(KDP) and R(KDP, ZDR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1–4% improvements in R(KDP, ZDR) at the C-band radar, which has a larger scanning temporal gap (up to 10 min). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs.

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