scholarly journals DEVELOPING SPACE-TIME STRUCTURE OF RAINDROP SIZE DISTRIBUTION AND QPE METHOD FROM NEW DSD RETRIEVING METHOD USING X-BAND POLARIMETRIC RADAR

2012 ◽  
Vol 68 (4) ◽  
pp. I_367-I_372
Author(s):  
Kosei YAMAGUCHI ◽  
Chiho KIMPARA ◽  
Eiichi NAKAKITA
Keyword(s):  
Size Distribution ◽  
Space Time ◽  
Time Structure ◽  
Polarimetric Radar ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Space Time Structure ◽  
Raindrop Size

scholarly journals Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

2016 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Climatic Regions ◽  
Retrieval Technique ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Double Moment ◽  
Raindrop Size ◽  
A New Technique

Abstract. A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific double-normalised DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.

scholarly journals Improving the rainfall rate estimation in the midstream of the Heihe River Basin using raindrop size distribution

2011 ◽  
Vol 15 (3) ◽  
pp. 943-951 ◽  
Author(s):  
G. Zhao ◽  
R. Chu ◽  
T. Zhang ◽  
J. Li ◽  
J. Shen ◽  
...  
Keyword(s):  
Size Distribution ◽  
Rain Rate ◽  
Tibet Plateau ◽  
Heihe River ◽  
Drop Diameter ◽  
Observation Data ◽  
Polarimetric Radar ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Raindrop Size

Abstract. During the intensive observation period of the Watershed Allied Telemetry Experimental Research (WATER), a total of 1074 raindrop size distribution were measured by the Parsivel disdrometer, the latest state-of-the-art optical laser instrument. Because of the limited observation data in Qinghai-Tibet Plateau, the modelling behaviour was not well done. We used raindrop size distributions to improve the rain rate estimator of meteorological radar in order to obtain many accurate rain rate data in this area. We got the relationship between the terminal velocity of the raindrop and the diameter (mm) of a raindrop: v(D) = 4.67D0.53. Then four types of estimators for X-band polarimetric radar are examined. The simulation results show that the classical estimator R (ZH) is most sensitive to variations in DSD and the estimator R (KDP, ZH, ZDR) is the best estimator for estimating the rain rate. An X-band polarimetric radar (714XDP) is used for verifying these estimators. The lowest sensitivity of the rain rate estimator R (KDP, ZH, ZDR) to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes KDP, is proportional to the 3rd power of the drop diameter. On the other hand, the exponent of the backscatter cross-section, which contributes to ZH, is proportional to the 6th power of the drop diameter. Because the rain rate R is proportional to the 3.57th power of the drop diameter, KDP is less sensitive to DSD variations than ZH.

scholarly journals STUDY ON SUMMER RAINDROP SIZE DISTRIBUTION AND RETRIEVED POLARIMETRIC RADAR PARAMETER OVER LEIZHOU PENINSULA

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 245-250
Author(s):  
Z. B. Zhou ◽  
J. J. Lv ◽  
S. J. Niu
Keyword(s):  
Size Distribution ◽  
Gamma Distribution ◽  
Least Squares Method ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Leizhou Peninsula ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Subtropical Monsoon Climate

Abstract. Leizhou peninsula is located in the south of Guangdong Province, near South China Sea, and has a tropical and subtropical monsoon climate. Based on observed drop size distribution (DSD) data from July 2007 to August 2007 with PARSIVEL disdrometers deployed at Zhanjiang and Suixi, the characterists of DSDs are studied. Non-linear least squares method is used to fit Gamma distribution. Convective and stratiform averaged DSDs are in good agreement with Gamma distribution, especially in stratiform case. Convective average DSDs have a wider spectrum and higher peak. Microphysical parameter differences between convective and stratiform are discussed, convective precipitation has a higher mass-weighted mean diameter (Dm) and generalized intercepts (Nw) in both areas. The constrained relations between Gamma distribution parameter (μ, Λ, N0) is derived. The retrieved polarimetric radar parameter (KDP, ZDR, Zh) have a good self-consistency, which can be used to improve the accuracy of KDP calculation. R-KDP-ZDR is superior to the R-KDP, R-ZDR-Zh in quantitative precipitation estimation (QPE), with a correlation coefficient higher than 0.98.

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.

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