High resolution quantitative precipitation estimation derived from measurement of S-band dual-polarization radar network over southern China

2017 ◽  
Author(s):  
Sheng Chen ◽  
Haonan Chen ◽  
V. Chandrasekar ◽  
Junjun Hu ◽  
Asi Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Southern China ◽  
Dual Polarization ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation

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 Quantitative Precipitation Estimation of the Epic 2013 Colorado Flood Event: Polarization Radar-Based Variational Scheme

2016 ◽  
Vol 55 (7) ◽  
pp. 1477-1495 ◽  
Author(s):  
Wei-Yu Chang ◽  
Jothiram Vivekanandan ◽  
Kyoko Ikeda ◽  
Pay-Liam Lin
Keyword(s):  
Power Law ◽  
Rain Rate ◽  
Flood Event ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Observational Error ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
Event Based

AbstractThe accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the “R(ZHH, ZDR)” power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R(ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD).

scholarly journals Quantitative Precipitation Estimation using Overlapped Area in Radar Network

2017 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Jeongho Choi ◽  
Myoungsun Han ◽  
Chulsang Yoo ◽  
Jiho Lee
Keyword(s):  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation

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

scholarly journals Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 30 ◽  
Author(s):  
Yonghua Zhang ◽  
Liping Liu ◽  
Shuoben Bi ◽  
Zhifang Wu ◽  
Ping Shen ◽  
...  
Keyword(s):  
Drop Size ◽  
Southern China ◽  
Average Diameter ◽  
Squall Line ◽  
Polarimetric Radar ◽  
Quantitative Precipitation Estimation ◽  
Squall Lines ◽  
Fitting Method ◽  
Precipitation Estimation ◽  
Differential Reflectivity

Typhoon rainstorms often cause disasters in southern China. Quantitative precipitation estimation (QPE) with the use of polarimetric radar can improve the accuracy of precipitation estimation and enhance typhoon defense ability. On the basis of the observed drop size distribution (DSD) of raindrops, a comparison is conducted among the DSD parameters and the polarimetric radar observation retrieved from DSD in five typhoon and three squall line events that occurred in southern China from 2016 to 2017. A new piecewise fitting method (PFM) is used to develop the QPE estimators for landfall typhoons and squall lines. The performance of QPE is evaluated by two fitting methods for two precipitation types using DSD data collected. Findings indicate that the number concentration of raindrops in typhoon precipitation is large and the average diameter is small, while the raindrops in squall line rain have opposite characteristics. The differential reflectivity (ZDR) and specific differential phase (KDP) in these two precipitation types increase slowly with the reflectivity factor (ZH), whereas the two precipitation types have different ZDR and KDP in the same ZH. Thus, it is critical to fit the rainfall estimator for different precipitation types. Enhanced estimation can be obtained using the estimators for specific precipitation types, whether the estimators are derived from the conventional fitting method (CFM) or PFM, and the estimators fitted using the PFM can produce better results. The estimators for the developed polarimetric radar can be used in operational QPE and quantitative precipitation foresting, and they can improve disaster defense against typhoons and heavy rains.

scholarly journals An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

2017 ◽  
Vol 18 (4) ◽  
pp. 917-937 ◽  
Author(s):  
Haonan Chen ◽  
V. Chandrasekar ◽  
Renzo Bechini
Keyword(s):  
Spatial Coherence ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Field Campaign ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Ground Validation ◽  
Single Polarization ◽  
The Impact ◽  
Self Aggregation

Abstract Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

scholarly journals The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

Geomatics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 347-368
Author(s):  
Tomeu Rigo ◽  
Maria Carmen Llasat ◽  
Laura Esbrí
Keyword(s):  
Quality Control ◽  
Weather Radar ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Daily Data ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
Point Data ◽  
Single Polarization

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

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