Evaluation of Dual Polarization Rainfall Estimation Algorithm Applicability in Korea: A Case Study on Biseulsan Radar

2016 ◽  
Vol 16 (1) ◽  
pp. 281-293
Author(s):  
Gildo Kim ◽  
Jung Mo Ku ◽  
Yonghun Ro ◽  
Chulsang Yoo
Keyword(s):  
Estimation Algorithm ◽  
Dual Polarization ◽  
Rainfall Estimation

A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

2011 ◽  
Vol 28 (3) ◽  
pp. 352-364 ◽  
Author(s):  
R. Cifelli ◽  
V. Chandrasekar ◽  
S. Lim ◽  
P. C. Kennedy ◽  
Y. Wang ◽  
...  
Keyword(s):  
Estimation Algorithm ◽  
State University ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Differential Phase ◽  
Precipitation Estimation ◽  
The One ◽  
Differential Reflectivity

Abstract The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) have advantages over traditional Z–R methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume. An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Zh, Zdr, and Kdp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds. In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice. Both the data collected from the S-band Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

An Adaptive Rainfall Estimation Algorithm for Dual-polarization Radar

2022 ◽  
pp. 1-1
Author(s):  
Leilei Kou ◽  
Jiaqi Tang ◽  
Zhixuan Wang ◽  
Yinfeng Jiang ◽  
Zhigang Chu
Keyword(s):  
Estimation Algorithm ◽  
Dual Polarization ◽  
Rainfall Estimation

A Regression-Free Rainfall Estimation Algorithm for Dual-Polarization Radars

2018 ◽  
Vol 35 (8) ◽  
pp. 1701-1721 ◽  
Author(s):  
Bin Pei ◽  
Firat Y. Testik
Keyword(s):  
Cost Function ◽  
Rain Rate ◽  
Estimation Algorithm ◽  
Cost Functions ◽  
Size Distributions ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Raindrop Size ◽  
Rain Rates

AbstractIn this study a new radar rainfall estimation algorithm—rainfall estimation using simulated raindrop size distributions (RESID)—was developed. This algorithm development was based upon the recent finding that measured and simulated raindrop size distributions (DSDs) with matching triplets of dual-polarization radar observables (i.e., horizontal reflectivity, differential reflectivity, and specific differential phase) produce similar rain rates. The RESID algorithm utilizes a large database of simulated gamma DSDs, theoretical rain rates calculated from the simulated DSDs, the corresponding dual-polarization radar observables, and a set of cost functions. The cost functions were developed using both the measured and simulated dual-polarization radar observables. For a given triplet of measured radar observables, RESID chooses a suitable cost function from the set and then identifies nine of the simulated DSDs from the database that minimize the value of the chosen cost function. The rain rate associated with the given radar observable triplet is estimated by averaging the calculated theoretical rain rates for the identified simulated DSDs. This algorithm is designed to reduce the effects of radar measurement noise on rain-rate retrievals and is not subject to the regression uncertainty introduced in the conventional development of the rain-rate estimators. The rainfall estimation capability of our new algorithm was demonstrated by comparing its performance with two benchmark algorithms through the use of rain gauge measurements from the Midlatitude Continental Convective Clouds Experiment (MC3E) and the Olympic Mountains Experiment (OLYMPEx). This comparison showed favorable performance of the new algorithm for the rainfall events observed during the field campaigns.

A Study on DTW-based RUL Estimation Algorithm of Propulsion Motor: Case Study

2021 ◽  
Vol 26 (4) ◽  
pp. 386-397
Author(s):  
Junseok Kim ◽  
Gangbok Lee ◽  
Hoesun Hwang ◽  
Jisoo Ahn ◽  
Jeongrim Oh ◽  
...  
Keyword(s):  
Estimation Algorithm

scholarly journals Precipitation Classification and Quantification Using X-Band Dual-Polarization Weather Radar: Application in the Hydrometeorology Testbed

2013 ◽  
Vol 30 (9) ◽  
pp. 2108-2120 ◽  
Author(s):  
S. Lim ◽  
R. Cifelli ◽  
V. Chandrasekar ◽  
S. Y. Matrosov
Keyword(s):  
Quality Control ◽  
Phase Retrieval ◽  
Estimation Method ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Differential Phase ◽  
Melting Region ◽  
X Band ◽  
Fuzzy Logic Technique

Abstract This paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a “fuzzy logic” technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications.

scholarly journals An empirical method to improve rainfall estimation of dual polarization radar using ground measurements

2016 ◽  
Author(s):  
Jungsoo Yoon ◽  
Mi-Kyung Suk ◽  
Kyung-Yeub Nam ◽  
Jeong-Seok Ko ◽  
Hae-Lim Kim ◽  
...  
Keyword(s):  
Empirical Method ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
High Rainfall ◽  
Estimation Algorithms ◽  
Hourly Rainfall ◽  
Constant Altitude ◽  
Position Indicator

Abstract. This study presents an easy and convenient empirical method to optimize polarimetric variables and produce more accurate dual polarization radar rainfall estimation. Weather Radar Center (WRC) in Korea Meteorological Administration (KMA) suggested relations between polarimetric variables (Z–ZDR and Z–KDP) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images composed at 1 km of height were adjusted using the WRC's relations. Then dual polarization radar rainfalls were estimated by six different radar rainfall estimation algorithms, which are using either Z, Z and ZDR, or Z, ZDR and KDP. Accuracy of radar rainfall estimations derived by the six algorithms using the adjusted variables was assessed through comparison with raingauge observations. As a result, the accuracy of the radar rainfall estimation using adjusted polarimetric variables has improved from 50 % to 70 % approximately. Three high rainfall events with more than 40 mm of maximum hourly rainfall were shown the best accuracy on the rainfall estimation derived by using Z, ZDR and KDP. Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and ZDR.

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