Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

2014 ◽  
Vol 14 (6) ◽  
pp. 105-116 ◽  
Author(s):  
Seong Jin Noh ◽  
Sanghun Lim ◽  
Shin-Woo Choi ◽  
Seok Hwan Hwang ◽  
Dong-Ryul Lee
Keyword(s):  
Dual Polarization ◽  
Estimation Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements

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 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 PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

2020 ◽  
Vol 101 (3) ◽  
pp. E286-E302 ◽  
Author(s):  
Phu Nguyen ◽  
Eric J. Shearer ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Rate ◽  
Extreme Rainfall ◽  
Rate Model ◽  
Spatiotemporal Resolution ◽  
Atmospheric Rivers ◽  
Estimation Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Products Analysis

Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

scholarly journals Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China

2021 ◽  
Vol 13 (16) ◽  
pp. 3157
Author(s):  
Yonghua Zhang ◽  
Shuoben Bi ◽  
Liping Liu ◽  
Haonan Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China ◽  
Rainfall Intensity ◽  
Spatiotemporal Variability ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Radar Observations ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), QPENetV3 performs the best.

scholarly journals Overview of the D3R Observations during the IFloodS Field Experiment with Emphasis on Rainfall Mapping and Microphysics

2015 ◽  
Vol 16 (5) ◽  
pp. 2118-2132 ◽  
Author(s):  
Robert M. Beauchamp ◽  
V. Chandrasekar ◽  
Haonan Chen ◽  
Manuel Vega
Keyword(s):  
United States ◽  
Doppler Radar ◽  
Hydrological Response ◽  
Dual Frequency ◽  
Dual Polarization ◽  
Convective Initiation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Ground Validation ◽  
Ku Band

Abstract The NASA dual-frequency, dual-polarization Doppler radar (D3R) was deployed as part of the GPM Iowa Flood Studies (IFloodS) ground validation field campaign from 1 May through 15 June 2013. The D3R participated in a multi-instrument targeted investigation of convective initiation and hydrological response in the midwestern United States. An overview of the D3R’s calibration and observations is presented. A method for attenuation correction of Ka-band observations using Ku-band results is introduced. Dual-frequency ratio estimates in stratiform rain and ice are presented and compared with theoretical values. Ku-band quantitative precipitation estimation results are validated against IFloodS ground instruments.

scholarly journals Quantification of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China

2020 ◽  
Vol 12 (12) ◽  
pp. 2058
Author(s):  
Qiulei Xia ◽  
Wenjuan Zhang ◽  
Haonan Chen ◽  
Wen-Chau Lee ◽  
Lei Han ◽  
...  
Keyword(s):  
Southern China ◽  
Rain Gauge ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Flood Warning ◽  
Microphysical Properties ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Radar Measurements

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.

scholarly journals Utilization of Specific Attenuation for Tropical Rainfall Estimation in Complex Terrain

2014 ◽  
Vol 15 (6) ◽  
pp. 2250-2266 ◽  
Author(s):  
Yadong Wang ◽  
Pengfei Zhang ◽  
Alexander V. Ryzhkov ◽  
Jian Zhang ◽  
Pao-Liang Chang
Keyword(s):  
Complex Terrain ◽  
Estimation Algorithm ◽  
Rainfall Rate ◽  
Rainfall Estimation ◽  
A Algorithm ◽  
Drop Shape ◽  
Estimation Algorithms ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract To improve the accuracy of quantitative precipitation estimation (QPE) in complex terrain, a new rainfall rate estimation algorithm has been developed and applied on two C-band dual-polarization radars in Taiwan. In this algorithm, the specific attenuation A is utilized in the rainfall rate R estimation, and the parameters used in the R(A) method were estimated using the local drop size distribution (DSD) and drop shape relation (DSR) observations. In areas of complex terrain where the lowest antenna tilt is completely blocked, observations from higher tilts are used in radar QPE. Correction of the vertical profile of rain rate estimated by the R(A) algorithm (VPRA) is applied to account for the vertical variability of rain. It has been found that the VPRA correction improved the accuracy of estimated rainfall in severely blocked areas. The R(A)–VPRA scheme was tested for different precipitation cases including typhoon, stratiform, and convective rain. Compared to existing rainfall estimation algorithms such as rainfall–reflectivity (R–Z) and rainfall–specific differential phase (R–KDP), the new method is able to provide accurate and robust rainfall estimates when the radar reflectivity is miscalibrated or significantly biased by attenuation or when the lower tilt of the radar beam is significantly blocked.

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