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.

scholarly journals An empirical QPE method based on polarimetric variable adjustments

2017 ◽  
Author(s):  
Jungsoo Yoon ◽  
Jong-Sook Park ◽  
Hae-Lim Kim ◽  
Mi-Kyung Suk ◽  
Kyung-Yeub Nam
Keyword(s):  
Empirical Method ◽  
Convective Precipitation ◽  
Estimation Accuracy ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Estimation Algorithms ◽  
Hourly Rainfall ◽  
Using Data ◽  
Constant Altitude

Abstract. This study presents an empirical method for optimizing polarimetric variables in order to improve the accuracy of dual-polarization radar rainfall estimation using data derived from radars operated by different agencies. The empirical method was developed using the Yong-In Testbed (YIT) radar operated by the Korea Meteorological Administration (KMA). The method is based on the determination of relations between polarimetric variables. Relations for Z – ZDR and Z – KDP are derived from the measurements of a two-dimensional video disdrometer installed about 30 km away from the YIT radar. These relations were used to adjust the polarimetric variables of the dual-polarization constant altitude plan position indicator (CAPPI) at a height of 1.5 km. The CAPPI data with the adjusted polarimetric variables were used to estimate rainfalls using three different radar rainfall estimation algorithms. The first algorithm is based on Z, the second on Z and ZDR, and the third on Z, ZDR, and KDP. The accuracy of the radar-estimated rainfall was then assessed using raingauge observations. Three rainfall events with more than 40 mm of maximum hourly rainfall were shown to have the best estimation when the method using Z, ZDR, and KDP was used. However, stratiform precipitation events were better estimated by the algorithm using Z and ZDR. The method was also applied to the data of three radars that belong to KMA and the Ministry of Land, Infrastructure, and Transport. The evaluation was done for six months (May–October) in 2015. The results show an improvement in radar rainfall estimation accuracy for stratiform, frontal, and convective precipitation from approximately 50 % to 70 %.

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.

scholarly journals Country-wide CML rainfall estimation and CML-Radar combination in Germany

2021 ◽  
Author(s):  
Christian Chwala ◽  
Tanja Winterrath ◽  
Maximilian Graf ◽  
Julius Polz ◽  
Harald Kunstmann
Keyword(s):  
Large Scale ◽  
Rain Event ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Large Scale Analysis ◽  
Seasonal And Diurnal Variations ◽  
Hourly Rainfall ◽  
First Results ◽  
Microwave Link

<p>Commercial microwave links (CMLs) have emerged as a valuable source of rainfall information that can complement existing observations. In Germany, we acquire attenuation data from 4000 CMLs with a temporal resolution of one minute. In this contribution we present our results of deriving country-wide rainfall information from these CML data and show the first long-term application of CML data for adjusting the radar rainfall field.</p><p>We present results of a large-scale analysis of our country-wide dataset for one full year (Graf et al. 2020) and compare it with the gauge adjusted radar product RADOLAN-RW from the German Weather Service and the climatologically corrected radar product RADKLIM-YW. Our analysis also compares several different methods for processing CML data, including our recent improvements for the separation of dry and rainy periods in noisy CML attenuation time series based on a convolutional neural network (Polz et al. 2020). We show seasonal and diurnal variations of the performance of CML-derived rainfall data. Promising results are achieved year-round except for periods with solid precipitation. Pearson correlations for the comparison of the hourly rainfall sums reach up to 0.7 for summer months.</p><p>Furthermore, we present results from using the CML rainfall estimates to adjust radar rainfall fields. We extended the RADOLAN-method for radar-gauge adjustment for this purpose. The path-averaged CML rainfall information is compared to the gridded radar rainfall information at the path-intersecting grids. This information is then used in addition to the adjustments derived from rain gauges. We show first results of an hourly adjustment over several months. We further discuss the envisaged operational system for this application and give an outlook on the potential for radar rainfall field adjustments with higher temporal resolutions.</p><p>Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020</p><p>Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020</p>

scholarly journals Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 773
Author(s):  
Cheolhwan You ◽  
Miyoung Kang ◽  
Dong-In Lee
Keyword(s):  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
Stratiform Rain ◽  
Rainfall Variation ◽  
Precipitation Type ◽  
Rainfall Type ◽  
The Impact

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

scholarly journals Radar-rainfall estimation algorithms of Hydro-NEXRAD

2010 ◽  
Vol 13 (2) ◽  
pp. 277-291 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski ◽  
Anton Kruger ◽  
Piotr Domaszczynski ◽  
James A. Smith ◽  
...  
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Hydrologic Modelling ◽  
Estimation Algorithms ◽  
Data Archives ◽  
The Us ◽  
Reflectivity Data ◽  
Prototype Software

Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple (‘Quick Look’) to complex and computing-intensive (‘Hi-Fi’). The ‘Pseudo NWS PPS’ option allows close comparison with the algorithm used operationally by the US National Weather Service. The ‘Custom’ algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications

scholarly journals Dual-Polarization Radar Rainfall Estimation over Tropical Oceans

2018 ◽  
Vol 57 (3) ◽  
pp. 755-775 ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Steven A. Rutledge ◽  
Brenda Dolan ◽  
Merhala Thurai ◽  
V. Chandrasekar
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Warm Pool ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Light Rain ◽  
Tropical Oceans ◽  
Pacific Warm Pool ◽  
Differential Reflectivity

AbstractDual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude (<0.5 dB, <0.3° km−1) and Zdr was lower for a given Kdp or Zh, consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(Kdp), R(Ah), R(Kdp, ζdr), R(z, ζdr), and R(Ah, ζdr), which use linear versions of Zdr and Zh, namely ζdr and z. Except for R(Kdp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(Kdp, ζdr), followed by R(Ah, ζdr) and R(z, ζdr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζdr), R(Kdp), and R(Kdp, ζdr) depending on Zdr > 0.25 dB and Kdp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Zdr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

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