scholarly journals Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Young-A Oh ◽  
DaeHyung Lee ◽  
Sung-Hwa Jung ◽  
Kyung-Yeub Nam ◽  
GyuWon Lee
Keyword(s):  
Attenuation Correction ◽  
Random Error ◽  
Estimation Method ◽  
Rain Gauge ◽  
Spatial Average ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Gauge Network

The effects of attenuation correction in rainfall estimation with X-band dual-polarization radar were investigated with a dense rain gauge network. The calibration bias in reflectivity (ZH) was corrected using a self-consistency principle. The attenuation correction ofZHand the differential reflectivity (ZDR) were performed by a path integration method. After attenuation correction,ZHandZDRwere significantly improved, and their scatter plots matched well with the theoretical relationship betweenZHandZDR. The comparisons between the radar rainfall estimation and the rain gauge rainfall were investigated using the bulk statistics with different temporal accumulations and spatial averages. The bias significantly improves from 70% to 0% withR(ZH). However, the improvement withR(ZH,ZDR)was relatively small, from 3% to 1%. This indicated that rainfall estimation using a polarimetric variable was more robust at attenuation than was a single polarimetric variable method. The bias did not show improvement in comparisons between the temporal accumulations or the spatial averages in either rainfall estimation method. However, the random error improved from 68% to 25% with different temporal accumulations or spatial averages. This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.

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.

X-band Polarimetric Radar Rainfall Measurements in Keys Area Microphysics Project

2006 ◽  
Vol 63 (1) ◽  
pp. 187-203 ◽  
Author(s):  
Emmanouil N. Anagnostou ◽  
Mircea Grecu ◽  
Marios N. Anagnostou
Keyword(s):  
Phase Shift ◽  
Attenuation Correction ◽  
Doppler Radar ◽  
Model Parameters ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Differential Phase ◽  
X Band ◽  
Filter Noise

Abstract The Keys Area Microphysics Project (KAMP), conducted as part of NASA’s Fourth Convective and Moisture Experiment (CAMEX-4) in the lower Keys area, deployed a number of ground radars and four arrays of rain gauge and disdrometer clusters. Among the various instruments is an X-band dual-polarization Doppler radar on wheels (XPOL), contributed by the University of Connecticut. XPOL was used to retrieve rainfall rate and raindrop size distribution (DSD) parameters to be used in support of KAMP science objectives. This paper presents the XPOL measurements in KAMP and the algorithm developed for attenuation correction and estimation of DSD model parameters. XPOL observations include the horizontal polarization reflectivity ZH, differential reflectivity ZDR, and differential phase shift ΦDP. Here, ZH and ZDR were determined to be positively biased by 3 and 0.3 dB, respectively. A technique was also applied to filter noise and correct for potential phase folding in ΦDP profiles. The XPOL attenuation correction uses parameterizations that relate the path-integrated specific (differential) attenuation along a radar ray to the filtered-ΦDP (specific attenuation) profile. Attenuation-corrected ZH and specific differential phase shift (derived from filtered ΦDP profiles) data are then used to derive two parameters of the normalized gamma DSD model, that is, intercept (Nw) and mean drop diameter (D0). The third parameter (shape parameter μ) is calculated using a constrained μ–Λ relationship derived from the measured raindrop spectra. The XPOL attenuation correction is evaluated using coincidental nonattenuated reflectivity fields from the Key West Weather Surveillance Radar-1988 Doppler (WSR-88D), while the DSD parameter retrievals are statistically assessed using DSD parameters calculated from the measured raindrop spectra. Statistics show that XPOL DSD parameter estimation is consistent with independent observations. XPOL estimates of water content and Nw are also shown to be consistent with corresponding retrievals from matched ER-2 Doppler radar (EDOP) profiling observations from the 19 September airborne campaign. Results shown in this paper strengthen the applicability of X-band dual-polarization high resolution observations in cloud modeling and precipitation remote sensing studies.

scholarly journals Rainfall-induced attenuation correction for two operational dual-polarization C-band radars in the Netherlands

2021 ◽  
Author(s):  
Aart Overeem ◽  
Hylke de Vries ◽  
Hassan Al Sakka ◽  
Remko Uijlenhoet ◽  
Hidde Leijnse
Keyword(s):  
Attenuation Correction ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Numerical Weather Prediction Model ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Freezing Level ◽  
Weather Radars ◽  
Data Archives ◽  
Hourly Rainfall

AbstractThe Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2-D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. In order to improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) Modified Kraemer (MK) method, i.e. Hitschfeld-Bordan where parameters of the power-law Zh–kh relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZh and attenuation correction is limited to 10 dB; 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase Kdp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

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 Close-range radar rainfall estimation and error analysis

2016 ◽  
Vol 9 (8) ◽  
pp. 3837-3850 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. Hazenberg ◽  
R. Uijlenhoet
Keyword(s):  
Positive Impact ◽  
Rain Gauge ◽  
Minor Role ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Sources Of Error ◽  
Ground Clutter ◽  
Received Power ◽  
Radar Calibration ◽  
Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

scholarly journals Integrated Correction Algorithm for X Band Dual-Polarization Radar Reflectivity Based on CINRAD/SA Radar

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 119
Author(s):  
Chao Wang ◽  
Chong Wu ◽  
Liping Liu ◽  
Xi Liu ◽  
Chao Chen
Keyword(s):  
Attenuation Correction ◽  
Fixed Ratio ◽  
Water Layer ◽  
Resonance Scattering ◽  
Rain Attenuation ◽  
Correction Algorithm ◽  
Dual Polarization ◽  
Differential Phase ◽  
X Band ◽  
Ratio Correction

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China’s New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm.

scholarly journals Evaluation of X-Band Dual-Polarization Radar-Rainfall Estimates from OLYMPEX

2019 ◽  
Vol 20 (9) ◽  
pp. 1941-1959 ◽  
Author(s):  
Yagmur Derin ◽  
Emmanouil Anagnostou ◽  
Marios Anagnostou ◽  
John Kalogiros
Keyword(s):  
Rainfall Variability ◽  
Dual Polarization ◽  
Significant Information ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
X Band ◽  
Error Characterization ◽  
Median Volume ◽  
Multiparameter Measurements

Abstract The difficulty of representing high rainfall variability over mountainous areas using ground-based sensors is an open problem in hydrometeorology. Observations from locally deployed dual-polarization X-band radar have the advantage of providing multiparameter measurements near ground that carry significant information useful for estimating drop size distribution (DSD) and surface rainfall rate. Although these measurements are at fine spatiotemporal scale and are less inhibited by complex topography than operational radar network observations, uncertainties in their estimates necessitate error characterization based upon in situ measurements. During November 2015–February 2016, a dual-polarized Doppler on Wheels (DOW) X-band radar was deployed on the Olympic Peninsula of Washington State as part of NASA’s Olympic Mountain Experiment (OLYMPEX). In this study, rain gauges and disdrometers from a dense network positioned within 40 km of DOW are used to evaluate the self-consistency and accuracy of the attenuation and brightband/vertical profile corrections, and rain microphysics estimation by SCOP-ME, an algorithm that uses optimal parameterization and best-fitted functions of specific attenuation coefficients and DSD parameters with radar polarimetric measurements. In addition, the SCOP-ME precipitation microphysical retrievals of median volume diameter D0 and normalized intercept parameter NW are evaluated against corresponding parameters derived from the in situ disdrometer spectra observations.

Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project

2015 ◽  
Vol 16 (4) ◽  
pp. 1658-1675 ◽  
Author(s):  
Bong-Chul Seo ◽  
Brenda Dolan ◽  
Witold F. Krajewski ◽  
Steven A. Rutledge ◽  
Walter Petersen
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Superior Performance ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Precipitation Estimation ◽  
Improved Performance ◽  
Rainfall Estimates

Abstract This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (Z–R) relation that might lead to substantial underestimation for the presented case.

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