Operational Polarimetric Radar Research Articles

Polarimetric Radar Observations of Biological Scatterers in the Eye of Typhoon Lekima (2019)

AbstractTropical cyclones not only cause strong winds and heavy rainfall, but they can also facilitate the transport of birds and insects from tropical regions to areas along their paths. Before super Typhoon Lekima made landfall in 2019, an operational polarimetric radar in Wenzhou City observed biological scatterers in the typhoon's eye. These scatterers were likely birds and insects that were trapped in the calm center of the typhoon by strong winds and heavy rain. The polarimetric variables of these biological scatterers had specific characteristics: low reflectivity factor (ZH) values with a median of 7.5 dBZ, low cross‐correlation coefficient (ρhv) values with a median of 0.65, large differential reflectivity (ZDR) values with a median of 2.8 dB and a maximum of ∼7.8 dB, and widely‐distributed differential phase (ΦDP) values with 25th and 75th percentiles ranging from 1.8° to 36.0° and a median of 20.1°. When the edge of the eye reached the coastline, the birds and insects landed, with the biological scatterer signature changing from a circular shape to a band shape. We further compared Lekima with Atlantic Hurricane Irene which happened in 2011, and both storms had similar polarimetric characteristics contributed by both birds and insects. However, the biological echoes in Lekima better exhibited the characteristics of birds with a larger proportion of samples with ZDR lower than 5 dB as well as large ΦDP and low ρhv values. This finding could help to understand the role of typhoons in driving biological migration between oceans and/or islands and continents.

Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou

The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation <50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (>50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.

Kinematics and Microphysics of Convection in the Outer Rainband of Typhoon Nida (2016) Revealed by Polarimetric Radar

Abstract This study analyzes the microphysics of convective cells in an outer rainband of Typhoon Nida (2016) using data collected by a newly upgraded operational polarimetric radar in China. The life cycle of these convective cells is divided into three stages: developing, mature, and decaying according to the intensity of the corresponding updraft. Composite analysis shows that deep columns of ZDR and KDP collocate well with the enhanced updraft as the cells develop to their mature stage. A layered microphysical structure is observed in the ice region with riming near the −5°C level within the updraft, aggregation around the −15°C level, and deposition anywhere above the 0°C level. These ice-phase microphysical processes are important pathways of particle growth in the outer rainbands. In particular, riming contributes significantly to surface heavy rainfall. These contrast to previously documented inner rainbands, where warm-rain processes are the predominant pathway of particle growth.

Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational Approach

AbstractQuantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (KDP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (ZH) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.

Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

A modified hydrometeor classification algorithm (HCA) is developed in this study for Chinese polarimetric radars. This algorithm is based on the U.S. operational HCA. Meanwhile, the methodology of statistics-based optimization is proposed including calibration checking, datasets selection, membership functions modification, computation thresholds modification, and effect verification. Zhuhai radar, the first operational polarimetric radar in South China, applies these procedures. The systematic bias of calibration is corrected, the reliability of radar measurements deteriorates when the signal-to-noise ratio is low, and correlation coefficient within the melting layer is usually lower than that of the U.S. WSR-88D radar. Through modification based on statistical analysis of polarimetric variables, the localized HCA especially for Zhuhai is obtained, and it performs well over a one-month test through comparison with sounding and surface observations. The algorithm is then utilized for analysis of a squall line process on 11 May 2014 and is found to provide reasonable details with respect to horizontal and vertical structures, and the HCA results—especially in the mixed rain-hail region—can reflect the life cycle of the squall line. In addition, the kinematic and microphysical processes of cloud evolution and the differences between radar-detected hail and surface observations are also analyzed. The results of this study provide evidence for the improvement of this HCA developed specifically for China.

Rainfall Estimation Using Specific Differential Phase for the First Operational Polarimetric Radar in Korea

To assess the performance of rainfall estimation using specific differential phase observed by Bislsan radar, the first polarimetric radar in Korea, three rainfall cases occurring in 2011 were selected, each caused by different conditions: the first is the Changma front and typhoon, the second is only the Changma front, and the third is only a typhoon. For quantitative use of specific differential phase (KDP), a data quality algorithm was developed for differential phase shift (ΦDP), composed of two steps; the first involves removal of scattered noise and the second is unfolding ofΦDP. This order of the algorithm is necessary so as not to remove unfolded areas, which are the real meteorological target. All noise was removed and the foldedΦDPwere unfolded successfully for this study.RKDPrelations for S-band radar were calculated for 84,754 samples of observed drop size distribution (DSD) using different drop shape assumptions. The relation for the Bringi drop shape showed the best statistics: 0.28 for normalized error, and 6.7 mm for root mean square error for rainfall heavier than 10 mm h-1. Because the drop shape assumption affects the accuracy of rainfall estimation differently for different rainfall types, such characteristics should be taken into account to estimate rainfall more accurately using polarimetric variables.

The New French Operational Polarimetric Radar Rainfall Rate Product

AbstractIn 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

Sources of Errors in Rainfall Measurements by Polarimetric Radar: Variability of Drop Size Distributions, Observational Noise, and Variation of Relationships between R and Polarimetric Parameters

Abstract Using a set of long-term disdrometric data and of actual radar measurements from the McGill S-band operational polarimetric radar, several sources of errors in rain measurement with polarimetric radar are explored in order to investigate their relative importance and the feasibility of a polarimetric technique for estimating R in the context of the McGill S-band operational radar that performs a full volume scan of 24 plan position indicators (PPIs) every 5 min. The sources of errors considered are the variability of drop size distributions (DSDs), observational noise, and systematic variation of the relationships between R and polarimetric parameters at different climate regimes. Additional polarimetric parameters dramatically reduce the effect of the DSD variability on rain estimates by radar. The effectiveness of various multiparameter relationships is investigated. The relationships from the literature that are derived from the DSD model and measured DSDs at a different climate regime differ from those derived from the disdrometric dataset herein. An application of these relationships to the Montreal dataset results in a bias (about 10%–20%) and the significant random error resulting from the DSD variability. These errors should be eliminated by using a relationship suitable for the local climate. Assuming a measurement noise as expected from a slow scanning polarimetric radar [∼1 rotation per minute (rpm)] and a 10-min smoothing, the R − (Zh, ZDR) relationship outperforms the conventional R − Zh because of the combined effect of the DSD variability and measurement errors. In addition, the marginal measurement noise that is required to have the same accuracy of R − Zh and R − (Zh, ZDR) algorithms is obtained as a function of temporal smoothing. The quantified measurement noise of the McGill S-band fast scanning operational radar (∼6 rpm) is significantly larger than that of a slow scanning radar, implying that a temporal averaging of ZDR of 1 h is needed to achieve some gain with R − (Zh, ZDR).

Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data

Summary A long time record of drop size distributions (DSDs) is used to evaluate the effect of the DSD variability on the accuracy of radar adjustment by comparison with a rain gage on a daily basis. Radar and gage measurements are simulated from DSDs. When a single R–Z relationship is used in the adjustment of the radar as a hydrological instrument, a standard deviation of fractional error of ∼28% is expected. This uncertainty is related to the DSD variability in time. A calibration of reflectivity can be done if a disdrometer is available. This disdrometric radar calibration is not affected by the DSD variability. Thus, the uncertainty that is expected in the radar adjustment with a gage is eliminated. Some uncertainty in radar-disdrometer comparison due to the difference in sampling volumes is minimized by applying a sequential intensity filtering technique (SIFT). Good correlations between radar and disdrometric reflectivities indicate that this could be an excellent way of calibrating radar on a daily basis when a disdrometer is located at close range (less than 30 km) from radar. Furthermore, the consistency of independent checks of radar calibration error with different disdrometers and polarimetry validates the argument that the radar-disdrometer comparison can be used as a tool for absolute radar calibration. The information from the McGill operational S-band polarimetric radar is also used to calibrate radar. This method is based on the fact that the specific differential phase shift (KDP) or differential phase shift (ΦDP) between the horizontal and vertical polarized beams is immune to the radar calibration error whereas the reflectivity is affected by the calibration error. Due to the variability of DSDs only, the uncertainty in polarimetric calibration is the standard deviation of 1 dB with a single parameter KDP and reduces to 0.5 dB when the differential reflectivity (ZDR) is added as well. To guarantee the stability of this calibration method, data longer than at least an hour is necessary to calculate the calibration error for the fast scanning McGill operational polarimetric radar and contamination by bright band or snow should be avoided. The sensitivity of this calibration method with respect to the drop deformation is tested.