scholarly journals Operational C-Band Dual-Polarization Radar QPE for the Subtropical Complex Terrain of Taiwan

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yadong Wang ◽  
Jian Zhang ◽  
Pao-Liang Chang ◽  
Carrie Langston ◽  
Brian Kaney ◽  
...  

Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitation at the ground and result in errors due to vertical variations of the radar variables. To obtain accurate radar QPEs in the subtropical complex terrain of Taiwan, a vertically corrected composite algorithm (VCCA) was developed for two C-band polarimetric radars. The new algorithm corrects higher tilt radar variables with the vertical profile of reflectivity (VPR) or vertical profile of specific differential phase (VPSDP) and estimates rainfall rate at the ground through an automated combination ofR-ZandR-KDPrelations. The VCCA was assessed with three precipitation cases of different regimes including typhoon, mei-yu, and summer stratiform precipitation events. The results showed that a combination ofR-ZandR-KDPrelations provided more accurate QPEs than each alone becauseR-Zprovides better rainfall estimates for light rains andR-KDPrelation is more suitable for heavy rains. The vertical profile corrections for reflectivity and specific differential phase significantly reduced radar QPE errors caused by inadequate sampling of the orographic enhancement of precipitation near the ground.

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.


2021 ◽  
Author(s):  
Anil Kumar Khanal ◽  
Guy Delrieu ◽  
Brice Boudevillain ◽  
Frédéric Cazenave ◽  
Nan Yu

<p>The RadAlp experiment at the Grenoble region in the French Alps aims to advance the radar remote sensing techniques of precipitation in high mountain regions. Since 2016, two dual-polarimetric X-band radars, one on top of Mt Moucherotte (1901 m asl) and another in the Grenoble valley (220 m asl) are operated by Metro France and IGE respectively. High spatio-temporal variability of precipitation (e.g. intensity and phase) in the complex terrain requires high-resolution observations. X-band radar provides high spatial and temporal resolution imagery which makes it ideal for use in complex terrain but also comes with significant attenuation problems during heavy precipitation and in the melting layer (ML). The development of polarimetric techniques, especially differential phase shift (ϕDP) has helped to mitigate the power signal attenuation problem to a certain extent. The ϕDP is immune to attenuation due to rainfall, radar calibration errors and partial beam blockage, making it an attractive parameter for quantitative precipitation estimation (QPE) through attenuation correction of the reflectivity (Z). The ϕDP, however, is quite noisy and requires regularization. An iterative algorithm based on maximum allowed step sizes provides a robust solution in ϕDP regularization. In this study, we aim to understand the relationship between differential phase shift (ϕDP) and path integrated attenuation (PIA) at X-band. This relationship is crucial for quantitative precipitation estimation (QPE) using polarimetric techniques. Furthermore, this relationship is still poorly documented within the melting layer due to the complexity of the hydrometeors' distributions in terms of phase, size, shape and density. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events as compared to dry periods. The quasi-vertical profiles from the valley-based radar (XPORT) help to identify, characterize and follow the evolution of the melting layer. For the mountaintop radar (MOUC) stratiform events (59 days between Nov 2016 to Dec 2019) where the O° elevation angle beam passes through the melting layer are considered.  The PIA/ ϕDP ratios at different strata of the ML, snow-ML interface and ML-rain interface are studied. Initial results show that the PIA/ ϕDP ratio peaks at the levels of cross-correlation coefficient (ρHV) minima, remains strong in the upper part of the ML and tends to 0 towards the top of ML. Additionally, its value in rain (0.32 dB per deg) below the ML matches closely with the specific attenuation vs specific phase (k-KDP) relationship (0.29) derived from the disdrometer at ground level.  Its value increases steadily in the lower part of ML (peaks around 0.70 dB per deg), remains strong in the upper part of ML (0.5 - 0.6 dB per degree), and decreases rapidly to 0.13 dB per degree above the ML (in snow).</p>


2015 ◽  
Vol 16 (5) ◽  
pp. 2230-2247 ◽  
Author(s):  
Yadong Wang ◽  
Jian Zhang ◽  
Pao-Liang Chang ◽  
Qing Cao

Abstract Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.


2017 ◽  
Vol 18 (12) ◽  
pp. 3199-3215 ◽  
Author(s):  
Leonardo Porcacchia ◽  
P. E. Kirstetter ◽  
J. J. Gourley ◽  
V. Maggioni ◽  
B. L. Cheong ◽  
...  

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.


2013 ◽  
Vol 52 (11) ◽  
pp. 2529-2548 ◽  
Author(s):  
Silke Trömel ◽  
Matthew R. Kumjian ◽  
Alexander V. Ryzhkov ◽  
Clemens Simmer ◽  
Malte Diederich

AbstractOn the basis of simulations and observations made with polarimetric radars operating at X, C, and S bands, the backscatter differential phase δ has been explored; δ has been identified as an important polarimetric variable that should not be ignored in precipitation estimations that are based on specific differential phase KDP, especially at shorter radar wavelengths. Moreover, δ bears important information about the dominant size of raindrops and wet snowflakes in the melting layer. New methods for estimating δ in rain and in the melting layer are suggested. The method for estimating δ in rain is based on a modified version of the “ZPHI” algorithm and provides reasonably robust estimates of δ and KDP in pure rain except in regions where the total measured differential phase ΦDP behaves erratically, such as areas affected by nonuniform beam filling or low signal-to-noise ratio. The method for estimating δ in the melting layer results in reliable estimates of δ in stratiform precipitation and requires azimuthal averaging of radial profiles of ΦDP at high antenna elevations. Comparisons with large disdrometer datasets collected in Oklahoma and Germany confirm a strong interdependence between δ and differential reflectivity ZDR. Because δ is immune to attenuation, partial beam blockage, and radar miscalibration, the strong correlation between ZDR and δ is of interest for quantitative precipitation estimation: δ and ZDR are differently affected by the particle size distribution (PSD) and thus may complement each other for PSD moment estimation. Furthermore, the magnitude of δ can be utilized as an important calibration parameter for improving microphysical models of the melting layer.


2020 ◽  
Vol 21 (6) ◽  
pp. 1367-1381 ◽  
Author(s):  
Shruti A. Upadhyaya ◽  
Pierre-Emmanuel Kirstetter ◽  
Jonathan J. Gourley ◽  
Robert J. Kuligowski

ABSTRACTThe launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.


2014 ◽  
Vol 15 (6) ◽  
pp. 2250-2266 ◽  
Author(s):  
Yadong Wang ◽  
Pengfei Zhang ◽  
Alexander V. Ryzhkov ◽  
Jian Zhang ◽  
Pao-Liang Chang

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.


2010 ◽  
Vol 49 (10) ◽  
pp. 2167-2180 ◽  
Author(s):  
Pierre-Emmanuel Kirstetter ◽  
Hervé Andrieu ◽  
Guy Delrieu ◽  
Brice Boudevillain

Abstract Nonuniform beam filling associated with the vertical variation of atmospheric reflectivity is an important source of error in the estimation of rainfall rates by radar. It is, however, possible to correct for this error if the vertical profile of reflectivity (VPR) is known. This paper presents a method for identifying VPRs from volumetric radar data. The method aims at improving an existing algorithm based on the analysis of ratios of radar measurements at multiple elevation angles. By adding a rainfall classification procedure defining more homogeneous precipitation patterns, the issue of VPR homogeneity is specifically addressed. The method is assessed using the dataset from a volume-scanning strategy for radar quantitative precipitation estimation designed in 2002 for the Bollène radar (France). The identified VPR is more representative of the rain field than are other estimated VPRs. It has also a positive impact on radar data processing for precipitation estimation: while scatter remains unchanged, an overall bias reduction at all time steps is noticed (up to 6% for all events) whereas performance varies with type of events considered (mesoscale convective systems, cold fronts, or shallow convection) according to the radar-observation conditions. This is attributed to the better processing of spatial variations of the vertical profile of reflectivity for the stratiform regions. However, adaptation of the VPR identification in the difficult radar measurement context in mountainous areas and to the rainfall classification procedure proved challenging because of data fluctuations.


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