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- Author or Editor: Haonan Chen x
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Abstract
Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (K DP)–reflectivity (Z H ) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived K DP and Z H relationships, as well as rainfall estimates based on R(Z H ) and R(K DP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of Z H measurements can effectively be enhanced; (ii) the distribution of radar-measured K DP versus Z H is more consistent with the DSD-derived K DP versus Z H ; (iii) the measured Z H from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where Z H is larger than 30 dBZ; and (iv) rainfall estimates based on R(Z H ) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(K DP).
Abstract
Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (K DP)–reflectivity (Z H ) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived K DP and Z H relationships, as well as rainfall estimates based on R(Z H ) and R(K DP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of Z H measurements can effectively be enhanced; (ii) the distribution of radar-measured K DP versus Z H is more consistent with the DSD-derived K DP versus Z H ; (iii) the measured Z H from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where Z H is larger than 30 dBZ; and (iv) rainfall estimates based on R(Z H ) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(K DP).
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.
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.
Abstract
It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.
Abstract
It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.
Abstract
This study develops a flexible Bayesian technique to quantify uncertainties associated with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) quantitative precipitation estimation (QPE) products over complex terrain. Radar-only rainfall estimates and rain gauge observations over the Russian River watershed in Northern California are utilized to demonstrate this new bias correction approach. Conventional mean field bias (MFB) and local bias (LB) correction methods are also implemented for comparison purposes. Results show that the proposed Bayesian technique outperforms the conventional MFB and LB correction approaches. The radar QPE performance is dramatically improved after the Bayesian-based bias correction: the root-mean-square error is reduced from 4.2 to 1.71 mm, the normalized mean absolute error is reduced from 64.5% to 24.2%, and the correlation with gauge measurements increases from 0.11 to 0.74. In addition, the terrain impact on radar QPE bias correction performance is investigated. After incorporating the terrain elevation information in the Bayesian framework, the QPE performance is further enhanced. Overall, the QPE performance scores after including the terrain information are improved about 10% relative to those only based on rainfall intensity values.
Abstract
This study develops a flexible Bayesian technique to quantify uncertainties associated with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) quantitative precipitation estimation (QPE) products over complex terrain. Radar-only rainfall estimates and rain gauge observations over the Russian River watershed in Northern California are utilized to demonstrate this new bias correction approach. Conventional mean field bias (MFB) and local bias (LB) correction methods are also implemented for comparison purposes. Results show that the proposed Bayesian technique outperforms the conventional MFB and LB correction approaches. The radar QPE performance is dramatically improved after the Bayesian-based bias correction: the root-mean-square error is reduced from 4.2 to 1.71 mm, the normalized mean absolute error is reduced from 64.5% to 24.2%, and the correlation with gauge measurements increases from 0.11 to 0.74. In addition, the terrain impact on radar QPE bias correction performance is investigated. After incorporating the terrain elevation information in the Bayesian framework, the QPE performance is further enhanced. Overall, the QPE performance scores after including the terrain information are improved about 10% relative to those only based on rainfall intensity values.
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.
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.
Abstract
The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation A H , which requires a good estimate of the ray-averaged ratio between A H and specific differential phase K DP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of A H and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity Z DR dependence on horizontal reflectivity factor Z H . Similar to the Z DR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.
Abstract
The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation A H , which requires a good estimate of the ray-averaged ratio between A H and specific differential phase K DP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of A H and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity Z DR dependence on horizontal reflectivity factor Z H . Similar to the Z DR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.
