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- Author or Editor: Youcun Qi x
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Abstract
The U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network has provided meteorologists and hydrologists with quantitative precipitation observations at an unprecedented high spatial–temporal resolution since its deployment in the mid-1990s. Since each single radar can only cover a maximum range of 460 km, a mosaic of multiple-radar observations is needed to generate any national-scale products. The Multi-Radar Multi-Sensor (MRMS) system utilizes a physically based two-dimensional mosaicking algorithm of the WSR-88D data to generate seamless national quantitative precipitation estimation (QPE) products. For areas covered by multiple radars, the mosaicking scheme first determines if precipitation is present by checking the lowest-altitude observation. If the lowest observed radar data indicate no precipitation, then the mosaicked value is set to no precipitation. Otherwise, a weighted mean of multiple-radar observations is taken as the mosaicked value. The weighting function is based on multiple factors, including the distance from the radar and the height of the observation with respect to the melting layer. The mosaic algorithm uses the physically lowest radar observations with no/little blockage while maintaining a spatial continuity in the mosaicked field. The performance of the MRMS seamless radar mosaic algorithm was examined for various precipitation events of different characteristics. The results of these case evaluations are presented in this paper.
Abstract
The U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network has provided meteorologists and hydrologists with quantitative precipitation observations at an unprecedented high spatial–temporal resolution since its deployment in the mid-1990s. Since each single radar can only cover a maximum range of 460 km, a mosaic of multiple-radar observations is needed to generate any national-scale products. The Multi-Radar Multi-Sensor (MRMS) system utilizes a physically based two-dimensional mosaicking algorithm of the WSR-88D data to generate seamless national quantitative precipitation estimation (QPE) products. For areas covered by multiple radars, the mosaicking scheme first determines if precipitation is present by checking the lowest-altitude observation. If the lowest observed radar data indicate no precipitation, then the mosaicked value is set to no precipitation. Otherwise, a weighted mean of multiple-radar observations is taken as the mosaicked value. The weighting function is based on multiple factors, including the distance from the radar and the height of the observation with respect to the melting layer. The mosaic algorithm uses the physically lowest radar observations with no/little blockage while maintaining a spatial continuity in the mosaicked field. The performance of the MRMS seamless radar mosaic algorithm was examined for various precipitation events of different characteristics. The results of these case evaluations are presented in this paper.
Abstract
The melting of aggregated snow/crystals often results in an enhancement of the reflectivity observed by weather radars, and this is commonly referenced as the bright band (BB). The locally high reflectivity often causes overestimation in radar quantitative precipitation estimates (QPE) if no appropriate correction is applied. When the melting layer is high, a complete BB layer profile (including top, peak, and bottom) can be observed by the ground radar, and a vertical profile of reflectivity (VPR) correction can be made to reduce the BB impact. When a melting layer is near the ground and the bottom part of the bright band cannot be observed by the ground radar, a VPR correction cannot be made directly from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar observations. This paper presents a new VPR correction method under this situation. From high-resolution precipitation profiler data, an empirical relationship between BB peak and BB bottom is developed. The empirical relationship is combined with the apparent BB peak observed by volume scan radars and the BB bottom is found. Radar QPEs are then corrected based on the estimated BB bottom. The new method was tested on 13 radars during seven low brightband events over different areas in the United States. It is shown to be effective in reducing the radar QPE overestimation under low brightband situations.
Abstract
The melting of aggregated snow/crystals often results in an enhancement of the reflectivity observed by weather radars, and this is commonly referenced as the bright band (BB). The locally high reflectivity often causes overestimation in radar quantitative precipitation estimates (QPE) if no appropriate correction is applied. When the melting layer is high, a complete BB layer profile (including top, peak, and bottom) can be observed by the ground radar, and a vertical profile of reflectivity (VPR) correction can be made to reduce the BB impact. When a melting layer is near the ground and the bottom part of the bright band cannot be observed by the ground radar, a VPR correction cannot be made directly from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar observations. This paper presents a new VPR correction method under this situation. From high-resolution precipitation profiler data, an empirical relationship between BB peak and BB bottom is developed. The empirical relationship is combined with the apparent BB peak observed by volume scan radars and the BB bottom is found. Radar QPEs are then corrected based on the estimated BB bottom. The new method was tested on 13 radars during seven low brightband events over different areas in the United States. It is shown to be effective in reducing the radar QPE overestimation under low brightband situations.
Abstract
The bright band (BB) is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals. The locally high reflectivity causes significant overestimation in radar precipitation estimates if an appropriate correction is not applied. The main objective of the current study is to develop a method that automatically corrects for large errors due to BB effects in a real-time national radar quantitative precipitation estimation (QPE) product. An approach that combines the mean apparent vertical profile of reflectivity (VPR) computed from a volume scan of radar reflectivity observations and an idealized linear VPR model was used for computational efficiency. The methodology was tested for eight events from different regions and seasons in the United States. The VPR correction was found to be effective and robust in reducing overestimation errors in radar-derived QPE, and the corrected radar precipitation fields showed physically continuous distributions. The correction worked consistently well for radars in flat land regions because of the relatively uniform spatial distributions of the BB in those areas. For radars in mountainous regions, the performance of the correction is mixed because of limited radar visibility in addition to large spatial variations of the vertical precipitation structure due to underlying topography.
Abstract
The bright band (BB) is a layer of enhanced reflectivity due to melting of aggregated snow and ice crystals. The locally high reflectivity causes significant overestimation in radar precipitation estimates if an appropriate correction is not applied. The main objective of the current study is to develop a method that automatically corrects for large errors due to BB effects in a real-time national radar quantitative precipitation estimation (QPE) product. An approach that combines the mean apparent vertical profile of reflectivity (VPR) computed from a volume scan of radar reflectivity observations and an idealized linear VPR model was used for computational efficiency. The methodology was tested for eight events from different regions and seasons in the United States. The VPR correction was found to be effective and robust in reducing overestimation errors in radar-derived QPE, and the corrected radar precipitation fields showed physically continuous distributions. The correction worked consistently well for radars in flat land regions because of the relatively uniform spatial distributions of the BB in those areas. For radars in mountainous regions, the performance of the correction is mixed because of limited radar visibility in addition to large spatial variations of the vertical precipitation structure due to underlying topography.
Abstract
Microtopography on a building roof will direct rainfall from roofs to the ground through downspouts and transform the rainfall spatial distribution from plane to points. However, the issues on whether and how the building-induced rainfall redistribution (BIRR) influences hydrologic responses are still not well understood despite the numerous downspouts in the urban area. Hence, this study brings the roof layer into a grid-based urban hydrologic model (gUHM) to quantitatively evaluate the impacts of BIRR, aiming to enhance the understanding of building effects in urban hydrology and subsequently to identify the necessity of incorporating BIRR into flood forecasting. Nine land development strategies and 27 rainfall conditions are considered herein to characterize the changing circumstance. Results indicate that the impacts of BIRR depend on multiple circumstance factors and are nonnegligible in urban hydrology. The BIRR causes not only bidirectional impacts on the hydrologic characteristic values (e.g., peak flow and runoff volume) but also an obvious alteration of the hydrograph. Overall, the BIRR tends to increase the peak flow, and more importantly, the impact will be aggravated by the increase of rainfall intensity with the maximum relative error of peak flow approaching 10%. This study contributes to a better understanding of building effects on urban hydrology and a step forward to reduce the uncertainty in urban flood warnings.
Abstract
Microtopography on a building roof will direct rainfall from roofs to the ground through downspouts and transform the rainfall spatial distribution from plane to points. However, the issues on whether and how the building-induced rainfall redistribution (BIRR) influences hydrologic responses are still not well understood despite the numerous downspouts in the urban area. Hence, this study brings the roof layer into a grid-based urban hydrologic model (gUHM) to quantitatively evaluate the impacts of BIRR, aiming to enhance the understanding of building effects in urban hydrology and subsequently to identify the necessity of incorporating BIRR into flood forecasting. Nine land development strategies and 27 rainfall conditions are considered herein to characterize the changing circumstance. Results indicate that the impacts of BIRR depend on multiple circumstance factors and are nonnegligible in urban hydrology. The BIRR causes not only bidirectional impacts on the hydrologic characteristic values (e.g., peak flow and runoff volume) but also an obvious alteration of the hydrograph. Overall, the BIRR tends to increase the peak flow, and more importantly, the impact will be aggravated by the increase of rainfall intensity with the maximum relative error of peak flow approaching 10%. This study contributes to a better understanding of building effects on urban hydrology and a step forward to reduce the uncertainty in urban flood warnings.
Abstract
The accessibility of high-resolution surface data enables fine distributed modeling for urban flooding. However, the surface routing processes between nonhomogeneous land cover components remain in most grid units, due to the high spatial heterogeneity of urban surfaces. Limited by the great difficulty in the acquisition, subgrid routing information (SRI) is always ignored in high-resolution urban flood modeling, and more importantly, the potential impacts of missing SRI on flood forecasting are still less understood. In this study, 54 urban-oriented scenarios of subgrid routing schemes are designed at an isolated grid, including three types of land parcels, two routing directions, and nine routing percents. The impacts of missing SRI are evaluated comprehensively under 60 different rainfall scenarios, in terms of the peak runoff (PR) and the runoff coefficient (RC). Furthermore, the influence mechanism is revealed as well to explain the discrepancy of the impacts under different conditions. Results show the missing of the routing process from impervious to pervious areas leads to significant impacts on the simulation of both PR and RC. Overestimated RC is detected, however, the impacts on PR are bidirectional depending on the rainfall intensity. Overestimation of PR due to missing SRI is observed in light rainfall events, but the opposite effect is identified under heavy rainfall conditions. This study highlights the importance of incorporating the SRI for urban flood forecasting to avoid underestimating the hazard risk in heavy rainfall. Simultaneously, it identifies that blindly utilizing infiltration-based green infrastructure is not feasible in urban stormwater management, due to the possible increase in peak runoff.
Abstract
The accessibility of high-resolution surface data enables fine distributed modeling for urban flooding. However, the surface routing processes between nonhomogeneous land cover components remain in most grid units, due to the high spatial heterogeneity of urban surfaces. Limited by the great difficulty in the acquisition, subgrid routing information (SRI) is always ignored in high-resolution urban flood modeling, and more importantly, the potential impacts of missing SRI on flood forecasting are still less understood. In this study, 54 urban-oriented scenarios of subgrid routing schemes are designed at an isolated grid, including three types of land parcels, two routing directions, and nine routing percents. The impacts of missing SRI are evaluated comprehensively under 60 different rainfall scenarios, in terms of the peak runoff (PR) and the runoff coefficient (RC). Furthermore, the influence mechanism is revealed as well to explain the discrepancy of the impacts under different conditions. Results show the missing of the routing process from impervious to pervious areas leads to significant impacts on the simulation of both PR and RC. Overestimated RC is detected, however, the impacts on PR are bidirectional depending on the rainfall intensity. Overestimation of PR due to missing SRI is observed in light rainfall events, but the opposite effect is identified under heavy rainfall conditions. This study highlights the importance of incorporating the SRI for urban flood forecasting to avoid underestimating the hazard risk in heavy rainfall. Simultaneously, it identifies that blindly utilizing infiltration-based green infrastructure is not feasible in urban stormwater management, due to the possible increase in peak runoff.
Abstract
Automated rain gauge networks provide direct measurements of precipitation and have been used for numerous applications, such as generating regional and national precipitation maps, calibrating remote sensing quantitative precipitation estimation (QPE), and validating hydrological and meteorological model predictions. However, automated gauge observations are prone to be affected by a variety of error sources and require a careful quality-control (QC) procedure. Many previous gauge QC techniques were based on spatiotemporal checks within the gauge network itself, and their effectiveness can be dependent on gauge densities and precipitation regimes. The current study takes advantage of the multisensor data sources in the Multi-Radar Multi-Sensor (MRMS) system and develops an automated and computationally efficient gauge QC scheme based on the consistency of hourly gauge and radar QPE observations. Radar and gauge error characteristics related to radar sampling geometry, precipitation regimes, and freezing-level height is utilized within this scheme. This QC scheme is evaluated by testing its capability to identify suspect gauges and comparing the ability to quality-controlled gauges through statistical and spatial comparisons of gauge-influenced gridded QPE products. Spatial analysis of the gridded QPE products in MRMS resulted in a more physical spatial QPE distribution using quality-controlled gauges versus the same product created with non-quality-controlled gauge data.
Abstract
Automated rain gauge networks provide direct measurements of precipitation and have been used for numerous applications, such as generating regional and national precipitation maps, calibrating remote sensing quantitative precipitation estimation (QPE), and validating hydrological and meteorological model predictions. However, automated gauge observations are prone to be affected by a variety of error sources and require a careful quality-control (QC) procedure. Many previous gauge QC techniques were based on spatiotemporal checks within the gauge network itself, and their effectiveness can be dependent on gauge densities and precipitation regimes. The current study takes advantage of the multisensor data sources in the Multi-Radar Multi-Sensor (MRMS) system and develops an automated and computationally efficient gauge QC scheme based on the consistency of hourly gauge and radar QPE observations. Radar and gauge error characteristics related to radar sampling geometry, precipitation regimes, and freezing-level height is utilized within this scheme. This QC scheme is evaluated by testing its capability to identify suspect gauges and comparing the ability to quality-controlled gauges through statistical and spatial comparisons of gauge-influenced gridded QPE products. Spatial analysis of the gridded QPE products in MRMS resulted in a more physical spatial QPE distribution using quality-controlled gauges versus the same product created with non-quality-controlled gauge data.
Abstract
This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.
Abstract
This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.
Abstract
Understanding spatiotemporal rainfall patterns in mountainous areas is of great importance for prevention of natural disasters such as flash floods and landslides. There is little knowledge about rainfall variability over historically underobserved complex terrains, however, and especially about the variations of hourly rainfall. In this study, the spatiotemporal variations of hourly rainfall in the Three Gorges region (TGR) of China are investigated with gauge and newly available radar data. The spatial pattern of hourly rainfall has been examined by a number of statistics, and they all show that the rainfall variations are time-scale and location dependent. In general, the northern TGR receives more-intense and longer-duration rainfall than do other parts of the TGR, and short-duration storms could occur in most of the TGR. For temporal variations, the summer diurnal cycle shifts from a morning peak in the west to a late-afternoon peak in the east while a mixed pattern of two peaks exists in the middle. In statistical terms, empirical model–based estimation indicates that the correlation scale of hourly rainfall is about 40 km. Further investigation shows that the correlation distance varies with season, from 30 km in the warm season to 60 km in the cold season. In addition, summer rainstorms extracted from radar rainfall data are characterized by short duration (6–8 h) and highly localized patterns (5–17 and 13–36 km in the minor and major directions, respectively). Overall, this research provides quantitative information about the rainfall regime in the TGR and shows that the combination of gauge and radar data is useful for characterizing the spatiotemporal pattern of storm rainfall over complex terrain.
Abstract
Understanding spatiotemporal rainfall patterns in mountainous areas is of great importance for prevention of natural disasters such as flash floods and landslides. There is little knowledge about rainfall variability over historically underobserved complex terrains, however, and especially about the variations of hourly rainfall. In this study, the spatiotemporal variations of hourly rainfall in the Three Gorges region (TGR) of China are investigated with gauge and newly available radar data. The spatial pattern of hourly rainfall has been examined by a number of statistics, and they all show that the rainfall variations are time-scale and location dependent. In general, the northern TGR receives more-intense and longer-duration rainfall than do other parts of the TGR, and short-duration storms could occur in most of the TGR. For temporal variations, the summer diurnal cycle shifts from a morning peak in the west to a late-afternoon peak in the east while a mixed pattern of two peaks exists in the middle. In statistical terms, empirical model–based estimation indicates that the correlation scale of hourly rainfall is about 40 km. Further investigation shows that the correlation distance varies with season, from 30 km in the warm season to 60 km in the cold season. In addition, summer rainstorms extracted from radar rainfall data are characterized by short duration (6–8 h) and highly localized patterns (5–17 and 13–36 km in the minor and major directions, respectively). Overall, this research provides quantitative information about the rainfall regime in the TGR and shows that the combination of gauge and radar data is useful for characterizing the spatiotemporal pattern of storm rainfall over complex terrain.
Abstract
Quantitative precipitation estimation (QPE) in the West Coast region of the United States has been a big challenge for Weather Surveillance Radar-1988 Doppler (WSR-88D) because of severe blockages caused by the complex terrain. The majority of the heavy precipitation in the West Coast region is associated with strong moisture flux from the Pacific that interacts with the coastal mountains. Such orographic enhancement of precipitation occurs at low levels and cannot be observed well by WSR-88D because of severe blockages. Specifically, the radar beam either samples too high above the ground or misses the orographic enhancement at lower levels, or the beam broadens with range and cannot adequately resolve vertical variations of the reflectivity structure. The current study developed an algorithm that uses S-band Precipitation Profiler (S-PROF) radar observations in northern California to improve WSR-88D QPEs in the area. The profiler data are used to calculate two sets of reference vertical profiles of reflectivity (RVPRs), one for the coastal mountains and another for the Sierra Nevada. The RVPRs are then used to correct the WSR-88D QPEs in the corresponding areas. The S-PROF–based VPR correction methodology (S-PROF-VPR) has taken into account orographic processes and radar beam broadenings with range. It is tested using three heavy rain events and is found to provide significant improvements over the operational radar QPE.
Abstract
Quantitative precipitation estimation (QPE) in the West Coast region of the United States has been a big challenge for Weather Surveillance Radar-1988 Doppler (WSR-88D) because of severe blockages caused by the complex terrain. The majority of the heavy precipitation in the West Coast region is associated with strong moisture flux from the Pacific that interacts with the coastal mountains. Such orographic enhancement of precipitation occurs at low levels and cannot be observed well by WSR-88D because of severe blockages. Specifically, the radar beam either samples too high above the ground or misses the orographic enhancement at lower levels, or the beam broadens with range and cannot adequately resolve vertical variations of the reflectivity structure. The current study developed an algorithm that uses S-band Precipitation Profiler (S-PROF) radar observations in northern California to improve WSR-88D QPEs in the area. The profiler data are used to calculate two sets of reference vertical profiles of reflectivity (RVPRs), one for the coastal mountains and another for the Sierra Nevada. The RVPRs are then used to correct the WSR-88D QPEs in the corresponding areas. The S-PROF–based VPR correction methodology (S-PROF-VPR) has taken into account orographic processes and radar beam broadenings with range. It is tested using three heavy rain events and is found to provide significant improvements over the operational radar QPE.
Abstract
High-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (Z–R) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a synergistic approach to QPE by combining radar, rain gauge, and an orographic precipitation climatology. In the merged QPE, radar data depict high-resolution spatial distributions of the precipitation and rain gauges provide accurate precipitation measurements that correct potential biases in the radar QPE. The climatology provides a high-resolution background of the spatial precipitation distribution in the complex terrain where radar coverage is limited or nonexistent. The merging algorithm was tested on heavy precipitation events in different areas of the United States and provided a superior QPE to the individual components. The new QPE algorithm is fully automated and can be easily implemented in an operational system.
Abstract
High-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (Z–R) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a synergistic approach to QPE by combining radar, rain gauge, and an orographic precipitation climatology. In the merged QPE, radar data depict high-resolution spatial distributions of the precipitation and rain gauges provide accurate precipitation measurements that correct potential biases in the radar QPE. The climatology provides a high-resolution background of the spatial precipitation distribution in the complex terrain where radar coverage is limited or nonexistent. The merging algorithm was tested on heavy precipitation events in different areas of the United States and provided a superior QPE to the individual components. The new QPE algorithm is fully automated and can be easily implemented in an operational system.
