Search Results
Abstract
The WSR-88D Precipitation Processing Subsystem (PPS) is a multicomponent rainfall-estimation algorithm with a large number of parameters controlling its performance. Currently, the parameter values of the PPS are set based on limited experimental studies and do not account for rainfall-regime differences. This translates into potential increase of uncertainty in the system-estimated precipitation products.
The authors propose to formulate the PPS calibration as a global optimization problem. The parameter values are determined by optimizing a selected criterion at the level of gridded hourly rainfall-accumulation products. The criterion is the root-mean-square difference between the hourly radar rainfall products and rainfall accumulations from rain gauges under the radar umbrella. The main advantages of this approach are 1) it simultaneously estimates the optimal parameters providing an integral assessment of the algorithm’s performance, and 2) it allows for an assessment of the relative importance of the PPS parameters in the full context of rainfall estimation.
The optimization approach is illustrated using two months of Melbourne, Florida, WSR-88D radar-reflectivity data and the corresponding rain gauge measurements. Global optimization of the PPS parameters yields a reduction of 10% on average and up to 22% on individual days with respect to the default system. The illustration is completed by a sensitivity analysis of the PPS to identify the most significant parameters.
Abstract
The WSR-88D Precipitation Processing Subsystem (PPS) is a multicomponent rainfall-estimation algorithm with a large number of parameters controlling its performance. Currently, the parameter values of the PPS are set based on limited experimental studies and do not account for rainfall-regime differences. This translates into potential increase of uncertainty in the system-estimated precipitation products.
The authors propose to formulate the PPS calibration as a global optimization problem. The parameter values are determined by optimizing a selected criterion at the level of gridded hourly rainfall-accumulation products. The criterion is the root-mean-square difference between the hourly radar rainfall products and rainfall accumulations from rain gauges under the radar umbrella. The main advantages of this approach are 1) it simultaneously estimates the optimal parameters providing an integral assessment of the algorithm’s performance, and 2) it allows for an assessment of the relative importance of the PPS parameters in the full context of rainfall estimation.
The optimization approach is illustrated using two months of Melbourne, Florida, WSR-88D radar-reflectivity data and the corresponding rain gauge measurements. Global optimization of the PPS parameters yields a reduction of 10% on average and up to 22% on individual days with respect to the default system. The illustration is completed by a sensitivity analysis of the PPS to identify the most significant parameters.
Abstract
This paper explores the skill of river stage forecasts produced by the National Weather Service (NWS). Despite the importance of the verification process in establishing a reference that allows advancement in river forecast technology, there is relatively little literature on this topic. This study aims to contribute to this subject. The study analyzed the North Central River Forecast Center’s river stage forecasts for 51 gauges in eastern and central Iowa between 1999 and 2014. The authors explored forecast skill dependence characteristics such as upstream area, water travel time, and the number of gauges located upstream of each forecasting point. They also assessed the influence of rainfall uncertainty on stage error by examining the relationship between the forecast skill and its antecedent 24-h observed rainfall. The results show that when using persistence as a reference for comparison with NWS actual forecasts, the NWS forecasts are better for predictions below and above flood stage. The difference in root-mean-square error (RMSE) between the actual and persistence forecasts ranges between 0.04 and 1.24 ft, and it increases with lead time. Locations with fewer upstream gauges exhibit greater variation in forecast skill than locations that are well gauged, especially at high flood levels. Strong predictive relationships between the physical characteristics of a basin (travel time, upstream drainage area), rainfall quantities, and forecast skill have not been identified.
Abstract
This paper explores the skill of river stage forecasts produced by the National Weather Service (NWS). Despite the importance of the verification process in establishing a reference that allows advancement in river forecast technology, there is relatively little literature on this topic. This study aims to contribute to this subject. The study analyzed the North Central River Forecast Center’s river stage forecasts for 51 gauges in eastern and central Iowa between 1999 and 2014. The authors explored forecast skill dependence characteristics such as upstream area, water travel time, and the number of gauges located upstream of each forecasting point. They also assessed the influence of rainfall uncertainty on stage error by examining the relationship between the forecast skill and its antecedent 24-h observed rainfall. The results show that when using persistence as a reference for comparison with NWS actual forecasts, the NWS forecasts are better for predictions below and above flood stage. The difference in root-mean-square error (RMSE) between the actual and persistence forecasts ranges between 0.04 and 1.24 ft, and it increases with lead time. Locations with fewer upstream gauges exhibit greater variation in forecast skill than locations that are well gauged, especially at high flood levels. Strong predictive relationships between the physical characteristics of a basin (travel time, upstream drainage area), rainfall quantities, and forecast skill have not been identified.
Abstract
Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.
Abstract
Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.
Abstract
This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine the potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open-channel flow model. For streamflow DA and its evaluation, we used 6-yr (2016–21) data collected from 140 U.S. Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as the drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed an expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.
Abstract
This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine the potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open-channel flow model. For streamflow DA and its evaluation, we used 6-yr (2016–21) data collected from 140 U.S. Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as the drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed an expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.
