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Bong-Chul Seo and Witold F. Krajewski

Abstract

This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

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Bong-Chul Seo, Witold F. Krajewski, and Alexander Ryzhkov

Abstract

This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Z dr dependence on Z), the estimation equations of α and a proper number of Z drZ samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

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Bong-Chul Seo, Felipe Quintero, and Witold F. Krajewski

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This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling–Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1–3-h lead times and starts increasing with 4–6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.

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Luciana K. Cunha, James A. Smith, Witold F. Krajewski, Mary Lynn Baeck, and Bong-Chul Seo

Abstract

The NEXRAD program has recently upgraded the WSR-88D network observational capability with dual polarization (DP). In this study, DP quantitative precipitation estimates (QPEs) provided by the current version of the NWS system are evaluated using a dense rain gauge network and two other single-polarization (SP) rainfall products. The analyses are performed for the period and spatial domain of the Iowa Flood Studies (IFloodS) campaign. It is demonstrated that the current version (2014) of QPE from DP is not superior to that from SP mainly because DP QPE equations introduce larger bias than the conventional rainfall–reflectivity [i.e., R(Z)] relationship for some hydrometeor types. Moreover, since the QPE algorithm is based on hydrometeor type, abrupt transitions in the phase of hydrometeors introduce errors in QPE with surprising variation in space that cannot be easily corrected using rain gauge data. In addition, the propagation of QPE uncertainties across multiple hydrological scales is investigated using a diagnostic framework. The proposed method allows us to quantify QPE uncertainties at hydrologically relevant scales and provides information for the evaluation of hydrological studies forced by these rainfall datasets.

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Bong-Chul Seo, Brenda Dolan, Witold F. Krajewski, Steven A. Rutledge, and Walter Petersen

Abstract

This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (ZR) relation that might lead to substantial underestimation for the presented case.

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

Abstract

Rainfall maps that are derived from satellite observations provide hydrologists with an unprecedented opportunity to forecast floods globally. However, the limitations of using these precipitation estimates with respect to producing reliable flood forecasts at multiple scales are not well understood. To address the scientific and practical question of applicability of space-based rainfall products for global flood forecasting, a data evaluation framework is developed that allows tracking the rainfall effects in space and time across scales in the river network. This provides insights on the effects of rainfall product resolution and uncertainty. Obtaining such insights is not possible when the hydrologic evaluation is based on discharge observations from single gauges. The proposed framework also explores the ability of hydrologic model structure to answer questions pertaining to the utility of space-based rainfall observations for flood forecasting. To illustrate the framework, hydrometeorological data collected during the Iowa Flood Studies (IFloodS) campaign in Iowa are used to perform a hydrologic simulation using two different rainfall–runoff model structures and three rainfall products, two of which are radar based [stage IV and Iowa Flood Center (IFC)] and one satellite based [TMPA–Research Version (RV)]. This allows for exploring the differences in rainfall estimates at several spatial and temporal scales and provides improved understanding of how these differences affect flood predictions at multiple basin scales. The framework allows for exploring the differences in peak flow estimation due to nonlinearities in the hydrologic model structure and determining how these differences behave with an increase in the upstream area through the drainage network. The framework provides an alternative evaluation of precipitation estimates, based on the diagnostics of hydrological model results.

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Ibrahim Demir, Helen Conover, Witold F. Krajewski, Bong-Chul Seo, Radosław Goska, Yubin He, Michael F. McEniry, Sara J. Graves, and Walter Petersen

Abstract

In the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground-based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument monitoring, data acquisition, and visualizations after deploying the instruments and how they used a different set of tools to support data analysis and modeling after the campaign are also explained. All data collected during the campaign are available through the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC).

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Kumar Vijay Mishra, Witold F. Krajewski, Radoslaw Goska, Daniel Ceynar, Bong-Chul Seo, Anton Kruger, James J. Niemeier, Miguel B. Galvez, Merhala Thurai, V. N. Bringi, Leonid Tolstoy, Paul A. Kucera, Walter A. Petersen, Jacopo Grazioli, and Andrew L. Pazmany

Abstract

This article presents the data collected and analyzed using the University of Iowa’s X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA’s Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol.

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Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Mohamed ElSaadani, Radoslaw Goska, Luciana K. Cunha, Brenda Dolan, David B. Wolff, James A. Smith, Steven A. Rutledge, and Walter A. Petersen

Abstract

This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

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Witold F. Krajewski, Daniel Ceynar, Ibrahim Demir, Radoslaw Goska, Anton Kruger, Carmen Langel, Ricardo Mantilla, James Niemeier, Felipe Quintero, Bong-Chul Seo, Scott J. Small, Larry J. Weber, and Nathan C. Young

Abstract

The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.

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