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Grace Zalenski, Witold F. Krajewski, Felipe Quintero, Pedro Restrepo, and Steve Buan

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.

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C. Bryan Young, A. Allen Bradley, Witold F. Krajewski, Anton Kruger, and Mark L. Morrissey

Abstract

Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas–Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several complications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine verification of NEXRAD products.

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Yan Zhang, James A. Smith, Alexandros A. Ntelekos, Mary Lynn Baeck, Witold F. Krajewski, and Fred Moshary

Abstract

Heavy precipitation in the northeastern United States is examined through observational and numerical modeling analyses for a weather system that produced extreme rainfall rates and urban flash flooding over the New York–New Jersey region on 4–5 October 2006. Hydrometeorological analyses combine observations from Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars, the National Lightning Detection Network, surface observing stations in the northeastern United States, a vertically pointing lidar system, and a Joss–Waldvogel disdrometer with simulations from the Weather Research and Forecasting Model (WRF). Rainfall analyses from the Hydro-Next Generation Weather Radar (NEXRAD) system, based on observations from WSR-88D radars in State College, Pennsylvania, and Fort Dix, New Jersey, and WRF model simulations show that heavy rainfall was organized into long-lived lines of convective precipitation, with associated regions of stratiform precipitation, that develop along a frontal zone.

Structure and evolution of convective storm elements that produced extreme rainfall rates over the New York–New Jersey urban corridor were influenced by the complex terrain of the central Appalachians, the diurnal cycle of convection, and the history of convective evolution in the frontal zone. Extreme rainfall rates and flash flooding were produced by a “leading line–trailing stratiform” system that was rapidly dissipating as it passed over the New York–New Jersey region. Radar, disdrometer, and lidar observations are used in combination with model analyses to examine the dynamical and cloud microphysical processes that control the spatial and temporal structure of heavy rainfall. The study illustrates key elements of the spatial and temporal distribution of rainfall that can be used to characterize flash flood hazards in the urban corridor of the northeastern United States.

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Witold F. Krajewski, Grzegorz J. Ciach, Jeffrey R. McCollum, and Ciprian Bacotiu

Abstract

The Global Precipitation Climatology Project (GPCP) established a multiyear global dataset of satellite-based estimates of monthly rainfall accumulations averaged over a grid of 2.5° × 2.5° geographical boxes. This paper describes an attempt to quantify the error variance of these estimates at selected reference sites. Fourteen reference sites were selected over the United States at the GPCP grid locations where high-density rain gauge network and high-quality data are available. A rigorous methodology for estimation of the error statistics of the reference sites was applied. A method of separating the reference error variance from the observed mean square difference between the reference and the GPCP products was proposed and discussed. As a result, estimates of the error variance of the GPCP products were obtained. Two kinds of GPCP products were evaluated: 1) satellite-only products, and 2) merged products that incorporate some rain gauge data that were available to the project. The error analysis results show that the merged product is characterized by smaller errors, both in terms of bias as well as the random component. The bias is, on average, 0.88 for the merged product and 0.70 for the satellite-only product. The average random component is 21% for the merged product and 79% for the satellite-only product. The random error is worse in the winter than in the summer. The error estimates agree well with their counterparts produced by the GPCP.

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James A. Smith, Mary Lynn Baeck, Gabriele Villarini, and Witold F. Krajewski

Abstract

Extreme floods in the Delaware River basin are examined through analyses of a sequence of record and near-record floods during September 2004, April 2005, and June 2006. The three flood episodes reflect three principal flood-generating mechanisms in the eastern United States: tropical cyclones (September 2004); late winter–early spring extratropical systems (April 2005); and warm-season convective systems (June 2006). Extreme flooding in the Delaware River basin is the product of heavy rainfall and runoff from high-gradient portions of the watershed. Orographic precipitation mechanisms play a central role in the extreme flood climatology of the Delaware River basin and, more generally, for the eastern United States. Extreme flooding for the 2004–06 events was produced in large measure from forested portions of the watershed. Analyses of flood frequency based on annual flood peak observations from U.S. Geological Survey (USGS) stream gauging stations with “long” records illustrate the striking heterogeneity of flood response over the region, the important role of landfalling tropical cyclones for the upper tail of flood peak distributions, and the prevalence of nonstationarities in flood peak records. Analyses show that changepoints are a more common source of nonstationarity than linear time trends. Regulation by dams and reservoirs plays an important role in determining changepoints, but the downstream effects of reservoirs on flood distributions are limited.

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Merhala Thurai, Kumar Vijay Mishra, V. N. Bringi, and Witold F. Krajewski

Abstract

Data analyses for the mobile Iowa X-band polarimetric (XPOL) radar from a long-duration rain event that occurred during the NASA Iowa Flood Studies (IFloodS) field campaign are presented. A network of six 2D video disdrometers (2DVDs) is used to derive four rain-rate estimators for the XPOL-5 radar. The rain accumulation validations with a collocated network of twin and triple tipping-bucket rain gauges have highlighted the need for combined algorithms because no single estimator was found to be sufficient for all cases considered. A combined version of weighted and composite algorithms is introduced, including a new R(A h, Z dr) rainfall estimator for X band, where A h is the specific attenuation for horizontal polarization and Z dr is the differential reflectivity. Based on measurement and algorithm errors, the weights are derived to be as piecewise constant functions over reflectivity values. The weights are later turned into continuous functions using smoothing splines. A methodology to derive the weights in near–real time is proposed for the composite-weighted algorithm. Comparisons of 2-h accumulations and 8-h event totals obtained from the XPOL-5 with 12 rain gauges have shown 10%–40% improvement in normalized bias over individual rainfall estimators. The analyses have enabled the development of rain-rate estimators for the Iowa XPOL.

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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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Piotr A. Lewandowski, William E. Eichinger, Anton Kruger, and Witold F. Krajewski

Abstract

A significant scale gap between radar and in situ measurements of rainfall using rain gauges and disdrometers indicates a pressing need for improved knowledge of rainfall variability at the spatial scales below those of today’s operational radar rainfall products, that is, ∼1–4 km. Lidar technology has the potential to fulfill this need, but there has been inconsistency in the literature pertaining to quantitative observations of rain using lidar. Several publications have stated that light scattering properties of raindrops could not be correlated with rain rates, while other papers have demonstrated the existence of such relationships. This note provides empirical evidence in support of the latter claim.

The authors conducted a simple experiment using a near-horizontal-pointing elastic lidar to observe rain in Iowa City, Iowa, in the fall of 2005. The lidar signal was used to estimate rainfall quantities that were subsequently compared with independent estimates of the same quantities obtained from an optical disdrometer that was placed about 370 m from the lidar, ∼10 m below the lidar beam. To perform the conversion from the raw lidar signal, the authors used an optical geometry-based procedure to estimate optical extinction data. A theoretical relationship between extinction coefficients and rain rates was derived based on a theoretical drop size distribution. The parameters of the relationship were found through a best-fit procedure using lidar and disdrometer data. The results show that the lidar-derived rain rates correspond to those obtained from the optical disdrometer with a root-mean-square difference of 55%.

The authors conclude that although a great deal remains to be done to improve the inversion algorithm, lidar measurements of rain are possible and warrant further studies. Lidars deployed in conjunction with disdrometers can provide high spatial (<5 m) and temporal (<1 min disdrometer, ∼1 s lidar) resolution data over a relatively long distance for rainfall measurements (1–2 km in the case of the University of Iowa lidar).

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Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Steve Buan, and Brian Connelly

Abstract

This study assesses streamflow predictions generated by two distributed hydrologic models, the Hillslope Link Model (HLM) and the National Water Model (NWM), driven by three radar-based precipitation forcing datasets. These forcing data include the Multi-Radar Multi-Sensor (MRMS), and the Iowa Flood Center’s single-polarization-based (IFC-SP) and dual-polarization-based (IFC-DP) products. To examine forcing- and model-dependent aspects of the representation of hydrologic processes, we mixed and matched all forcing data and models, and simulated streamflow for 2016–18 based on six forcing–model combinations. The forcing product evaluation using independent ground reference data showed that the IFC-DP radar-only product’s accuracy is comparable to MRMS, which is rain gauge corrected. Streamflow evaluation at 140 U.S. Geological Survey (USGS) stations in Iowa demonstrated that the HLM tended to perform slightly better than the NWM, generating streamflow with smaller volume errors and higher predictive power as measured by Kling–Gupta efficiency (KGE). The authors also inspected the effect of estimation errors in the forcing products on streamflow generation and found that MRMS’s slight underestimation bias led to streamflow underestimation for all simulation years, particularly with the NWM. The less biased product (IFC-DP), which has higher error variability, resulted in increased runoff volumes with larger dispersion of errors compared to the ones derived from MRMS. Despite its tendency to underestimate, MRMS showed consistent performance with lower error variability as reflected by the KGE. The dispersion observed from the evaluation metrics (e.g., volume error and KGE) seems to decrease as scale becomes larger, implying that random errors in forcing are likely to average out at larger-scale basins. The evaluation of simulated peaks revealed that an accurate estimation of peak (e.g., time and magnitude) remains challenging, as demonstrated by the highly scattered distribution of peak errors for both hydrologic models.

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Emmanouil N. Anagnostou, Marios N. Anagnostou, Witold F. Krajewski, Anton Kruger, and Benjamin J. Miriovsky

Abstract

The paper presents a rainfall estimation technique based on algorithms that couple, along a radar ray, profiles of horizontal polarization reflectivity (Z H), differential reflectivity (Z DR), and differential propagation phase shift (ΦDP) from X-band polarimetric radar measurements. Based on in situ raindrop size distribution (DSD) data and using a three-parameter “normalized” gamma DSD model, relationships are derived that correct X-band reflectivity profiles for specific and differential attenuation, while simultaneously retrieving variations of the normalized intercept DSD parameter (N w). The algorithm employs an iterative scheme to intrinsically account for raindrop oblateness variations from equilibrium condition. The study is facilitated from a field experiment conducted in the period October–November 2001 in Iowa City, Iowa, where observations from X-band dual-polarization mobile radar (XPOL) were collected simultaneously with high-resolution in situ disdrometer and rain-gauge rainfall measurements. The observed rainfall events ranged in intensity from moderate stratiform precipitation to high-intensity (>50 mm h−1) convective rain cells. The XPOL measurements were tested for calibration, noise, and physical consistency using corresponding radar parameters derived from coincidentally measured raindrop spectra. Retrievals of N w from the attenuation correction scheme are shown to be unbiased and consistent with N w values calculated from independent raindrop spectra. The attenuation correction based only on profiles of reflectivity measurements is shown to diverge significantly from the corresponding polarimetric-based corrections. Several rain retrieval algorithms were investigated using matched pairs of instantaneous high-resolution XPOL observations with rain rates from 3-min-averaged raindrop spectra at close range (∼5 km) and rain-gauge measurements from further ranges (∼10 km). It is shown that combining along-a-ray (corrected ZH, Z DR, and specific differential phase shift) values gets the best performance in rainfall estimation with about 40% (53%) relative standard deviation in the radar–disdrometer (radar–gauge) differences. The case-tuned reflectivity–rainfall rate (ZR) relationship gives about 65% (73%) relative standard deviation for the same differences. The systematic error is shown to be low (∼3% overestimation) and nearly independent of rainfall intensity for the multiparameter algorithm, while for the standard ZR it varied from 10% underestimation to 3% overestimation.

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