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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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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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Mekonnen Gebremichael, Witold F. Krajewski, Mark Morrissey, Darin Langerud, George J. Huffman, and Robert Adler

Abstract

This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite–gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites—two in North Dakota and one in Thailand—all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These results show that the accuracy of the EVS error uncertainty estimates for the SG product differs from that of the MS product. The key factors that affect the errors of the EVS results, such as the gauge density, the gauge network, and the sample size, have been identified and their influence has been quantified. One major finding of this study is that 8–10 gauges, at the 2.5° scale, are required as a minimum to get good error uncertainty estimates for the SG products from the EVS method. For eight or more gauges, the normalized error uncertainty is about 0.86 ± 0.10 (North Dakota: Box 1) and 0.95 ± 0.10 (North Dakota: Box 2). Results show that, despite its error, the EVS method performs better than the root-mean-square error (rmse) approach that ignores the rain gauge sampling error. For the MS products, both the EVS method and the rmse approach give negligible bias. As expected, results show that the SG products give better rainfall estimates than the MS products, according to most of the criteria used.

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Witold F. Krajewski, Mark L. Morrissey, James A. Smith, and David T. Rexroth

Abstract

A Monte Carlo simulation study is conducted to investigate the performance of the area-threshold method of estimating mean areas rainfall. The study uses a stochastic space-time model of rainfall as the true rainfall-field generator. Simple schemes of simulating radar observations of the simulated rainfall fields are employed. The schemes address both random and systematic components of the radar rainfall-estimation process. The results of the area-threshold method are compared to the results based on conventional averaging of radar-estimated point rainfall observations. The results demonstrate that when the exponent parameter in the ZR relationship has small uncertainty (about ±10%), the conventional method works better than the area-threshold method. When the errors are higher (±20%), the area-threshold method with optimum threshold in the 5–10 mm h−1 range performs best. For even higher errors in the ZR relationship, the area-threshold method with a low threshold provides the best performance.

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Luciana K. Cunha, James A. Smith, Mary Lynn Baeck, and Witold F. Krajewski

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.

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Jeffrey R. McCollum, Witold F. Krajewski, Ralph R. Ferraro, and Mamoudou B. Ba

Abstract

A bias-adjusted radar rainfall product is created and used for evaluation of two satellite rainfall estimation algorithms. Three years of collocated rainfall estimates from radar, rain gauges, a microwave satellite algorithm, and a multispectral (visible through near-infrared) algorithm were collected over the continental United States from July 1998 through July 2001. The radar and gauge data are compared to determine the locations and times at which the rainfall occurrences estimated by these two sensors are in sufficient agreement for the data to be used for validation. This procedure serves as quality control for both sensors and determines the locations at which the radar has difficulty detecting rainfall and should not be used in a validation dataset. For the data remaining after quality control, the gauge data are used for multiplicative adjustment of the radar estimates to remove the radar bias with respect to the gauges. These bias-adjusted estimates are compared with the satellite rainfall estimates to observe the evolution of the satellite biases over the 3-yr period. The multispectral algorithm was under development throughout the 3-yr period, and improvement is evident. The microwave algorithm overestimates rainfall in the summer months, underestimates in the winter months, and has an east-to-west bias gradient, all of which are consistent with physical explanations and previous findings. The multispectral algorithm bias depends highly on diurnal sampling; there is much greater overestimation for the daytime overpasses. These results are applicable primarily to the eastern half of the United States, because few data in the western half remain after quality control.

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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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Hatim O. Sharif, Fred L. Ogden, Witold F. Krajewski, and Ming Xue

Abstract

The prediction uncertainty of a hydrologic model is closely related to model formulation and the uncertainties in model parameters and inputs. Currently, the foremost challenges concern not only whether hydrologic model outputs match observations, but also whether or not model predictions are meaningful and useful in the contexts of land use and climate change. The latter is difficult to determine given that model inputs, such as rainfall, have errors and uncertainties that cannot be entirely eliminated. In this paper the physically based simulation methodology developed by Sharif et al. is used to expand this investigation of the propagation of radar rainfall estimation errors in hydrologic simulations. The methodology includes a physics-based mesoscale atmospheric model, a three-dimensional radar simulator, and a two-dimensional infiltration-excess hydrologic model. A time series of simulated three-dimensional precipitation fields over a large domain and a small study watershed are used, which allows development of a large set of rainfall events with different rainfall volumes and vertical reflectivity profiles. Simulation results reveal dominant range-dependent error sources, and frequent amplification of radar rainfall estimation errors in terms of predicted hydrograph characteristics. It is found that in the case of Hortonian runoff predictions, the variance of hydrograph prediction error due to radar rainfall errors decreases for all radar ranges as the event magnitude increases. However, errors in Hortonian runoff predictions increase significantly with range, particularly beyond about 80 km, where the reflectivity signal is increasingly dominated by three-dimensional rainfall heterogeneity with increasing range under otherwise ideal observing conditions.

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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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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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