Scale Dependence Of Radar Rainfall Uncertainty: Initial Evaluation Of NEXRAD\u27s New Super-resolution Data For Hydrologic Applications

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. © 2010 American Meteorological Society

High-resolution QPF Uncertainty And Its Implications For Flood Prediction: A Case Study For The Eastern Iowa Flood Of 2016

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

Utility Of Vertically Integrated Liquid Water Content For Radar-Rainfall Estimation: Quality Control And Precipitation Type Classification

This study proposes a new estimation method for vertically integrated liquid water content (VIL) using radar reflectivity volume data and temperature sounding retrieved from the numerical weather model analysis. This method addresses uncertainty factors in conventional VIL estimation associated with the effects from the bright band (BB) and radar beam geometry near the radar site. The new VIL is then used for precipitation classification (convective/stratiform) and wind turbine clutter detection in the hope that the estimated VIL indicating vertical activities or development of precipitation systems will account for the two independent subjects together, in opposite ways. The non-precipitation radar echoes returned from wind turbines do not likely generate significant degree of VIL, compared to the one estimated from actual convective cells, which contain comparable reflectivity strength. We tested the proposed VIL estimation, precipitation classification, and wind turbine clutter detection methods using various Iowa cases and illustrated their successful application. We also performed a quantitative evaluation of precipitation classification using ground reference data from a dense rain gauge network over the Turkey River basin in Iowa. The evaluation results show improved performance for most non-convective event cases estimated by the stratiform estimator (Z = 200R1.6) because we applied the convective estimator (Z = 300R1.4) to all event cases without classification. In addition, we demonstrated the potential of the new classification to mitigate significant BB effects in quantitative precipitation estimation using a correction method based on the vertical profile of reflectivity

Multi-Scale Hydrologic Evaluation Of The National Water Model Streamflow Data Assimilation

Streamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model-simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF-Hydro® with the NWM modeling elements and assimilated 15-min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as nudging, the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi-scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open-loop simulation, and the improvements increase with areal coverage of upstream assimilation points

Evaluation Of The Specific Attenuation Method For Radar-based Quantitative Precipitation Estimation: Improvements And Practical Challenges

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 a, 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 Zdr dependence on Z), the estimation equations of a and a proper number of Zdr–Z samples required for a reliable a estimation are examined. Based on the dynamic estimation of a, 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 a 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., 38), which is another challenge for light rain

Using The New Dual-polarimetric Capability Of WSR-88D To Eliminate Anomalous Propagation And Wind Turbine Effects In Radar-rainfall

This study addresses the effect that the interaction between anomalous radar beam propagation (AP) and wind turbines that are located far from the radar has on radar-rainfall estimates. The interference of wind turbines in radar observations may lead to significant errors in rainfall estimates since wind turbines are often clustered to form wind farms. In this study, we propose a novel approach - based on the polarimetric capability recently added to the WSR-88D NEXRAD radars - that identifies and eliminates wind turbine clutter along with common ground clutter AP effects. Our primary objective is to devise a physically meaningful and fully automated dual-polarimetric method that effectively handles clutter features, which are hard to detect using single-channel reflectivity data alone. To address this issue, we explore the feasibility of using polarimetric variables such as differential reflectivity (ZDR), copolar correlation (RHO), and differential phase (PHIDP). Accordingly, we developed three new approaches using polarimetric variables, which are combined with the AP detection algorithm that uses a three-dimensional structure of reflectivity. We evaluate the new algorithms in terms of both eliminating non-meteorological radar returns and preserving returns from actual rain. The proposed algorithm, which uses RHO conditioned on horizontal reflectivity values while also accounting for the variation of ZDR or PHIDP, shows good performance for the presented cases

Uncertainty In Radar-rainfall Composite And Its Impact On Hydrologic Prediction For The Eastern Iowa Flood Of 2008

Key Points A significant potential source of error exists in mosaicked radar-rainfall maps. Different radar calibration offsets lead to misestimation of rainfall amounts. Systematic error in rainfall significantly affects hydrologic predictions. This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCAS\u27s ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions. ©2013. American Geophysical Union. All Rights Reserved

Four-dimensional Reflectivity Data Comparison Between Two Ground-based Radars: Methodology And Statistical Analysis

A methodology is proposed to compare radar reflectivity data obtained from two partially overlapping ground-based radars in order to explain relative differences in radar-rainfall products and establish sound merging procedures for multi-radar observing networks. To identify radar calibration differences, radar reflectivity is compared for well-matched radar sampling volumes viewing common meteorological targets. Temporal separation and three-dimensional matching of two different sampling volumes were considered based on the original polar coordinates of radar observation. Since the proposed method assumes radar beam propagation under standard atmospheric conditions, anomalous propagation cases were eliminated from the analysis. The reflectivity comparison results show systematic differences over time, but the variability of these differences is surprisingly large due to the sensitive nature of the radar reflectivity measurement. © 2014 IAHS Press

Electronic, optical and thermal properties of the hexagonal and fcc Ge2Sb2Te5 chalcogenide from first-principle calculations

We present a comprehensive computational study on the properties of face-centered cubic and hexagonal chalcogenide Ge2Sb2Te5. We calculate the electronic structure using density functional theory (DFT); the obtained density of states (DOS) compares favorably with experiments, also looking suitable for transport analysis. Optical constants including refraction index and absorption coefficient capture major experimental features, aside from an energy shift owed to an underestimate of the band gap that is typical of DFT calculations. We also compute the phonon DOS for the hexagonal phase, obtaining a speed of sound and thermal conductivity in good agreement with the experimental lattice contribution. The calculated heat capacity reaches ~ 1.4 x 106 J/(m3 K) at high temperature, in agreement with experimental data, and provides insight into the low-temperature range (< 150 K), where data are unavailable.Comment: 19 pages, 8 figure