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During May and June 2014, NOAA X-Pol (NOXP), the National Severe Storms Laboratory's dualpolarized X-band mobile radar, was deployed to the Pigeon River basin in the Great Smoky Mountains of North Carolina as part of the NASA Integrated Precipitation and Hydrology Experiment. Rain gauges and disdrometers were positioned within the basin to verify precipitation estimates from various radar and satellite precipitation algorithms. First, the performance of the Self-Consistent Optimal Parameterization-Microphysics Estimation (SCOP-ME) algorithm for NOXP was examined using ground instrumentation as validation and was found to perform similarly to or slightly outperform other precipitation algorithms over the course of the intensive observation period (IOP). Radar data were also used to examine ridge-valley differences in radar and microphysical parameters for a case of stratiform precipitation passing over the mountains. Inferred coalescence microphysical processes were found to dominate within the upslope region, while a combination of processes were present as the system propagated over the valley. This suggests that enhanced updrafts aided by orographic lift sustain convection over the upslope regions, leading to larger median drop diameters. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively. [ABSTRACT FROM AUTHOR]

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR-88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding 'true' precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity-to-precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space-based passive and active sensor algorithms. [ABSTRACT FROM AUTHOR]

The societal impacts of flash floods are more significant than any other weather-related hazard. They are often manifested in the form of damage to infrastructure, flooding of roadways and bridges, creating deadly hazards to motorists and inundation of crops and pasture. Some of these hazards can be anticipated and thus mitigated given effective warning systems. This study describes the tools proposed over recent decades in the USA to predict flash flooding and evaluates them using a common observational data set. Design recommendations for flash-flood forecasting systems are provided, taking into account today's availability of high-resolution rainfall data at scales commensurate with flash flooding, their archives, spatial data sets to describe physiographic properties, and ever-increasing computational resources.Editor D. Koutsoyiannis; Guest editor R.J. MooreCitation Gourley, J.J., Flamig, Z.L., Hong, Y., and Howard, K.W., 2014. Evaluation of past, present and future tools for radar-based flash-flood prediction in the USA. Hydrological Sciences Journal, 59 (7), 1377–1389. http://dx.doi.org/10.1080/02626667.2014.919391 [ABSTRACT FROM AUTHOR]

A major limitation of improved radar-based rainfall estimation is accurate calibration of radar reflectivity. In this paper, the authors fully automate a polarimetric method that uses the consistency between radar reflectivity, differential reflectivity, and the path integral of specific differential phase to calibrate reflectivity. Complete instructions are provided such that this study can serve as a guide for agencies that are upgrading their radars with polarimetric capabilities and require accurate calibration. The method is demonstrated using data from Météo-France's operational C-band polarimetric radar. Daily averages of the calibration of radar reflectivity are shown to vary by less than 0.2 dB. In addition to achieving successful calibration, a sensitivity test is also conducted to examine the impacts of using different models relating raindrop oblateness to diameter. It turns out that this study highlights the suitability of the raindrop shape models themselves. Evidence is shown supporting the notion that there is a unique model that relates drop oblateness to diameter in midlatitudes. [ABSTRACT FROM AUTHOR]

Radar reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) measured from the operational, polarimetric weather radar located in Trappes, France, were used to examine the effects of radar beam shielding on rainfall estimation. The objective of this study is to investigate the degree of immunity of Kdp-based rainfall estimates to beam shielding for C-band radar data during four typical rain events encountered in Europe. The rain events include two cold frontal rainbands with average rainfall rates of 7 and 17 mm h-1, respectively, and two summertime convective rain events with average rainfall rates of 11 and 22 mm h-1. The large effects of beam shielding on rainfall accumulation were observed for algorithms using Zh and Zdr with differences of up to ∼2 dB (40%) compared to a Kdp-based algorithm over a power loss range of 0–8 dB. This analysis reveals that Zdr and Kdp are not affected by partial beam shielding. Standard reflectivity corrections based on the degree of beam shielding would have overestimated rainfall rates by up to 1.5 dB for less than 40% beam shielding and up to 3 dB for beam shielding less than 75%. The investigation also examined the sensitivity of beam shielding effects on rainfall rate estimation to (i) axis–ratio parameterization and drop size distribution, (ii) methods used to smooth profiles of differential propagation phase (φdp) and estimate Kdp, and (iii) event-to-event variability. Although rainfall estimates were sensitive to drop size distribution and axis–ratio parameterization, differences between Zh- and Kdp-based rainfall rates increased independently from those parameters with amount of shielding. Different approaches to smoothing φdp profiles and estimating Kdp were examined and showed little impact on results. [ABSTRACT FROM AUTHOR]

A fuzzy logic algorithm has been developed for the purpose of segregating precipitating from nonprecipitating echoes using polarimetric radar observations at C band. Adequate polarimetric descriptions for each type of scatterer are required for the algorithm to be effective. An observations-based approach is presented in this study to derive membership functions and objectively weight them so that they apply directly to conditions experienced at the radar site and to the radar wavelength. Three case studies are examined and show that the algorithm successfully removes nonprecipitating echoes from rainfall accumulation maps. [ABSTRACT FROM AUTHOR]

Summary: A major goal in environmental modeling is identifying and quantifying sources of uncertainty in the modeling process. A forecast ensemble is developed in this study for a rainfall-runoff simulation system. This ensemble includes several quantitative precipitation estimates that serve as inputs to the Vflo™ hydrologic model. The rainfall estimates are derived from rain gauges, radar, satellite, and combinations, and their probability distribution is assumed to encompass the true, but unknown, rainfall. Sensitive model parameters in the model are also perturbed within their physical bounds to create a combined input-parameter ensemble. If all major sources of uncertainty are accounted for, then observations of river discharge should fall within simulation bounds. Otherwise, there may be additional errors that lie within the model structure. Probability distributions derived from the forecast ensemble encompass streamflow observations for three hydrologic events examined during October and December on the Blue River Basin in Oklahoma. It is discovered, however, that all simulations from an ensemble created for a warm season case overforecast discharge peaks and volumes. Climatological rain gauge, discharge, and soil moisture observations are introduced to illuminate the source of uncertainty that was not accounted for in the combined input-parameter ensemble. Observations show a strong correlation between dry, deep-layer soils and significantly reduced runoff production (provided the same rainfall inputs) during the summer months. The Green and Ampt methodology is used in the model to compute soil infiltration rates. Evidence suggests additional abstractions such as interception and evapotranspiration by vegetation and deep cracks in the soil structure contribute to enhanced infiltration rates during the warm season. These effects need to be considered for future infiltration models. [Copyright &y& Elsevier]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge. [ABSTRACT FROM AUTHOR]

Summary: In recent years, there has been an increase in flash flood impacts, even as our ability to forecast events and warn areas at risk increases. This increase results from a combination of extreme events and the exposure of vulnerable populations. The issues of exposure and vulnerability to flash floods are not trivial because environmental circumstances in such events are specific and complex enough to challenge the general understanding of natural risks. Therefore, it seems essential to consider physical processes of flash floods concurrently with the impacts they trigger. This paper takes a first step in addressing this need by creating and testing the coherence of an impact-focused database based on two pre-existing public and expert-based survey datasets: the Severe Hazards Analysis and Verification Experiment (SHAVE) and the US National Weather Service (NWS) Storm Data. The SHAVE initiative proposes a new method for collecting near-real-time high-resolution observations on both environmental circumstances and their disastrous consequences (material and human losses) to evaluate radar-based forecasting tools. Forecast verification tools and methods are needed to pursue improving the spatial and temporal accuracy of forecasts. Nevertheless by enhancing SHAVE and NWS datasets with socially and spatially relevant information, we aim at improving future forecast ability to predict the amount and types of impacts. This paper describes the procedures developed to classify and rank the impacts from the least to the most severe, then to verify the coherence and relevance of the impact-focused SHAVE dataset via cross-tabulation analysis of reported variables and GIS-sampled spatial characteristics. By crossing impact categories with socio-spatial characteristics, this analysis showed first benchmarks for the use of exposure layers in future flash flood impact forecasting models. The enhanced impact-focused datasets were used to test the capabilities of flash flood forecasting tools in predicting different categories of impacts for two extreme cases of flash flooding in Oklahoma, USA. Results showed a general tendency for the more severe impacts to be associated to higher mean exceedances over tool values. This means that, at least for these particular case studies, the tools were able to make a distinction between less severe and more severe impacts. Finally, a critical analysis of the NWS and SHAVE data collection methodologies was completed and challenges for future work were identified. [Copyright &y& Elsevier]

Summary: This study evaluated rainfall estimates from ground radar network and four satellite algorithms with a relatively dense rain gauge network over Taiwan Island for the 2009 extreme Typhoon Morakot at various spatiotemporal scales (from 0.04° to 0.25° and hourly to event total accumulation). The results show that all the remote-sensing products underestimate the rainfall as compared to the rain gauge measurements, in an order of radar (−18%), 3B42RT (−19%), PERSIANN-CCS (28%), 3B42V6 (−36%), and CMORPH (−61%). The ground radar estimates are also most correlated with gauge measurements, having a correlation coefficient (CC) of 0.81 (0.82) at 0.04° (0.25°) spatial resolution. For satellite products, CMORPH has the best spatial correlation (0.70) but largely underestimates the total rainfall accumulation. Compared to microwave ingested algorithms, the IR-dominant algorithms provide a better estimation of the total rainfall accumulation but poorly resolve the temporal evolution of the warm cloud typhoon, especially for a large overestimation at the early storm stage. This study suggests that the best performance comes from the ground radar estimates that could be used as an alternative in case of the gauge denial. However, the current satellite rainfall products still have limitations in terms of resolution and accuracy, especially for this type of extreme typhoon. [ABSTRACT FROM AUTHOR]