A. Allen Bradley

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

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 f...

River gauging using PIV techniques: a proof of concept experiment on the Iowa River

Abstract An image-based approach for discharge measurements is evaluated for river gauging in an experiment on the Iowa River at Iowa City, Iowa. Over a twenty-day period, ten discharge measurements were made using the image-based approach. A ten-minute video recording was made of the river flow for each measurement. Particle image velocimetry (PIV) was used to estimate surface velocities for the imaged area using naturally occurring foam as a flow tracer. The surface velocities were then estimated along a surveyed river section, and river discharge was computed using standard velocity–area methods over a selected cross-section. Several unique aspects of this experiment were the use of PIV for unseeded flow conditions, and the evaluation of discharge estimates over a range of flow conditions. A comparison of the PIV discharge measurements with traditional current meter measurements, which have been made at the site since 1984, showed that the PIV measurements were consistent with the observed stage–discharge relationship. Discharge for the experiment ranged from 50 to 300 m 3 s −1 , which covers a large portion of the existing rating curve. The experimental results suggest that image-based approach may be a reliable way of establishing a stage–discharge relationship at a site, perhaps even remotely, by making repeated measurement with a camera mounted at the site. Still, there are inherent limitations with the approach. These limitations include the need for recognizable tracer particles or flow patterns to detect motion, problems associated with shadows and reflections on the water surface, as well as the common the problem for all discharge measurement of the need for survey information on the channel cross-section.

An evaluation of NEXRAD precipitation estimates in complex terrain

Next Generation Weather Radar (NEXRAD) precipitation estimates are used for hydrological, meteorological, and climatological studies at a wide range of spatial and temporal scales. The utility of radar-based precipitation estimates in such applications hinges on an understanding of the sources and magnitude of estimation error. This study examines precipitation estimation in the complex mountainous terrain of the northern Appalachian Mountains. Hourly digital precipitation (HDP) products for two WSR-88D radars in New York state are evaluated for a 2-year period. This analysis includes evaluation of range dependence and spatial distribution of estimates, radar intercomparisons for the overlap region, and radar-gage comparisons. The results indicate that there are unique challenges for radar-rainfall estimation in mountainous terrain. Beam blockage is a serious problem that is not corrected by existing NEXRAD algorithms. Underestimation and nondetection of precipitation are also significant concerns. Improved algorithms are needed for merging estimates from multiple radars with spatially variable biases.

An Experimental Study of Small-Scale Variability of Radar Reflectivity Using Disdrometer Observations

Abstract Analysis of data collected by four disdrometers deployed in a 1-km2 area is presented with the intent of quantifying the spatial variability of radar reflectivity at small spatial scales. Spatial variability of radar reflectivity within the radar beam is a key source of error in radar-rainfall estimation because of the assumption that drops are uniformly distributed within the radar-sensing volume. Common experience tells one that, in fact, drops are not uniformly distributed, and, although some work has been done to examine the small-scale spatial variability of rain rates, little experimental work has been done to explore the variability of radar reflectivity. The four disdrometers used for this study include a two-dimensional video disdrometer, an X-band radar-based disdrometer, an impact-type disdrometer, and an optical spectropluviometer. Although instrumental differences were expected, the magnitude of these differences clouds the natural variability of interest. An algorithm is applied to ...

The hydrometeorological environment of extreme rainstorms in the southern plains of the United States

Abstract Convective storms are commonplace in the southern plains of the United States. Occasionally, convective storms produce extreme rainfall accumulations, causing streams and rivers to flood. In this paper, we examine the hydrometeorological environment associated with these extreme rainstorms. Datasets used include hourly precipitation data from more than 200 stations, upper-air data, and daily weather maps. The seasonal distribution of extreme rainstorms in the southern plains has pronounced peaks in late spring and early fall. Moisture availability and convective instability are higher than climatological averages during spring and fall extreme rainstorms, but nearer their averages during summer extreme rainstorms. Although high levels of moisture and convective instability are most common in the summer, the dynamic forcings that can initiate and focus convection are weak. It appears that late spring and early fall are the most likely times for extreme rainstorms because anomalously high levels of...

Distributions-Oriented Verification of Ensemble Streamflow Predictions

Abstract Ensemble streamflow prediction systems produce forecasts in the form of a conditional probability distribution for a continuous forecast variable. A distributions-oriented approach is presented for verification of these probability distribution forecasts. First, a flow threshold is used to transform the ensemble forecast into a probability forecast for a dichotomous event. The event is said to occur if the observed flow is less than or equal to the threshold; the probability forecast is the probability that the event occurs. The distributions-oriented approach, which has been developed for meteorological forecast verification, is then applied to estimate forecast quality measures for a verification dataset. The results are summarized for thresholds chosen to cover the range of possible flow outcomes. To aid in the comparison for different thresholds, relative measures are used to assess forecast quality. An application with experimental forecasts for the Des Moines River basin illustrates the app...

Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score

Abstract For probability forecasts, the Brier score and Brier skill score are commonly used verification measures of forecast accuracy and skill. Using sampling theory, analytical expressions are derived to estimate their sampling uncertainties. The Brier score is an unbiased estimator of the accuracy, and an exact expression defines its sampling variance. The Brier skill score (with climatology as a reference forecast) is a biased estimator, and approximations are needed to estimate its bias and sampling variance. The uncertainty estimators depend only on the moments of the forecasts and observations, so it is easy to routinely compute them at the same time as the Brier score and skill score. The resulting uncertainty estimates can be used to construct error bars or confidence intervals for the verification measures, or perform hypothesis testing. Monte Carlo experiments using synthetic forecasting examples illustrate the performance of the expressions. In general, the estimates provide very reliable inf...

Distributions-oriented verification of probability forecasts for small data samples

Abstract The distributions-oriented approach to forecast verification uses an estimate of the joint distribution of forecasts and observations to evaluate forecast quality. However, small verification data samples can produce unreliable estimates of forecast quality due to sampling variability and biases. In this paper, new techniques for verification of probability forecasts of dichotomous events are presented. For forecasts of this type, simplified expressions for forecast quality measures can be derived from the joint distribution. Although traditional approaches assume that forecasts are discrete variables, the simplified expressions apply to either discrete or continuous forecasts. With the derived expressions, most of the forecast quality measures can be estimated analytically using sample moments of forecasts and observations from the verification data sample. Other measures require a statistical modeling approach for estimation. Results from Monte Carlo experiments for two forecasting examples sho...

Regional frequency analysis methods for evaluating changes in hydrologic extremes

A common assumption in frequency analysis is that hydrologic extremes (floods or heavy precipitation) are generated by a random process. This implies that natural climatic variability does not change the distribution of extreme events. A regional frequency analysis approach is proposed to test the hypothesis of randomness over secular timescales. Observed regional occurrences of extreme events are compared to those from a random process. Significant departures may indicate nonrandomness due to climatic variability. Application of the approach to a region in the Southern Plains indicates nonrandomness in annual maximum precipitation.

Summary Verification Measures and Their Interpretation for Ensemble Forecasts

AbstractEnsemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for discrete events, where the events are defined by one or more threshold values. Then, skill is evaluated using the mean-square error (MSE; i.e., Brier) skill score for binary events, or the ranked probability skill score (RPSS) for multicategory events. A framework is introduced that generalizes this approach, by describing the forecast quality of ensemble forecasts as a continuous function of the threshold value. Viewing ensemble forecast quality this way leads to the interpretation of the RPSS and the continuous ranked probability skill score (CRPSS) as measures of the weighted-average skill over the threshold values. It also motivates additional measures, derived to summarize other features of a continuous forecast quality function, which can be interpret...