Radar accuracy in estimating qualitative precipitation estimation at distances larger than 120 km degrades rapidly because of increased volume coverage and beam height. The performance of the recently upgraded dual-polarized technology to the NEXRAD network and its capabilities are in need of further examination, as improved rainfall estimates at large distances would allow for significant hydrological modelling improvements. Parameter based methods were applied to radars from St. Louis (KLSX) and Kansas City (KEAX), Missouri, USA, to test the precision and accuracy of both dual-and single-polarized parameter estimations of precipitation at large distances. Hourly aggregated precipitation data from terrestrial-based tipping buckets provided ground-truthed reference data. For all KLSX data tested, an R(Z,ZDR) algorithm provided the smallest absolute error (3.7 mm h À1 ) and root-mean-square-error (45%) values. For most KEAX data, R(ZDR,KDP) and R(KDP) algorithms performed best, with RMSE values of 37%. With approximately 100 h of precipitation data between April and October of 2014, nearly 800 and 400 mm of precipitation were estimated by radar precipitation algorithms but was not observed by terrestrialbased precipitation gauges for KLSX and KEAX, respectively. Additionally, nearly 30 and 190 mm of measured precipitation observed by gauges were not detected by the radar rainfall estimates from KLSX and KEAX, respectively. Results improve understanding of radar based precipitation estimates from long ranges thereby advancing applications for hydrometeorological modelling and flood forecasting.

Abstract:In this work, we will identify the existence of "rough dependence on initial conditions" in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time scale. With rough dependence, two initial states diverge very quickly, implying forecasting may be impossible. Using previous research in atmospheric science, rough dependence is characterized by using quantities that can be calculated using atmospheric data and quantities. Rough dependence will be tested for and identified in atmospheric phenomena at different time scales using case studies. Data were provided for this project by archives outside the University of Missouri (MU) and by using the MU RADAR at the South Farm experiment station.

Abstract. Since the advent of dual-polarization radar technology, many studies have been conducted to determine the extent to which the differential reflectivity (ZDR) and specific differential phase shift (KDP) add benefits to estimating rain rates (R) compared to reflectivity (Z) alone. It has been previously noted that this new technology provides significant improvement to rain-rate estimation, primarily for ranges within 125 km of the radar. Beyond this range, it is unclear as to whether the National Weather Service (NWS) conventional R(Z)-convective algorithm is superior, as little research has investigated radar precipitation estimate performance at larger ranges. The current study investigates the performance of three radars -St. Louis (KLSX), Kansas City (KEAX), and Springfield (KSGF), MO -with 15 tipping bucket gauges serving as ground truth to the radars. With over 300 h of precipitation data being analyzed for the current study, it was found that, in general, performance degraded with range beyond, approximately, 150 km from each of the radars. Probability of detection (PoD) in addition to bias values decreased, while the false alarm rates increased as range increased. Bright-band contamination was observed to play a potential role as large increases in the absolute bias and overall error values near 120 km for the cool season and 150 km in the warm season. Furthermore, upwards of 60 % of the total error was due to precipitation being falsely estimated, while 20 % of the total error was due to missed precipitation. Correlation coefficient values increased by as much as 0.4 when these instances were removed from the analyses (i.e., hits only). Overall, due to the lowest normalized standard error (NSE) of less than 1.0, a National Severe Storms Laboratory (NSSL) R(Z,ZDR) equation was determined to be the most robust, while a R(ZDR,KDP) algorithm recorded NSE values as high as 5. The addition of dual-polarized technology was shown to estimate quantitative precipitation estimates (QPEs) better than the conventional equation. The analyses further our understanding of the strengths and limitations of the Next Generation Radar (NEXRAD) system overall and from a seasonal perspective.