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This study investigates an approach that combines physically-based and conceptual model features in two stages of distributed modeling: model structure development and estimation of spatially variable parameters. The approach adds more practicality to the process of model parameterization, and facilitates an easier transition from current lumped model-based operational systems to more powerful distributed systems. This combination of physically-based and conceptual model features is implemented within the Hydrology Laboratory Research Modeling System (HL-RMS). HL-RMS consists of a well-tested conceptual water balance model applied on a regular spatial grid linked to physically-based kinematic hillslope and channel routing models. Parameter estimation procedures that combine spatially distributed and 'integrated' basin-outlet properties have been developed for the water balance and routing components. High-resolution radar-based precipitation data over a large region are used in testing HL-RMS. Initial tests show that HL-RMS yields results comparable to well-calibrated lumped model simulations in several headwater basins, and it outperforms a lumped model in basins where spatial rainfall variability effects are significant. It is important to note that simulations for two nested basins (not calibrated directly, but parameters from the calibration of the parent basin were applied instead) outperformed lumped simulations even more consistently, which means that HL-RMS has the potential to improve the accuracy and resolution of river runoff forecasts. Published by Elsevier B.V.

Systematic biases in WSR‐88D (Weather Surveillance Radar–1988 Doppler) hourly precipitation accumulation estimates are characterized from analyses of more than 1 year of WSR‐88D data and rain gage data from the southern plains. Biases are examined in three contexts: (1) biases that arise from the range‐dependent sampling of the WSR‐88D, (2) systematic differences in radar rainfall estimates from two radars observing the same area, and (3) systematic differences between radar and rain gage estimates of rainfall. Range‐dependent biases affect hourly rainfall accumulations products over much of the area covered by the WSR‐88D. Significant underestimation of rainfall occurs within 40 km range of the radar due to bias in reflectivity observations at the higher elevation angles used for rainfall estimation close to the radar. Bright band and anomalous propagation (AP) lead to systematic overestimation of rainfall at intermediate range. Beyond 150 km in spring‐summer and beyond 100 km in winter‐fall, underestimation of precipitation is pronounced due to incomplete beam filling and overshooting of precipitation. Radar‐radar intercomparison studies suggest that radar calibration is a significant problem at some sites. Anomalous propagation during clear‐air conditions, a major problem with previous National Weather Service network radars, has been largely eliminated by the WSR‐88D processing. AP remains a problem for cases in which AP returns are embedded in rain. Radar–rain gage intercomparison analyses indicate systematic underestimation by the WSR‐88D relative to rain gages for paired gage‐radar rainfall estimates. Analyses of spatial coverage of heavy rainfall, however, illustrate fundamental advantages of radar over rain gage networks for rainfall estimation.

extends hydrologie ensemble services from 6-hour to year-ahead forecasts and includes additional weather and climate infoi'mation as well as improved quantification of major uncertainties.

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development. Through about ten years of R&D, a real-time NMQ system has been implemented (http://nmq.ou.edu). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time. The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.

The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002–12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used.