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This paper investigates the effect of adjusting the mean field bias (MFB) in radar-based precipitation data on analysis and prediction of streamflow and soil moisture in assimilating streamflow or streamflow and in situ soil moisture data into distributed hydrologic models. To evaluate the effect of adjusting the MFB under realistic as well as idealized conditions, both real-world and synthetic experiments are carried out for the Eldon Catchment on the border of Oklahoma and Arkansas in the US. In the synthetic experiment, the MFB is modeled as a stationary Markov chain process. The synthetic experiment showed that adjusting the MFB in the assimilation process significantly improves streamflow analysis when the initial conditions are known with reasonable certainty, and that assimilating soil moisture in addition to streamflow improves analysis of streamflow as well as soil moisture if the initial conditions are largely uncertain. Adjusting the MFB during the assimilation process noticeably improved streamflow analysis over ranges of the MFB and random noise in the precipitation data. On the other hand, increasing the MFB and random noise in the precipitation data tended to degrade soil moisture analysis due possibly to over-adjusting soil moisture to mitigate the precipitation error. The real-world experiment with one-year dataset showed that adjusting the MFB during the assimilation process helped capture the peak as well as volume of outlet flow analysis as well as prediction, and that additionally assimilating interior flow observations was necessary to improve analysis and prediction of peak flows at interior locations. [ABSTRACT FROM AUTHOR]

The Hydrometeorological Automated Data System (HADS) is a real-time data acquisition, processing, and distribution system operated by the Office of Hydrologic Development (OHD) of NOAA’s National Weather Service (NWS). The initial reprocessing of HADS data from its original format since its inception in July 1996 has been completed at NOAA’s National Climatic Data Center (NCDC). The quality of the reprocessed HADS hourly precipitation data from rain gauges is assessed by two objective metrics: the average fraction of missing values and the percentage of top-of-the-hour observations for a 3-yr period (2003–05). Pairwise comparisons between the reprocessed product and the real-time product are made using representative samples (about 13%) from the 48 contiguous United States. The monthly average of missing values varies from 0.5% to 2% in the reprocessed product and from 1.7% to 10.1% in the real-time product. Except for January 2003, the reprocessed product consistently reduced missing values, by as much as 9.4% in October 2004. The availability of top-of-the-hour observations is about 85% in the reprocessed product, while the real-time product has top-of-the-hour observations only about 50% of the time. This paper discusses real-time product quality issues, additional quality assurance algorithms used in the reprocessing environment, and the design of system-wide performance comparisons. Thus, the benefits to users of reprocessing the HADS data are the correction of 4-h observation time errors during 1 July–11 August 2005 and the demonstration of diurnals pattern of precipitation frequencies in regional domains. A Web-based interactive quality assessment tool for reprocessed HADS hourly precipitation data and access to the data are also presented. [ABSTRACT FROM AUTHOR]

Summary: In operational hydrologic forecasting, to account for errors in the initial and boundary conditions, and in parameters and structures of the hydrologic models, the forecasters routinely make adjustments in real-time to the hydrometeorological input, hydrologic model states and, in certain cases, model parameters based on streamflow observations. Though a great deal of effort has been made in recent years to automate such “run-time modifications” (MOD) by human forecasters to a possible extent, automatic state updating of hydrologic models is yet to be widely accepted or routinely practiced in operational hydrology for a range of reasons. In this paper, we describe a state updating procedure intended specifically for operational streamflow forecasting for gauged headwater basins, and compare its performance with human forecaster MOD through a real-time forecasting experiment. The procedure is based on variational assimilation (VAR) of streamflow, precipitation and potential evaporation (PE) data into lumped soil moisture accounting and routing models operating at a 1-h timestep. The procedure has been in experimental operation since 2003 at the National Weather Service’s (NWS) West Gulf River Forecast Center (WGRFC) in Fort Worth, TX. Also described is a novel parameter estimation and optimization tool, the Adjoint-Based OPTimizer (AB_OPT), used for lumped hydrologic modeling at a 1-h timestep necessary for VAR. [Copyright &y& Elsevier]

Abstract: Ensemble forecasting is widely used in meteorology and, increasingly, in hydrology to quantify and propagate uncertainty. In practice, ensemble forecasts cannot account for every source of uncertainty, and many uncertainties are difficult to quantify accurately. Thus, ensemble forecasts are subject to errors, which may be correlated in space and time and may be systematic. Ensemble verification is necessary to quantify these errors, and to better understand the sources of predictive error and skill in particular modeling situations. The Ensemble Verification System (EVS) is a flexible, user-friendly, software tool that is designed to verify ensemble forecasts of numeric variables, such as temperature, precipitation and streamflow. It can be applied to forecasts from any number of discrete locations, which may be issued with any frequency and lead time. The EVS can also produce and verify forecasts that are aggregated in time, such as daily precipitation totals based on hourly forecasts, and can aggregate verification statistics across several discrete locations. This paper is separated into four parts. It begins with an overview of the EVS and the structure of the Graphical User Interface. The verification metrics available in the EVS are then described. These include metrics that verify the forecast probabilities and metrics that verify the ensemble mean forecast. Several new verification metrics are also presented. Following a description of the Application Programming Interface, the procedure for adding a new metric to the EVS is briefly outlined. Finally, the EVS is illustrated with two examples from the National Weather Service (NWS), one focusing on ensemble forecasts of precipitation from the NWS Ensemble Pre-Processor and one focusing on ensemble forecasts of streamflow from the NWS Ensemble Streamflow Prediction system. The conclusions address future enhancements to, and applications of, the EVS. [Copyright &y& Elsevier]