Showing papers by "Dong Jun Seo published in 2010"

The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations

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.

A Nonparametric Postprocessor for Bias Correction of Hydrometeorological and Hydrologic Ensemble Forecasts

Abstract: This paper describes a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast. This ccdf represents the “true” probability distribution of the forecast variable, subject to sampling uncertainties. In the absence of a known distributional form, the ccdf should be estimated nonparametrically. It is noted that the probability of exceeding a threshold of the observed variable, such as flood stage, is equivalent to the expectation of an indicator variable defined for that threshold. The ccdf is then modeled through a linear combination of the indicator variables of the forecast ensemble members. The technique is based on Bayesian opt...

Multisensor Precipitation Reanalysis

Abstract: Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather S...

Hydrologic ensemble prediction for risk-based water resources mangement and hazard mitigation

Abstract: Hydrologic predictions are subject to various sources of error due to uncertainties in the atmospheric forcing observations and predictions, hydrologic model initial conditions, parameters and structures, and streamflow regulations. To allow risk-based decision making in water resources and emergency management, quantification of predictive uncertainty in streamflow forecasts across short, medium and long ranges is necessary. To obtain reliable predictive uncertainty, it is necessary to account for both input (i.e. atmospheric) and hydrologic uncertainties accurately. To provide uncertainty-quantified streamflow forecast products operationally, the National Weather Service (NWS) Office of Hydrologic Development (OHD) and its partners have been developing a prototype hydrologic ensemble forecast system, the EXperimental Ensemble Forecast System (XEFS). The principal components of the prototype system are currently implemented in the Community Hydrologic Prediction System (CHPS). Testing and experimental operation of the XEFS components have begun in late 2009 at selected NWS River Forecast Centers (RFC). In this paper, we describe the progress and plans, evaluation results from hindcasting experiments, and challenges ahead.

Multi-sensor Precipitation Reanalysis

Abstract: Accurate historical precipitation analysis is needed for various hydrologic, hydrometeorological, hydroclimatological applications. Increasingly, many of these applications require analysis at high spatiotemporal resolutions. Since the implementation in the early to mid-1990’s the network of Weather Surveillance Radar – 1988 Doppler (WSR-88D), commonly known as the Next Generation Weather Radar (NEXRAD), realtime precipitation analysis in the U.S. has been heavily relying on radar data for high-resolution precipitation information. In the continental U.S., the WSR-88D network consists of approximately 140 sites, most of which have been operational for well over a decade now. The WSR-88Ds provide radar reflectivity estimates for the NEXRAD Precipitation Processing Subsystem (PPS, Fulton et al. 1998) which produces radar-derived precipitation products in real time in support of the National Weather Service’s mission and external users. Quantitative precipitation estimates (QPE) from radar, however, are subject to various sources of error (Wilson and Brandes 1979, Vasiloff et al. 2007) and, by themselves, are generally not suitable for quantitative hydroclimatological applications. To produce precipitation estimates that are more accuate than those obtainable from radar or rain gauges alone, multisensor estimation is necessary, consisting usually of bias correction of radar QPE and multivariate analysis, or merging, of bias-corrected radar QPE and rain gauge data. In NWS, such multisensor precipitation estimation applications produce precipitation estimates at different spatial scales and in stages (Hudlow 1988, Vasiloff et al. 2007). For example, the so-called Stage III products are generated at the River Forecast Centers (RFC), which are nationally mosaicked at the National Centers for Environmental Prediction (NCEP) to produce the Stage IV products (Fulton et al, 1998, Vasiloff et al. 2007). In the early 2000’s, the Multisensor