Showing papers by "Dong Jun Seo published in 2007"

Improving QPE and Very Short Term QPF: An Initiative for a Community-Wide Integrated Approach

Abstract: Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide int...

Precipitation and temperature ensemble forecasts from single-value forecasts

Abstract: . A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).

Experimental hydrometeorological and hydrological ensemble forecasts and their verification in the US National Weather Service

Abstract: An ensemble preprocessor is being developed by the Office of Hydrologic Development, NOAA/National Weather Service (NWS), USA, to produce reliable short-term hydrometeorological ensemble forecasts from single-value forecasts of precipitation and temperature. These hydro-meteorological ensemble forecasts are then ingested into the NWS Ensemble Streamflow Prediction system to produce probabilistic hydrological forecasts that reflect the hydrometeorological uncertainty. The preprocessor methodology attempts to remove biases in single-value forecasts, and capture the skill and uncertainty therein, while preserving the space-time statistical properties of the hydrometeorological variables. The ensemble preprocessor currently operates experimentally at four NOAA/NWS River Forecast Centers in the USA. The verification results presented in this paper show that the precipitation ensembles generated from the ensemble preprocessor produce highly reliable probability estimates and improve the streamflow ensemble forecast performance. Further work is needed to reduce and fully account for hydrological uncertainties in order to improve the quality of streamflow ensemble forecasts.