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 pe- 5 riod 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 distri-bution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distri-bution of events that may occur for that forecast. This conditional forecast distribution 10 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 relation-ships between events that have a significant effect on hydrological response tend to be preserved. 15 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 ap-plies. Although the “Schaake Shuffle” procedure, when applied to construct ensem-ble members from a time-series of single value forecasts, may preserve some of this 20 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 25 Prediction system to produce ensemble forecasts of streamflow and other hydrologi-cal 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 re-sults 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)