Precipitation and temperature ensemble forecasts from single-value forecasts

Abstract

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).

Comparison of pre‐ and post‐processors for ensemble streamflow prediction

“…The conditional distribution of the forecasted variables is then fitted with the selected subset of the historical data of the forecasted variables. Schaake et al (2007) proposed a method to produce a probabilistic forecast from a deterministic and singlevalue forecast. We call this method the 'Deterministic to Probabilistic forecasts Conversion (DPC)' method.…”

Hydrological monitoring and seasonal forecasting: Progress and perspectives

“…Further the uncertainties in the forecasts are generally greater than that associated with the observations. Statistical post-processing methods have been widely used to deal with the errors in QPFs and QTFs (Glahn and Lowry, 1972;Krzysztofowicz and Sigrest, 1999;Schaake et al, 2007). Data assimilation methods have been widely used to reduce the uncertainty associated with the initial conditions (ICs) used in the hydrological models.…”

A methodology for probabilistic real-time forecasting – an urban case study

“…The approach presented here has some similarities to the method described first by Schaake et al (2007), which was then further improved by Wu et al (2011), in the sense that it portions the historical observed and corresponding forecasted data into four sub-regions to estimate the uncertainty of the rainfall forecast and provide a probabilistic hydrological forecast.…”