580 WEATHER AND FORECASTING VOLUME 22 Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles CHUNGU LU,* HUILING YUAN,BARRY E. SCHWARTZ, AND STANLEY G. BENJAMIN NOAA/Earth System Research Laboratory, Boulder, Colorado (Manuscript received 3 February 2006, in final form 20 September 2006) ABSTRACT A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble mem- bers. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time- lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts. 1. Introduction Brooks et al. (1995) examined the feasibility of SREF applications in NWP. Du et al. (1997) examined the Because of the uncertainties in the numerical impact of initial condition uncertainty on quantitative weather prediction (NWP) models, analyses, and fore- precipitation forecasts from SREFs based on a meso- casts, ensemble methods have been studied in the re- scale model. Hamill and Colucci (1997, 1998) evaluated search community quite extensively in recent years. the performance of National Oceanic and Atmospheric Many forecast centers around the world, such as the Administration (NOAA)/NCEP’s Eta Model– European Centre for Medium-Range Weather Fore- Regional Spectral Model (RSM) SREFs. Following casts (ECMWF) and the National Centers for Environ- these studies, an experimental SREF was developed at mental Prediction (NCEP), have also developed and NOAA/NCEP, and a series of studies were conducted implemented various ensemble forecast systems to gen- for evaluating this system (e.g., Stensrud et al. 1999; erate forecast references and guidelines in addition to Wandishin et al. 2001). The further development of this the deterministic ones. The short-range ensemble fore- system into operations has been described in Du and casts (SREFs) concern forecast lead times between 0 Tracton (2001), and the possibility of including mem- and 48 h, and typically use limited-area models with bers from the NOAA Rapid Update Cycle (RUC) fore- relatively fine spatial resolutions and frequent forecast cast system has been reported upon in Lu et al. (2004). outputs. With the advance of computational ability and A comprehensive verification of SREF was also con- the success of the medium-range ensemble forecasting, ducted in Hou et al. (2001) for the Storm and Mesoscale Ensemble Experiment (SAMEX). The NOAA RUC forecast system (Benjamin et al. 2004a,b; Bleck and Benjamin 1993) has been consis- * Additional affiliation: Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colo- tently putting out regional weather forecasts in the rado. short range over the years. Because the RUC system assimilates high-frequency observational data in an hourly cycle, its 1–12-h forecasts have provided a valu- Corresponding author address: Chungu Lu, NOAA/Earth Sys- tem Research Laboratory, 325 South Broadway, Boulder, CO able reference and complement to less frequently up- 80305. dated forecasts from other operational NOAA models. E-mail: [email protected] Also, because of the same reason, one may wonder how DOI: 10.1175/WAF999.1 © 2007 American Meteorological Society Unauthenticated | Downloaded 09/27/21 10:52 AM UTC WAF999 JUNE 2007 L U E T A L . 581 forecasts from these hourly initializations vary, and, if examine the forecast variability among forecasts from there is some variability, how feasible and sensible an different initialization cycles. Next, we will describe ensemble forecast is by using these forecasts as a set of how to construct time-lagged ensembles using the eight ensemble members. available deterministic forecasts within a 12-h initializa- Time-lagged ensemble forecasting has been studied tion cycle, and briefly introduce the verification– and proposed for the medium range of 6–10 days (Hoff- observation data and method. In the next section, we man and Kalnay 1983; Dalcher et al. 1988; van den obtain time-lagged ensemble forecasts using a simple Dool and Rukhovets 1994). The concept has also been ensemble mean approach (equally weighted). These applied to the short-range forecast recently in Hou et time-lagged ensembles along with various deterministic al. (2001) and in Walser et al. (2004). We have experi- forecasts are verified against upper-air (rawinsonde) mented with the time-lagged ensemble approach using observations at station locations (section 3). To further the NOAA RUC for a number of years. The current reduce the ensemble forecast error, we construct time- study is a report on this effort. We believe that the lagged ensembles with unequal-weighted forecasts us- method we present in this study can be applied to any ing a multilinear regression method (section 4). In sec- rapid update data assimilation and forecast system. tion 5, we will conduct verifications of the probabilistic One reason to adopt the time-lagged ensemble tech- forecasts, constructed by time-lagged ensembles. Con- nique to the short-range forecasting is because a short- clusions will be provided in section 6. range forecast generally possesses a relatively strong dependency on the initial conditions. The forecast er- rors in the very short range may be strongly correlated 2. Time-lagged ensemble method to the uncertainties in the initial analysis. The time- a. Description of the forecast model lagged ensembles can be interpreted as forecasts ob- The NOAA RUC forecast/data assimilation system tained from a set of perturbed initial conditions (van was developed at the NOAA/Forecast Systems Labo- den Dool and Rukhovets 1994). Because these initial ratory (currently, the NOAA/Earth System Research perturbations are generated from different forecast ini- Laboratory/Global System Division), and has been tialization cycles, conceptually, time-lagged ensembles used as the operational forecast model for the Federal reflect the forecast error covariance with time-evolving Aviation Administration (FAA) and also been used as (flow dependent) information. In the very short range, the rapid update forecast and data assimilation system this flow-dependent forecast error can be, for example, at NOAA/NCEP. The model’s dynamical core is com- a result of an initial imbalance or a shock of model posed of a hybrid terrain-following sigma (at lower lev- fields due to various data being ingested at initialization els) and isentropic (at upper levels) vertical coordi- time and the ensuing adjustment of the model fields. nates, and a set of hydrostatic primitive equations. In In this study, we will report on how a time-lagged addition, there is a complete set of physical parameter- ensemble forecast system can be constructed using ization schemes, including those for the planetary forecasts from the RUC, and evaluate how much skill boundary layer, radiation, land surface physics, cumu- this type of ensemble forecast can provide over each lus convection, and explicit mixed-phase cloud physics, individual deterministic forecast for the very short to represent various physical processes in the model range weather prediction of 1–3 h. This type of short- and to close the model dynamic equations. The data range ensemble forecast system could be used in short- assimilation system includes a series of implementa- range decision support systems, such as those for avia- tions of optimal interpolation (OI), three-dimensional tion weather forecast applications including air traffic variational data assimilation (3DVAR) analysis, and management, in which case frequent updates to a fore- the nudging technique. The nudging technique is cast are needed. Analyses and verification of these mostly applied to the surface fields, while the main at- time-lagged ensemble systems, for both the ensemble mospheric fields are assimilated with the 3DVAR al- mean and probabilistic forecasts, will be conducted. We gorithm. The lateral-boundary conditions are given by will introduce two different approaches for the weight- NCEP Eta Model (for detail, see the aforementioned ing of RUC hourly forecasts, and compare the improve- references). ment of short-range forecasts by these two ensemble The RUC horizontal domain covers the contiguous systems. United States and adjacent areas of Canada, Mexico, The paper is organized as follows. In section 2, we and the Pacific and Atlantic Oceans. The operational will first give a brief description of the NOAA RUC RUC has been run at a series of horizontal grid spac- data assimilation and modeling system. We will then ings: 60, 40, 20, and 13 km. In this study, we use the Unauthenticated | Downloaded 09/27/21 10:52 AM UTC 582 WEATHER AND FORECASTING VOLUME 22 40-km resolution analyses and forecasts during Novem- ered for the ensemble member pool. Figure 3 shows ber and December of 2003. There are 24 runs per day, schematically a time-lagged ensemble forecast system. but we only use the forecasts that are initialized within Because the forecast data from the hourly initialization 12 h previously. The hourly runs of the operational for operational 40-km RUC runs were archived up to 6 RUC 40-km data are archived for only the first 6 h, with h, after that only the forecast data with 3-hourly initial- the longer archives limited to the 3-hourly runs (e.g., izations were archived, the maximum size of the time- 0000, 0300, 0600 UTC, etc.).